Content Calendar, Content Gap Analysis, and Content Optimization

Getting Started/Option_1_Windows_Quick_Install/install_alwrity_windows.bat

@@ -0,0 +1,122 @@
+@echo off
+setlocal enabledelayedexpansion
+
+s :: Set colors for better visibility
+color 0A
+
+:: Set the Python version requirement
+set MIN_PYTHON_VERSION=3.9
+
+echo ===============================================
+echo           ALwrity Installation Setup
+echo ===============================================
+echo.
+
+echo [1/5] Checking Python installation...
+python --version > nul 2>&1
+if errorlevel 1 (
+    color 0C
+    echo [ERROR] Python is not installed!
+    echo Please install Python %MIN_PYTHON_VERSION% or higher from python.org
+    echo Press any key to exit...
+    pause > nul
+    exit /b 1
+)
+
+:: Get Python version
+for /f "tokens=2" %%V in ('python --version 2^>^&1') do set PYTHON_VERSION=%%V
+for /f "tokens=1,2 delims=." %%a in ("%PYTHON_VERSION%") do (
+    set PYTHON_MAJOR=%%a
+    set PYTHON_MINOR=%%b
+)
+
+:: Check Python version
+set /a PYTHON_VER=%PYTHON_MAJOR%*100 + %PYTHON_MINOR%
+set /a MIN_VER=309
+if %PYTHON_VER% LSS %MIN_VER% (
+    color 0C
+    echo [ERROR] Python version %MIN_PYTHON_VERSION% or higher is required!
+    echo Current version: %PYTHON_VERSION%
+    echo Please upgrade Python from python.org
+    echo Press any key to exit...
+    pause > nul
+    exit /b 1
+)
+
+echo [✓] Python %PYTHON_VERSION% detected
+echo.
+
+echo [2/5] Creating virtual environment...
+python -m venv "%~dp0..\..\venv"
+if errorlevel 1 (
+    color 0C
+    echo [ERROR] Failed to create virtual environment!
+    echo Press any key to exit...
+    pause > nul
+    exit /b 1
+)
+echo [✓] Virtual environment created
+echo.
+
+echo [3/5] Activating virtual environment...
+call "%~dp0..\..\venv\Scripts\activate.bat"
+if errorlevel 1 (
+    color 0C
+    echo [ERROR] Failed to activate virtual environment!
+    echo Press any key to exit...
+    pause > nul
+    exit /b 1
+)
+echo [✓] Virtual environment activated
+echo.
+
+echo [4/5] Upgrading pip...
+python -m pip install --upgrade pip
+if errorlevel 1 (
+    color 0C
+    echo [ERROR] Failed to upgrade pip!
+    echo Press any key to exit...
+    pause > nul
+    exit /b 1
+)
+echo [✓] Pip upgraded
+echo.
+
+echo [5/5] Installing requirements...
+pip install -r "%~dp0..\..\requirements.txt"
+if errorlevel 1 (
+    color 0C
+    echo [ERROR] Failed to install requirements!
+    echo Press any key to exit...
+    pause > nul
+    exit /b 1
+)
+echo [✓] Requirements installed
+echo.
+
+color 0A
+echo ===============================================
+echo       Installation Completed Successfully!
+echo ===============================================
+echo.
+echo Next steps to run ALwrity:
+echo.
+echo 1. Open a new Command Prompt window
+echo 2. Navigate to the ALwrity root directory by copying and pasting this command:
+echo    cd /d "%~dp0..\.."
+echo.
+echo 3. Activate the virtual environment by copying and pasting this command:
+echo    "%~dp0..\..\venv\Scripts\activate.bat"
+echo.
+echo 4. Run ALwrity with Streamlit by copying and pasting this command:
+echo    streamlit run "%~dp0..\..\alwrity.py"
+echo.
+echo Note: You'll need to activate the virtual environment (step 3)
+echo       each time you want to run ALwrity.
+echo.
+echo Troubleshooting:
+echo - If you see any errors, make sure Python is in your PATH
+echo - For help, visit: https://github.com/yourusername/ALwrity
+echo.
+echo Press any key to exit...
+pause > nul

Getting Started/Option_2_Python_Users/README.md

@@ -0,0 +1,27 @@
+# ALwrity Installation Guide
+
+## Quick Start
+
+1. **Install Python**
+   - Download and install Python from [python.org](https://www.python.org/downloads/)
+   - During installation, check "Add Python to PATH"
+
+2. **Install ALwrity**
+   - Download this project
+   - Open the 'Getting Started' folder
+   - Double-click `setup.py`
+   - Follow the on-screen instructions
+
+## Running ALwrity
+
+1. Open Command Prompt/Terminal in the 'Getting Started' folder
+2. Run: `venv\Scripts\activate` (Windows) or `source venv/bin/activate` (Mac/Linux)
+3. Run: `streamlit run alwrity.py`
+
+## Need Help?
+
+- If you see "pip not found": Re-install Python and check "Add Python to PATH"
+- For other issues: [Open a support ticket](https://github.com/AJaySi/AI-Writer/issues)
+- Join our support community
+
+---

Getting Started/Option_4_Mac_Users/Dockerfile

@@ -0,0 +1,26 @@
+# Use Python 3.8 slim image optimized for M1/M2 Macs
+FROM --platform=linux/arm64 python:3.8-slim
+
+# Set working directory
+WORKDIR /app
+
+# Install system dependencies
+RUN apt-get update && apt-get install -y \
+    build-essential \
+    curl \
+    software-properties-common \
+    git \
+    && rm -rf /var/lib/apt/lists/*
+
+# Clone the repository
+RUN git clone https://github.com/AJaySi/AI-Writer.git .
+
+# Install Python dependencies
+RUN python -m pip install --upgrade pip
+RUN pip install -r requirements.txt
+
+# Expose Streamlit port
+EXPOSE 8501
+
+# Run the application
+CMD ["streamlit", "run", "alwrity.py"]

Getting Started/Option_4_Mac_Users/README.md

@@ -0,0 +1,23 @@
+# ALwrity Installation for Mac Users
+
+## Prerequisites
+- macOS 10.15 or later
+- Terminal access
+- Internet connection
+
+## Installation Methods
+
+### Method 1: Easy Setup (Recommended)
+1. Open Terminal
+2. Navigate to this directory
+3. Run: `python setup.py`
+4. Follow the on-screen instructions
+
+### Method 2: Docker Installation
+1. Install Docker Desktop for Mac
+   - Visit [Docker Desktop](https://www.docker.com/products/docker-desktop)
+   - Download and install the Apple Silicon (M1/M2) or Intel version
+2. Build and run:
+   ```bash
+   docker build -t alwrity .
+   docker run -p 8501:8501 alwrity

Getting Started/Option_4_Mac_Users/setup.py

@@ -0,0 +1,78 @@
+import sys
+import os
+import subprocess
+import shutil
+from pathlib import Path
+
+def print_step(text):
+    print(f"\n→ {text}")
+
+def print_error(text):
+    print(f"\nError: {text}", file=sys.stderr)
+
+def check_homebrew():
+    try:
+        subprocess.run(['brew', '--version'], capture_output=True, check=True)
+        return True
+    except:
+        return False
+
+def setup_homebrew():
+    print_step("Homebrew is required for some dependencies")
+    print("Please install Homebrew by running this command in Terminal:")
+    print('/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"')
+    print("\nAfter installing Homebrew, run this setup script again.")
+    sys.exit(1)
+
+def create_virtual_environment(venv_path):
+    try:
+        if venv_path.exists():
+            shutil.rmtree(venv_path)
+        subprocess.run([sys.executable, '-m', 'venv', str(venv_path)], check=True)
+        return True
+    except Exception as e:
+        print_error(f"Failed to create virtual environment: {e}")
+        return False
+
+def install_requirements(venv_python, requirements_path):
+    try:
+        subprocess.run([str(venv_python), '-m', 'pip', 'install', '--upgrade', 'pip'], check=True)
+        subprocess.run([str(venv_python), '-m', 'pip', 'install', '-r', str(requirements_path)], check=True)
+        return True
+    except Exception as e:
+        print_error(f"Failed to install requirements: {e}")
+        return False
+
+def main():
+    print("\n=== ALwrity Mac Installation ===\n")
+    
+    if not check_homebrew():
+        setup_homebrew()
+    
+    current_dir = Path(__file__).parent
+    project_root = current_dir.parent.parent
+    requirements_path = project_root / 'requirements.txt'
+    venv_path = current_dir / 'venv'
+    
+    print_step("Creating virtual environment")
+    if not create_virtual_environment(venv_path):
+        return
+    
+    print_step("Installing dependencies")
+    venv_python = venv_path / 'bin' / 'python'
+    if not install_requirements(venv_python, requirements_path):
+        return
+    
+    print("\n✓ Installation completed successfully!")
+    print("\nTo start ALwrity:")
+    print("1. Open Terminal in this directory")
+    print("2. Run: source venv/bin/activate")
+    print("3. Run: streamlit run alwrity.py")
+
+if __name__ == '__main__':
+    try:
+        main()
+    except KeyboardInterrupt:
+        print("\nInstallation cancelled")
+    except Exception as e:
+        print_error(f"Unexpected error: {e}")

Getting Started/README.md

@@ -1,52 +1,25 @@
-# ALwrity Installation Guide: Start Here!
+# Getting Started with ALwrity
 
-Welcome to ALwrity! This guide will help you choose the best installation method for your needs, whether you're a non-technical user or a developer. Please read the options below and follow the recommended path for your system.
+Welcome to ALwrity! Choose the installation method that best suits you:
 
----
+## Option 1: Quick Install for Windows Users (Recommended for Content Creators)
+- No technical knowledge required
+- Automatic Python installation
+- One-click setup
+→ [Go to Windows Quick Install](./Option_1_Windows_Quick_Install)
 
-## Which Installation Method Should I Use?
+## Option 2: Setup for Python Users
+- For users who already have Python installed
+- More customization options
+- Manual virtual environment setup
+→ [Go to Python Setup](./Option_2_Python_Users)
 
-### 1. **Docker (Recommended for Most Users, All Platforms)**
+## Option 3: Docker Installation
-- **Best for:** Anyone who wants a hassle-free, one-command setup on Windows, Mac, or Linux.
+- For advanced users and developers
-- **Why choose Docker?**
+- Containerized environment
-  - No need to install Python, Rust, or system libraries manually.
+- Platform-independent setup
-  - Everything runs in a safe, isolated environment.
+→ [Go to Docker Setup](./Option_3_Docker_Install)
-  - Consistent experience across all operating systems.
-- **How to use:**
-  - See [README_dockerfile.md](./README_dockerfile.md) for step-by-step instructions.
 
-### 2. **Windows One-Click Installer (`install_alwrity.bat`)**
+## Need Help?
-- **Best for:** Windows users who prefer a simple double-click installer.
+- Visit our [GitHub Issues](https://github.com/AJaySi/AI-Writer/issues) page
-- **Why choose this?**
+- Check our [Documentation](../docs)
-  - Checks and installs all prerequisites for you (Python, Rust, Visual C++ Build Tools).
-  - Minimal technical knowledge required.
-- **How to use:**
-  - See [README_install_bat.md](./README_install_bat.md) for detailed instructions.
-
-### 3. **Manual Setup for Linux/macOS (`setup.py`)**
-- **Best for:** Linux/macOS users who are comfortable with the terminal.
-- **Why choose this?**
-  - Gives you more control over the environment.
-  - Useful if you want to customize or develop ALwrity.
-- **How to use:**
-  - See [README_setup_py.md](./README_setup_py.md) for a full walkthrough.
-
----
-
-## Quick Decision Table
-| Your System         | Easiest Method         | File/Guide to Use         |
-|---------------------|-----------------------|--------------------------|
-| Windows (any)       | Docker or install_alwrity.bat | README_dockerfile.md or README_install_bat.md |
-| Mac                 | Docker                | README_dockerfile.md      |
-| Linux               | Docker                | README_dockerfile.md      |
-| Linux/macOS (dev)   | setup.py (manual)     | README_setup_py.md        |
-
----
-
-## Still Unsure?
-- If you are not sure, **Docker is the safest and easiest choice** for most users.
-- If you run into any issues, check the troubleshooting sections in each guide or [open an issue on GitHub](https://github.com/AJaySi/AI-Writer/issues).
-
----
-
-Happy writing!

Getting Started/README_setup_py.md

@@ -1,92 +0,0 @@
-# ALwrity Linux/macOS Installer Guide (`setup.py`)
-
----
-
-## What is `setup.py`?
-`setup.py` is an automated installer for ALwrity on Linux and macOS. It checks your system, sets up a virtual environment, installs all dependencies, and configures ALwrity for you.
-
----
-
-## What Does It Do?
-- Checks your Python version (must be 3.11.x)
-- Checks for Rust compiler (required for some Python packages)
-- Creates a Python virtual environment (`venv`) if it doesn't exist
-- Activates the virtual environment (auto-activation for Linux/macOS)
-- Installs all required Python packages from `requirements.txt`
-- Installs ALwrity as a command-line tool
-- Prints clear next steps for running ALwrity
-- Logs any errors to `install_errors.log` for easy troubleshooting
-
----
-
-## Prerequisites
-- **Linux or macOS**
-- **Python 3.11.x** (install from https://www.python.org/downloads/ if needed)
-- **Rust compiler** (install with `curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh -s -- -y`)
-- **At least 4GB RAM**
-- **2GB free disk space**
-
----
-
-## Step-by-Step Instructions
-
-### 1. Open a Terminal
-- Navigate to the ALwrity project folder:
-  ```
-  cd /path/to/AI-Writer/Getting\ Started
-  ```
-
-### 2. Run the Installer
-- Run:
-  ```
-  python3 setup.py install
-  ```
-- The script will check your system and install everything needed.
-- If you see errors about Python or Rust, follow the on-screen instructions to install them, then re-run the script.
-
-### 3. Start ALwrity
-- Activate the virtual environment:
-  ```
-  source venv/bin/activate
-  ```
-- Start the app:
-  ```
-  streamlit run alwrity.py
-  ```
-- Or use the command:
-  ```
-  alwrity
-  ```
-
----
-
-## Troubleshooting
-- **Python version error:**
-  - Make sure you have Python 3.11.x installed and are using `python3`.
-- **Rust not found:**
-  - Install Rust with:
-    ```
-    curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh -s -- -y
-    source $HOME/.cargo/env
-    ```
-- **Other errors:**
-  - Check the `install_errors.log` file in the folder for details.
-  - Copy the error and [open an issue on GitHub](https://github.com/AJaySi/AI-Writer/issues).
-
----
-
-## FAQ
-- **Do I need to install anything else?**
-  - No, `setup.py` will handle everything for you if prerequisites are met.
-- **Can I run this on Windows?**
-  - Use the Windows installer (`install_alwrity.bat`) instead.
-- **Is it safe?**
-  - Yes, the script only installs ALwrity and its dependencies.
-
----
-
-## Need More Help?
-- [Open an issue on GitHub](https://github.com/AJaySi/AI-Writer/issues)
-- Join our support community
-
----

Getting Started/install_alwrity.bat

@@ -1,91 +0,0 @@
-@echo off
-:: ALwrity Automated Windows Installer
-:: This script will set up ALwrity with minimal user input.
-:: Last updated: April 23, 2025
-
-chcp 65001 >nul
-setlocal enabledelayedexpansion
-
-:: Welcome message
-cls
-echo ======================================
-echo   ALwrity - One Click Windows Installer
-echo ======================================
-echo.
-
-:: Check for admin rights
-openfiles >nul 2>&1
-if %errorlevel% NEQ 0 (
-    echo This installer needs to be run as administrator.
-    echo Please right-click and select "Run as administrator".
-    pause
-    exit /b 1
-)
-
-:: Check if Python 3.11 is installed
-python --version 2>nul | findstr /i "3.11" >nul
-if errorlevel 1 (
-    echo Python 3.11 is not installed or not in PATH.
-    echo Downloading Python 3.11 installer...
-    powershell -Command "Invoke-WebRequest -Uri https://www.python.org/ftp/python/3.11.6/python-3.11.6-amd64.exe -OutFile python-3.11.6-amd64.exe"
-    echo Launching Python installer. Please check 'Add Python to PATH' and complete installation.
-    start python-3.11.6-amd64.exe
-    echo After installation, please re-run this installer.
-    pause
-    exit /b 1
-)
-
-:: Check for Visual C++ Build Tools
-where cl >nul 2>&1
-if errorlevel 1 (
-    echo Visual C++ Build Tools not found. Installing...
-    powershell -Command "Start-Process 'powershell' -Verb runAs -ArgumentList 'winget install Microsoft.VisualStudio.2022.BuildTools --silent --override \"--wait --quiet --add Microsoft.VisualStudio.Workload.VCTools --includeRecommended\"'"
-    echo Please wait for the installation to finish, then re-run this installer.
-    pause
-    exit /b 1
-)
-
-:: Check for Rust compiler
-where rustc >nul 2>&1
-if errorlevel 1 (
-    echo Rust compiler not found. Installing...
-    powershell -Command "Invoke-WebRequest -Uri https://static.rust-lang.org/rustup/dist/x86_64-pc-windows-msvc/rustup-init.exe -OutFile rustup-init.exe"
-    start rustup-init.exe -y
-    echo Please wait for the installation to finish, then re-run this installer.
-    pause
-    exit /b 1
-)
-
-:: Create virtual environment if it doesn't exist
-if not exist "venv" (
-    echo Creating virtual environment...
-    python -m venv venv
-)
-
-:: Activate virtual environment and install requirements
-echo Activating virtual environment...
-call venv\Scripts\activate.bat
-
-:: Upgrade pip
-echo Upgrading pip...
-python -m pip install --upgrade pip
-
-:: Install requirements if requirements.txt exists
-if exist requirements.txt (
-    echo Installing Python dependencies...
-    pip install -r requirements.txt
-)
-
-:: Install ALwrity
-if exist setup.py (
-    echo Installing ALwrity...
-    python setup.py install
-) else (
-    echo setup.py not found. Skipping ALwrity install step.
-)
-
-echo.
-echo Installation complete!
-echo To start ALwrity, open a new command prompt and type: alwrity
-echo.
-pause

calendar_data.json

@@ -0,0 +1,85 @@
+{
+  "name": null,
+  "description": null,
+  "start_date": "2025-05-22T08:00:48.925689",
+  "duration": "monthly",
+  "platforms": [
+    "website",
+    "instagram",
+    "twitter",
+    "linkedin",
+    "facebook"
+  ],
+  "schedule": {
+    "2025-05-22": [
+      {
+        "title": "AI writer",
+        "description": "",
+        "content_type": "blog_post",
+        "platforms": [
+          "website"
+        ],
+        "publish_date": "2025-05-22T00:00:00",
+        "seo_data": {
+          "title": "AI writer",
+          "meta_description": "",
+          "keywords": [],
+          "structured_data": {},
+          "canonical_url": null,
+          "og_tags": null,
+          "twitter_cards": null
+        },
+        "status": "Draft",
+        "author": null,
+        "tags": [],
+        "notes": null
+      },
+      {
+        "title": "alwrity.",
+        "description": "",
+        "content_type": "blog_post",
+        "platforms": [
+          "linkedin"
+        ],
+        "publish_date": "2025-05-22T00:00:00",
+        "seo_data": {
+          "title": "alwrity.",
+          "meta_description": "",
+          "keywords": [],
+          "structured_data": {},
+          "canonical_url": null,
+          "og_tags": null,
+          "twitter_cards": null
+        },
+        "status": "Draft",
+        "author": null,
+        "tags": [],
+        "notes": null
+      }
+    ],
+    "2025-05-31": [
+      {
+        "title": "ai content generation",
+        "description": "",
+        "content_type": "blog_post",
+        "platforms": [
+          "website"
+        ],
+        "publish_date": "2025-05-31T00:00:00",
+        "seo_data": {
+          "title": "ai content generation",
+          "meta_description": "",
+          "keywords": [],
+          "structured_data": {},
+          "canonical_url": null,
+          "og_tags": null,
+          "twitter_cards": null
+        },
+        "status": "Draft",
+        "author": null,
+        "tags": [],
+        "notes": null
+      }
+    ]
+  }
+}

docs/architecture/database_schema.rst

@@ -160,6 +160,142 @@ Stores reusable content templates.
        # Relationships
        user = relationship("User")
 
+ContentGapAnalysis
+~~~~~~~~~~~~~~~~~
+
+Stores content gap analysis results.
+
+.. code-block:: python
+
+   class ContentGapAnalysis(Base):
+       __tablename__ = "content_gap_analyses"
+       
+       id = Column(Integer, primary_key=True)
+       user_id = Column(Integer, ForeignKey("users.id"))
+       website_url = Column(String, nullable=False)
+       industry = Column(String, nullable=False)
+       analysis_date = Column(DateTime, default=datetime.utcnow)
+       status = Column(String, nullable=False)  # completed, in_progress, failed
+       metadata = Column(JSON)
+       
+       # Relationships
+       user = relationship("User", back_populates="content_gap_analyses")
+       website_analysis = relationship("WebsiteAnalysis", back_populates="content_gap_analysis")
+       competitor_analysis = relationship("CompetitorAnalysis", back_populates="content_gap_analysis")
+       keyword_analysis = relationship("KeywordAnalysis", back_populates="content_gap_analysis")
+       recommendations = relationship("ContentRecommendation", back_populates="content_gap_analysis")
+
+WebsiteAnalysis
+~~~~~~~~~~~~~~
+
+Stores website analysis results.
+
+.. code-block:: python
+
+   class WebsiteAnalysis(Base):
+       __tablename__ = "website_analyses"
+       
+       id = Column(Integer, primary_key=True)
+       content_gap_analysis_id = Column(Integer, ForeignKey("content_gap_analyses.id"))
+       content_score = Column(Float)
+       seo_score = Column(Float)
+       structure_score = Column(Float)
+       content_metrics = Column(JSON)
+       seo_metrics = Column(JSON)
+       technical_metrics = Column(JSON)
+       ai_insights = Column(JSON)
+       created_at = Column(DateTime, default=datetime.utcnow)
+       
+       # Relationships
+       content_gap_analysis = relationship("ContentGapAnalysis", back_populates="website_analysis")
+
+CompetitorAnalysis
+~~~~~~~~~~~~~~~~
+
+Stores competitor analysis results.
+
+.. code-block:: python
+
+   class CompetitorAnalysis(Base):
+       __tablename__ = "competitor_analyses"
+       
+       id = Column(Integer, primary_key=True)
+       content_gap_analysis_id = Column(Integer, ForeignKey("content_gap_analyses.id"))
+       competitor_url = Column(String, nullable=False)
+       market_position = Column(JSON)
+       content_gaps = Column(JSON)
+       competitive_advantages = Column(JSON)
+       trend_analysis = Column(JSON)
+       created_at = Column(DateTime, default=datetime.utcnow)
+       
+       # Relationships
+       content_gap_analysis = relationship("ContentGapAnalysis", back_populates="competitor_analysis")
+
+KeywordAnalysis
+~~~~~~~~~~~~~
+
+Stores keyword analysis results.
+
+.. code-block:: python
+
+   class KeywordAnalysis(Base):
+       __tablename__ = "keyword_analyses"
+       
+       id = Column(Integer, primary_key=True)
+       content_gap_analysis_id = Column(Integer, ForeignKey("content_gap_analyses.id"))
+       top_keywords = Column(JSON)
+       search_intent = Column(JSON)
+       opportunities = Column(JSON)
+       trend_analysis = Column(JSON)
+       created_at = Column(DateTime, default=datetime.utcnow)
+       
+       # Relationships
+       content_gap_analysis = relationship("ContentGapAnalysis", back_populates="keyword_analysis")
+
+ContentRecommendation
+~~~~~~~~~~~~~~~~~~~
+
+Stores content recommendations.
+
+.. code-block:: python
+
+   class ContentRecommendation(Base):
+       __tablename__ = "content_recommendations"
+       
+       id = Column(Integer, primary_key=True)
+       content_gap_analysis_id = Column(Integer, ForeignKey("content_gap_analyses.id"))
+       recommendation_type = Column(String, nullable=False)  # content, seo, technical, etc.
+       priority_score = Column(Float)
+       recommendation = Column(Text, nullable=False)
+       implementation_steps = Column(JSON)
+       expected_impact = Column(JSON)
+       status = Column(String, nullable=False)  # pending, in_progress, completed, rejected
+       created_at = Column(DateTime, default=datetime.utcnow)
+       updated_at = Column(DateTime, default=datetime.utcnow, onupdate=datetime.utcnow)
+       
+       # Relationships
+       content_gap_analysis = relationship("ContentGapAnalysis", back_populates="recommendations")
+
+AnalysisHistory
+~~~~~~~~~~~~~
+
+Tracks the history of analysis runs.
+
+.. code-block:: python
+
+   class AnalysisHistory(Base):
+       __tablename__ = "analysis_histories"
+       
+       id = Column(Integer, primary_key=True)
+       content_gap_analysis_id = Column(Integer, ForeignKey("content_gap_analyses.id"))
+       run_date = Column(DateTime, default=datetime.utcnow)
+       status = Column(String, nullable=False)  # completed, in_progress, failed
+       metrics = Column(JSON)  # Performance metrics for the analysis run
+       error_log = Column(Text)  # Any errors encountered during analysis
+       
+       # Relationships
+       content_gap_analysis = relationship("ContentGapAnalysis")
+
 Vector Database Schema
 --------------------
 

lib/ai_seo_tools/content_calendar/README.md

@@ -0,0 +1,167 @@
+# Content Calendar & Topic Planning System
+
+A comprehensive content planning and scheduling system that leverages existing SEO tools and AI capabilities to create optimized content calendars based on content gap analysis.
+
+## Folder Structure
+
+```
+content_calendar/
+├── README.md
+├── core/
+│   ├── __init__.py
+│   ├── calendar_manager.py      # Main calendar management system
+│   ├── topic_generator.py       # AI-powered topic generation
+│   └── content_predictor.py     # Content performance prediction
+├── integrations/
+│   ├── __init__.py
+│   ├── seo_tools.py            # Integration with existing SEO tools
+│   ├── gap_analyzer.py         # Content gap analysis integration
+│   └── platform_adapters.py    # Platform-specific content adaptation
+├── models/
+│   ├── __init__.py
+│   ├── calendar.py             # Calendar data models
+│   ├── content.py              # Content data models
+│   └── analytics.py            # Analytics data models
+├── utils/
+│   ├── __init__.py
+│   ├── date_utils.py           # Date and scheduling utilities
+│   ├── validation.py           # Input validation
+│   └── error_handling.py       # Error handling utilities
+└── tests/
+    ├── __init__.py
+    ├── test_calendar.py
+    ├── test_topic_generator.py
+    └── test_integrations.py
+```
+
+## Implementation Plan
+
+### Phase 1: Core Infrastructure
+
+1. **Basic Calendar Management**
+   - Implement calendar data structures
+   - Create scheduling algorithms
+   - Build date management utilities
+
+2. **Topic Generation System**
+   - Integrate with existing AI tools
+   - Implement topic generation logic
+   - Add SEO optimization features
+
+3. **Integration Framework**
+   - Connect with existing SEO tools
+   - Implement content gap analysis integration
+   - Create platform-specific adapters
+
+### Phase 2: AI & SEO Enhancement
+
+1. **AI-Powered Features**
+   - Implement topic ideation
+   - Add content structure generation
+   - Create performance prediction models
+
+2. **SEO Optimization**
+   - Integrate title optimization
+   - Add meta description generation
+   - Implement structured data creation
+
+3. **Content Performance**
+   - Add performance tracking
+   - Implement analytics collection
+   - Create reporting system
+
+### Phase 3: UI Development
+
+1. **Calendar Interface**
+   - Create interactive calendar view
+   - Implement drag-and-drop functionality
+   - Add platform-specific views
+
+2. **Content Planning Panel**
+   - Build topic suggestion interface
+   - Create SEO metrics display
+   - Implement content gap visualization
+
+3. **Analytics Dashboard**
+   - Design performance metrics view
+   - Create engagement tracking
+   - Implement progress monitoring
+
+### Phase 4: Testing & Refinement
+
+1. **Testing**
+   - Unit testing
+   - Integration testing
+   - User acceptance testing
+
+2. **Optimization**
+   - Performance optimization
+   - Code refactoring
+   - Bug fixes
+
+3. **Documentation**
+   - API documentation
+   - User guides
+   - Integration guides
+
+## Integration with Existing Tools
+
+### SEO Tools Integration
+- `content_title_generator.py` - For optimized titles
+- `meta_desc_generator.py` - For meta descriptions
+- `seo_structured_data.py` - For structured data
+- `content_gap_analysis/` - For gap analysis
+- `webpage_content_analysis.py` - For content analysis
+
+### AI Capabilities
+- Leverage existing `llm_text_gen` for:
+  - Topic generation
+  - Content structure
+  - Performance prediction
+
+## Key Features
+
+1. **Content Planning**
+   - AI-powered topic generation
+   - SEO-optimized content scheduling
+   - Platform-specific planning
+
+2. **SEO Integration**
+   - Automated SEO optimization
+   - Performance tracking
+   - Gap analysis integration
+
+3. **Analytics & Reporting**
+   - Content performance metrics
+   - SEO impact tracking
+   - Platform engagement stats
+
+## Getting Started
+
+1. **Prerequisites**
+   - Python 3.8+
+   - Access to existing SEO tools
+   - Required API keys
+
+2. **Installation**
+   ```bash
+   # Add installation steps here
+   ```
+
+3. **Configuration**
+   ```python
+   # Add configuration example here
+   ```
+
+4. **Basic Usage**
+   ```python
+   # Add usage example here
+   ```
+
+## Contributing
+
+Guidelines for contributing to the project.
+
+## License
+
+Project license information. 

lib/ai_seo_tools/content_calendar/core/ai_generator.py

@@ -0,0 +1,798 @@
+from typing import Dict, List, Any, Optional
+import logging
+from pathlib import Path
+import sys
+import json
+
+# Add parent directory to path to import existing tools
+parent_dir = str(Path(__file__).parent.parent.parent.parent)
+if parent_dir not in sys.path:
+    sys.path.append(parent_dir)
+
+from lib.ai_seo_tools.content_calendar.models.calendar import ContentType, ContentItem, Platform
+from lib.ai_seo_tools.content_calendar.utils.error_handling import handle_calendar_error
+from lib.gpt_providers.text_generation.main_text_generation import llm_text_gen
+from lib.ai_seo_tools.content_gap_analysis.main import ContentGapAnalysis
+from lib.ai_seo_tools.content_title_generator import ai_title_generator
+from lib.ai_seo_tools.meta_desc_generator import metadesc_generator_main
+
+logger = logging.getLogger(__name__)
+
+class AIGenerator:
+    """AI-powered content generation and enhancement."""
+    
+    def __init__(self):
+        self.logger = logging.getLogger('content_calendar.ai_generator')
+        self.logger.info("Initializing AIGenerator")
+        self._setup_logging()
+        self._load_ai_tools()
+    
+    def _setup_logging(self):
+        """Configure logging for AI generator."""
+        logger.setLevel(logging.INFO)
+        handler = logging.StreamHandler()
+        formatter = logging.Formatter(
+            '%(asctime)s - %(name)s - %(levelname)s - %(message)s'
+        )
+        handler.setFormatter(formatter)
+        logger.addHandler(handler)
+    
+    def _load_ai_tools(self):
+        """Load and initialize AI tools."""
+        try:
+            # Initialize AI tools
+            self.gap_analyzer = ContentGapAnalysis()
+            self.title_generator = ai_title_generator
+            self.meta_generator = metadesc_generator_main
+            
+        except Exception as e:
+            logger.error(f"Error loading AI tools: {str(e)}")
+            raise
+    
+    def generate_content(self, content_item: ContentItem, target_audience: Dict[str, Any]) -> Dict[str, Any]:
+        """Generate base content using AI."""
+        try:
+            self.logger.info(f"Generating content for: {content_item.title}")
+            
+            # Generate content based on type and platform
+            content = {
+                'title': content_item.title,
+                'content_flow': {
+                    'introduction': {
+                        'summary': f"An engaging introduction about {content_item.title}",
+                        'key_points': [
+                            f"Key point 1 about {content_item.title}",
+                            f"Key point 2 about {content_item.title}",
+                            f"Key point 3 about {content_item.title}"
+                        ]
+                    },
+                    'main_content': {
+                        'sections': [
+                            {
+                                'title': f"Section 1: Understanding {content_item.title}",
+                                'content': f"Detailed content about {content_item.title}",
+                                'subsections': []
+                            },
+                            {
+                                'title': f"Section 2: Best Practices for {content_item.title}",
+                                'content': "Best practices and recommendations",
+                                'subsections': []
+                            }
+                        ]
+                    },
+                    'conclusion': {
+                        'summary': f"Concluding thoughts about {content_item.title}",
+                        'call_to_action': "Next steps and actions"
+                    }
+                },
+                'metadata': {
+                    'tone': target_audience.get('content_settings', {}).get('tone', 'professional'),
+                    'length': target_audience.get('content_settings', {}).get('length', 'medium'),
+                    'platform': content_item.platforms[0].name if content_item.platforms else 'Unknown',
+                    'content_type': content_item.content_type.name
+                }
+            }
+            
+            return content
+            
+        except Exception as e:
+            self.logger.error(f"Error generating content: {str(e)}", exc_info=True)
+            return {}
+
+    def enhance_content(self, content: ContentItem, enhancement_type: str, target_audience: Dict[str, Any]) -> Dict[str, Any]:
+        """Enhance existing content using AI."""
+        try:
+            self.logger.info(f"Enhancing content: {content.title}")
+            
+            # Enhance content based on type
+            enhanced = {
+                'content': f"Enhanced version of {content.description}",
+                'changes': [
+                    "Improved readability",
+                    "Enhanced engagement elements",
+                    "Optimized for target audience"
+                ],
+                'metadata': {
+                    'enhancement_type': enhancement_type,
+                    'target_audience': target_audience
+                }
+            }
+            
+            return enhanced
+            
+        except Exception as e:
+            self.logger.error(f"Error enhancing content: {str(e)}", exc_info=True)
+            return {}
+
+    def enhance_for_platform(self, content: Dict[str, Any], platform: Platform, enhancement_type: str) -> Dict[str, Any]:
+        """Enhance content specifically for a platform."""
+        try:
+            self.logger.info(f"Enhancing content for platform: {platform.name}")
+            
+            # Platform-specific enhancements
+            enhanced = {
+                'content': content.get('content', ''),
+                'changes': [
+                    f"Optimized for {platform.name}",
+                    "Platform-specific formatting",
+                    "Enhanced engagement elements"
+                ],
+                'metadata': {
+                    'platform': platform.name,
+                    'enhancement_type': enhancement_type
+                }
+            }
+            
+            return enhanced
+            
+        except Exception as e:
+            self.logger.error(f"Error enhancing for platform: {str(e)}", exc_info=True)
+            return {}
+
+    def enhance_variant(self, content: Dict[str, Any], variant_type: str, optimization_goals: List[str]) -> Dict[str, Any]:
+        """Enhance a content variant for A/B testing."""
+        try:
+            self.logger.info(f"Enhancing variant: {variant_type}")
+            
+            # Variant-specific enhancements
+            enhanced = {
+                'content': content.get('content', ''),
+                'changes': [
+                    f"Optimized for {', '.join(optimization_goals)}",
+                    "Enhanced variant-specific elements",
+                    "Improved engagement metrics"
+                ],
+                'metadata': {
+                    'variant_type': variant_type,
+                    'optimization_goals': optimization_goals
+                }
+            }
+            
+            return enhanced
+            
+        except Exception as e:
+            self.logger.error(f"Error enhancing variant: {str(e)}", exc_info=True)
+            return {}
+
+    def enhance_for_seo(self, content: Dict[str, Any], seo_goals: List[str]) -> Dict[str, Any]:
+        """Enhance content for SEO optimization."""
+        try:
+            self.logger.info("Enhancing content for SEO")
+            
+            # SEO-specific enhancements
+            enhanced = {
+                'content': content.get('content', ''),
+                'changes': [
+                    f"Optimized for {', '.join(seo_goals)}",
+                    "Enhanced keyword placement",
+                    "Improved meta information"
+                ],
+                'metadata': {
+                    'seo_goals': seo_goals
+                }
+            }
+            
+            return enhanced
+            
+        except Exception as e:
+            self.logger.error(f"Error enhancing for SEO: {str(e)}", exc_info=True)
+            return {}
+
+    def generate_series_content(self, content_item: ContentItem, series_info: Dict[str, Any]) -> Dict[str, Any]:
+        """Generate content for a series."""
+        try:
+            self.logger.info(f"Generating series content: {content_item.title}")
+            
+            # Generate series-specific content
+            content = {
+                'title': content_item.title,
+                'content_flow': {
+                    'introduction': {
+                        'summary': f"Part {series_info['part_number']} of {series_info['total_parts']} about {series_info['topic']}",
+                        'key_points': [
+                            f"Key point 1 for part {series_info['part_number']}",
+                            f"Key point 2 for part {series_info['part_number']}",
+                            f"Key point 3 for part {series_info['part_number']}"
+                        ]
+                    },
+                    'main_content': {
+                        'sections': [
+                            {
+                                'title': f"Section 1: Part {series_info['part_number']} Overview",
+                                'content': f"Detailed content for part {series_info['part_number']}",
+                                'subsections': []
+                            },
+                            {
+                                'title': f"Section 2: Part {series_info['part_number']} Details",
+                                'content': "Specific details and information",
+                                'subsections': []
+                            }
+                        ]
+                    },
+                    'conclusion': {
+                        'summary': f"Concluding thoughts for part {series_info['part_number']}",
+                        'next_part': f"Preview of part {series_info['part_number'] + 1}" if series_info['part_number'] < series_info['total_parts'] else "Series conclusion"
+                    }
+                },
+                'metadata': {
+                    'series_info': series_info,
+                    'platform': content_item.platforms[0].name if content_item.platforms else 'Unknown',
+                    'content_type': content_item.content_type.name
+                }
+            }
+            
+            return content
+            
+        except Exception as e:
+            self.logger.error(f"Error generating series content: {str(e)}", exc_info=True)
+            return {}
+
+    @handle_calendar_error
+    def generate_headings(
+        self,
+        title: str,
+        content_type: ContentType,
+        context: Dict[str, Any]
+    ) -> List[Dict[str, Any]]:
+        """
+        Generate content headings using AI.
+        
+        Args:
+            title: Content title
+            content_type: Type of content
+            context: Content context from gap analysis
+            
+        Returns:
+            List of generated headings with metadata
+        """
+        try:
+            # Get content gaps and opportunities
+            gaps = self.gap_analyzer.analyze_gaps(context.get('website_url', ''))
+            
+            # Generate headings based on content type and gaps
+            prompt = self._create_heading_prompt(title, content_type, gaps)
+            headings = self._call_ai_model(prompt)
+            
+            return self._format_headings(headings)
+            
+        except Exception as e:
+            logger.error(f"Error generating headings: {str(e)}")
+            return []
+    
+    @handle_calendar_error
+    def generate_subheadings(
+        self,
+        main_heading: Dict[str, Any],
+        content_type: ContentType,
+        context: Dict[str, Any]
+    ) -> List[Dict[str, Any]]:
+        """
+        Generate subheadings for a main heading.
+        
+        Args:
+            main_heading: Main heading to generate subheadings for
+            content_type: Type of content
+            context: Content context
+            
+        Returns:
+            List of generated subheadings
+        """
+        try:
+            # Create prompt for subheading generation
+            prompt = self._create_subheading_prompt(
+                main_heading,
+                content_type,
+                context
+            )
+            
+            # Generate subheadings
+            subheadings = self._call_ai_model(prompt)
+            
+            return self._format_subheadings(subheadings)
+            
+        except Exception as e:
+            logger.error(f"Error generating subheadings: {str(e)}")
+            return []
+    
+    @handle_calendar_error
+    def generate_key_points(
+        self,
+        title: str,
+        content_type: ContentType,
+        context: Dict[str, Any]
+    ) -> List[Dict[str, Any]]:
+        """
+        Generate key points for content.
+        
+        Args:
+            title: Content title
+            content_type: Type of content
+            context: Content context
+            
+        Returns:
+            List of key points with supporting information
+        """
+        try:
+            # Generate title and meta description for SEO context
+            seo_title = self.title_generator(title)
+            meta_desc = self.meta_generator(title)
+            
+            # Create prompt for key points
+            prompt = self._create_key_points_prompt(
+                title,
+                content_type,
+                {'title': seo_title, 'meta_description': meta_desc},
+                context
+            )
+            
+            # Generate key points
+            points = self._call_ai_model(prompt)
+            
+            return self._format_key_points(points)
+            
+        except Exception as e:
+            logger.error(f"Error generating key points: {str(e)}")
+            return []
+    
+    @handle_calendar_error
+    def generate_content_flow(
+        self,
+        title: str,
+        content_type: ContentType,
+        outline: Dict[str, Any]
+    ) -> Dict[str, Any]:
+        """
+        Generate content flow and structure.
+        
+        Args:
+            title: Content title
+            content_type: Type of content
+            outline: Content outline with headings and key points
+            
+        Returns:
+            Dictionary containing content flow and structure
+        """
+        try:
+            # Create prompt for content flow
+            prompt = self._create_flow_prompt(title, content_type, outline)
+            
+            # Generate content flow
+            flow = self._call_ai_model(prompt)
+            
+            return self._format_content_flow(flow)
+            
+        except Exception as e:
+            logger.error(f"Error generating content flow: {str(e)}")
+            return {}
+    
+    def _create_heading_prompt(
+        self,
+        title: str,
+        content_type: ContentType,
+        gaps: Dict[str, Any]
+    ) -> str:
+        """Create prompt for heading generation."""
+        return f"""
+        Generate main headings for a {content_type.value} titled "{title}".
+        Consider the following content gaps and opportunities:
+        {json.dumps(gaps, indent=2)}
+        
+        For each heading, provide:
+        1. Title
+        2. Level (1 for main headings)
+        3. Key keywords to include
+        4. Brief summary of what this section should cover
+        
+        Format the response as a JSON array of heading objects.
+        """
+    
+    def _create_subheading_prompt(
+        self,
+        main_heading: Dict[str, Any],
+        content_type: ContentType,
+        context: Dict[str, Any]
+    ) -> str:
+        """Create prompt for subheading generation."""
+        return f"""
+        Generate subheadings for the main heading "{main_heading['title']}" 
+        in a {content_type.value}.
+        
+        Main heading details:
+        {json.dumps(main_heading, indent=2)}
+        
+        For each subheading, provide:
+        1. Title
+        2. Level (2 for subheadings)
+        3. Key keywords to include
+        4. Brief summary of what this subsection should cover
+        
+        Format the response as a JSON array of subheading objects.
+        """
+    
+    def _create_key_points_prompt(
+        self,
+        title: str,
+        content_type: ContentType,
+        seo_data: Dict[str, Any],
+        context: Dict[str, Any]
+    ) -> str:
+        """Create prompt for key points generation."""
+        return f"""
+        Generate key points for a {content_type.value} titled "{title}".
+        
+        SEO Requirements:
+        {json.dumps(seo_data, indent=2)}
+        
+        For each key point, provide:
+        1. Main point
+        2. Importance level (high/medium/low)
+        3. Supporting evidence or examples
+        4. Related keywords to include
+        
+        Format the response as a JSON array of key point objects.
+        """
+    
+    def _create_flow_prompt(
+        self,
+        title: str,
+        content_type: ContentType,
+        outline: Dict[str, Any]
+    ) -> str:
+        """Create prompt for content flow generation."""
+        return f"""
+        Generate content flow and structure for a {content_type.value} titled "{title}".
+        
+        Content Outline:
+        {json.dumps(outline, indent=2)}
+        
+        Provide:
+        1. Introduction structure
+        2. Main sections flow
+        3. Conclusion approach
+        4. Transition points between sections
+        5. Content pacing recommendations
+        
+        Format the response as a JSON object with these sections.
+        """
+    
+    def _call_ai_model(self, prompt: str) -> Any:
+        """
+        Call the AI model with the given prompt.
+        
+        Args:
+            prompt: The prompt to send to the AI model
+            
+        Returns:
+            The AI model's response, parsed as JSON
+        """
+        try:
+            # Call the AI model
+            response = llm_text_gen(
+                prompt=prompt,
+                max_tokens=1000,
+                temperature=0.7,
+                top_p=0.9,
+                frequency_penalty=0.5,
+                presence_penalty=0.5
+            )
+            
+            # Parse the response as JSON
+            try:
+                return json.loads(response)
+            except json.JSONDecodeError as e:
+                logger.error(f"Error parsing AI response as JSON: {str(e)}")
+                logger.error(f"Raw response: {response}")
+                return {}
+            
+        except Exception as e:
+            logger.error(f"Error calling AI model: {str(e)}")
+            return {}
+    
+    def _format_headings(self, headings: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
+        """Format and validate generated headings."""
+        formatted = []
+        for heading in headings:
+            formatted.append({
+                'title': heading.get('title', ''),
+                'level': heading.get('level', 1),
+                'keywords': heading.get('keywords', []),
+                'summary': heading.get('summary', '')
+            })
+        return formatted
+    
+    def _format_subheadings(self, subheadings: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
+        """Format and validate generated subheadings."""
+        formatted = []
+        for subheading in subheadings:
+            formatted.append({
+                'title': subheading.get('title', ''),
+                'level': subheading.get('level', 2),
+                'keywords': subheading.get('keywords', []),
+                'summary': subheading.get('summary', '')
+            })
+        return formatted
+    
+    def _format_key_points(self, points: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
+        """Format and validate generated key points."""
+        formatted = []
+        for point in points:
+            formatted.append({
+                'point': point.get('point', ''),
+                'importance': point.get('importance', 'medium'),
+                'supporting_evidence': point.get('evidence', []),
+                'related_keywords': point.get('keywords', [])
+            })
+        return formatted
+    
+    def _format_content_flow(self, flow: Dict[str, Any]) -> Dict[str, Any]:
+        """Format and validate generated content flow."""
+        return {
+            'introduction': flow.get('introduction', {}),
+            'main_sections': flow.get('main_sections', []),
+            'conclusion': flow.get('conclusion', {}),
+            'transitions': flow.get('transitions', []),
+            'content_pacing': flow.get('pacing', {})
+        }
+
+    def generate_ai_suggestions(
+        self,
+        content_type: str,
+        topic: str,
+        audience: str,
+        goals: List[str],
+        tone: str,
+        length: str,
+        model_settings: Dict[str, Any],
+        style_preferences: List[str],
+        seo_preferences: Dict[str, Any],
+        platform_settings: Dict[str, Any],
+        platform: str
+    ) -> List[Dict[str, Any]]:
+        """
+        Generate AI content suggestions based on input parameters.
+        
+        Args:
+            content_type: Type of content to generate
+            topic: Main topic or subject
+            audience: Target audience
+            goals: List of content goals
+            tone: Desired tone
+            length: Content length
+            model_settings: AI model settings
+            style_preferences: Style preferences
+            seo_preferences: SEO preferences
+            platform_settings: Platform-specific settings
+            platform: Target platform
+            
+        Returns:
+            List of generated content suggestions
+        """
+        try:
+            self.logger.info(f"Generating AI suggestions for topic: {topic}")
+            
+            # Create a comprehensive prompt for content generation
+            prompt = f"""Generate content suggestions for the following parameters:
+
+Content Type: {content_type}
+Topic: {topic}
+Target Audience: {audience}
+Goals: {', '.join(goals)}
+Tone: {tone}
+Length: {length}
+
+Style Preferences:
+- Creativity Level: {model_settings['Creativity Level']}
+- Formality Level: {model_settings['Formality Level']}
+- Style Elements: {', '.join(style_preferences)}
+
+SEO Preferences:
+- Keyword Density: {seo_preferences['Keyword Density']}%
+- Internal Linking: {'Enabled' if seo_preferences['Internal Linking'] else 'Disabled'}
+- External Linking: {'Enabled' if seo_preferences['External Linking'] else 'Disabled'}
+
+Platform Settings:
+- Platform: {platform}
+- Platform-specific requirements: {', '.join(platform_settings)}
+
+Please generate 3 different content suggestions. Format your response as a valid JSON object with the following structure:
+{{
+    "suggestions": [
+        {{
+            "title": "string",
+            "introduction": "string",
+            "key_points": ["string"],
+            "main_sections": [
+                {{
+                    "title": "string",
+                    "content": "string",
+                    "engagement_elements": ["string"],
+                    "seo_elements": ["string"]
+                }}
+            ],
+            "conclusion": "string",
+            "seo_elements": ["string"],
+            "platform_optimizations": ["string"],
+            "engagement_strategies": ["string"],
+            "content_metrics": {{
+                "estimated_read_time": "string",
+                "word_count": "number",
+                "keyword_density": "number",
+                "engagement_score": "number"
+            }}
+        }}
+    ]
+}}
+
+IMPORTANT: Your response must be a valid JSON object. Do not include any text before or after the JSON object."""
+
+            # Define JSON structure for validation
+            json_struct = {
+                "type": "object",
+                "properties": {
+                    "suggestions": {
+                        "type": "array",
+                        "items": {
+                            "type": "object",
+                            "properties": {
+                                "title": {"type": "string"},
+                                "introduction": {"type": "string"},
+                                "key_points": {"type": "array", "items": {"type": "string"}},
+                                "main_sections": {
+                                    "type": "array",
+                                    "items": {
+                                        "type": "object",
+                                        "properties": {
+                                            "title": {"type": "string"},
+                                            "content": {"type": "string"},
+                                            "engagement_elements": {"type": "array", "items": {"type": "string"}},
+                                            "seo_elements": {"type": "array", "items": {"type": "string"}}
+                                        }
+                                    }
+                                },
+                                "conclusion": {"type": "string"},
+                                "seo_elements": {"type": "array", "items": {"type": "string"}},
+                                "platform_optimizations": {"type": "array", "items": {"type": "string"}},
+                                "engagement_strategies": {"type": "array", "items": {"type": "string"}},
+                                "content_metrics": {
+                                    "type": "object",
+                                    "properties": {
+                                        "estimated_read_time": {"type": "string"},
+                                        "word_count": {"type": "number"},
+                                        "keyword_density": {"type": "number"},
+                                        "engagement_score": {"type": "number"}
+                                    }
+                                }
+                            }
+                        }
+                    }
+                }
+            }
+            
+            # Generate content using llm_text_gen with JSON structure
+            generated_content = llm_text_gen(prompt, json_struct=json_struct)
+            
+            if not generated_content:
+                raise ValueError("Failed to generate content suggestions")
+            
+            # Parse the generated content
+            try:
+                # If generated_content is already a dict, use it directly
+                if isinstance(generated_content, dict):
+                    content_data = generated_content
+                else:
+                    # Try to parse as JSON string
+                    content_data = json.loads(generated_content)
+                
+                return self._format_suggestions(
+                    content_data,
+                    content_type,
+                    audience,
+                    goals,
+                    tone,
+                    length,
+                    model_settings,
+                    seo_preferences,
+                    platform
+                )
+                
+            except json.JSONDecodeError as e:
+                self.logger.error(f"Error parsing generated content: {str(e)}")
+                # Try to extract JSON from the response if it's wrapped in other text
+                try:
+                    # Find the first '{' and last '}'
+                    start = generated_content.find('{')
+                    end = generated_content.rfind('}') + 1
+                    if start >= 0 and end > start:
+                        json_str = generated_content[start:end]
+                        content_data = json.loads(json_str)
+                        return self._format_suggestions(
+                            content_data,
+                            content_type,
+                            audience,
+                            goals,
+                            tone,
+                            length,
+                            model_settings,
+                            seo_preferences,
+                            platform
+                        )
+                except Exception as e2:
+                    self.logger.error(f"Error extracting JSON from response: {str(e2)}")
+                    raise ValueError("Failed to parse generated content")
+            
+        except Exception as e:
+            self.logger.error(f"Error generating AI suggestions: {str(e)}", exc_info=True)
+            raise
+
+    def _format_suggestions(
+        self,
+        content_data: Dict[str, Any],
+        content_type: str,
+        audience: str,
+        goals: List[str],
+        tone: str,
+        length: str,
+        model_settings: Dict[str, Any],
+        seo_preferences: Dict[str, Any],
+        platform: str
+    ) -> List[Dict[str, Any]]:
+        """Format and process suggestions from content data."""
+        suggestions = []
+        for suggestion in content_data.get('suggestions', []):
+            formatted_suggestion = {
+                'title': suggestion.get('title', ''),
+                'type': content_type,
+                'platform': platform,
+                'audience': audience,
+                'impact': f"High impact for {', '.join(goals)}",
+                'preview': suggestion.get('introduction', ''),
+                'style_elements': [
+                    f"Tone: {tone}",
+                    f"Length: {length}",
+                    f"Creativity: {model_settings['Creativity Level']}",
+                    f"Formality: {model_settings['Formality Level']}"
+                ],
+                'seo_elements': [
+                    f"Keyword Density: {seo_preferences['Keyword Density']}%",
+                    "Internal Linking: Enabled" if seo_preferences['Internal Linking'] else "Internal Linking: Disabled",
+                    "External Linking: Enabled" if seo_preferences['External Linking'] else "External Linking: Disabled"
+                ],
+                'engagement_score': f"{85 + len(suggestions)*5}%",
+                'reach': 'High',
+                'conversion': f"{3.5 + len(suggestions)*0.5}%",
+                'seo_impact': 'Strong',
+                'platform_optimizations': suggestion.get('platform_optimizations', []),
+                'variations': [
+                    "Alternative headline",
+                    "Different content angle",
+                    "Alternative format"
+                ],
+                'seo_recommendations': suggestion.get('seo_elements', []),
+                'media_suggestions': [
+                    "Featured image",
+                    "Supporting graphics",
+                    "Social media visuals"
+                ]
+            }
+            suggestions.append(formatted_suggestion)
+        return suggestions 

lib/ai_seo_tools/content_calendar/core/calendar_manager.py

@@ -0,0 +1,196 @@
+from datetime import datetime, timedelta
+from typing import Dict, List, Any, Optional
+import logging
+import sys
+import json
+import os
+
+from ..integrations.seo_tools import SEOToolsIntegration
+from ..integrations.gap_analyzer import GapAnalyzerIntegration
+from ..models.calendar import Calendar, ContentItem
+from ..utils.date_utils import calculate_publish_dates
+from ..utils.error_handling import handle_calendar_error
+
+logging.basicConfig(
+    level=logging.DEBUG,
+    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
+    handlers=[
+        logging.StreamHandler(sys.stdout),
+        logging.FileHandler('content_calendar_debug.log', mode='a')
+    ]
+)
+logger = logging.getLogger(__name__)
+
+CALENDAR_JSON_PATH = "calendar_data.json"
+
+class CalendarManager:
+    """
+    Main calendar management system that coordinates content planning,
+    scheduling, and optimization.
+    """
+    
+    def __init__(self):
+        """Initialize calendar manager."""
+        self.logger = logging.getLogger('content_calendar.manager')
+        self.logger.info("Initializing CalendarManager")
+        
+        self.seo_tools = SEOToolsIntegration()
+        self.gap_analyzer = GapAnalyzerIntegration()
+        self._calendar: Optional[Calendar] = None
+        self.logger.info("CalendarManager initialized successfully")
+    
+    @handle_calendar_error
+    def create_calendar(
+        self,
+        start_date: datetime,
+        duration: str,  # 'weekly', 'monthly', 'quarterly'
+        platforms: List[str],
+        website_url: str
+    ) -> Calendar:
+        """
+        Create a new content calendar based on content gap analysis and SEO requirements.
+        
+        Args:
+            start_date: When the calendar should begin
+            duration: How long the calendar should span
+            platforms: List of platforms to create content for
+            website_url: URL of the website to analyze
+            
+        Returns:
+            Calendar object containing the content schedule
+        """
+        self.logger.info(f"Creating new calendar for {website_url}")
+        self.logger.debug(f"Parameters: start_date={start_date}, duration={duration}, platforms={platforms}")
+        
+        try:
+            # 1. Analyze content gaps
+            self.logger.info("Analyzing content gaps")
+            gap_analysis = self.gap_analyzer.analyze_gaps(website_url)
+            
+            # 2. Generate topics based on gaps
+            self.logger.info("Generating topics from gap analysis")
+            topics = self._generate_topics(gap_analysis, platforms)
+            
+            # 3. Calculate publish dates
+            self.logger.info("Calculating publish dates")
+            schedule = calculate_publish_dates(
+                topics=topics,
+                start_date=start_date,
+                duration=duration
+            )
+            
+            # 4. Create calendar
+            self.logger.info("Creating calendar object")
+            self._calendar = Calendar(
+                start_date=start_date,
+                duration=duration,
+                platforms=platforms,
+                schedule=schedule
+            )
+            
+            self.logger.info("Calendar created successfully")
+            return self._calendar
+            
+        except Exception as e:
+            self.logger.error(f"Error creating calendar: {str(e)}", exc_info=True)
+            raise
+    
+    def _generate_topics(
+        self,
+        gap_analysis: Dict[str, Any],
+        platforms: List[str]
+    ) -> List[ContentItem]:
+        """
+        Generate content topics based on gap analysis and platform requirements.
+        """
+        topics = []
+        
+        for gap in gap_analysis['gaps']:
+            # Generate topic using AI
+            topic = self._generate_topic_from_gap(gap, platforms)
+            
+            # Optimize for SEO
+            optimized_topic = self._optimize_topic(topic)
+            
+            topics.append(optimized_topic)
+        
+        return topics
+    
+    def _generate_topic_from_gap(
+        self,
+        gap: Dict[str, Any],
+        platforms: List[str]
+    ) -> ContentItem:
+        """
+        Generate a specific topic based on a content gap.
+        """
+        # Use existing AI tools to generate topic
+        topic_data = {
+            'title': self._generate_title(gap),
+            'description': self._generate_description(gap),
+            'keywords': gap.get('keywords', []),
+            'platforms': platforms,
+            'content_type': self._determine_content_type(gap, platforms)
+        }
+        
+        return ContentItem(**topic_data)
+    
+    def _optimize_topic(self, topic: ContentItem) -> ContentItem:
+        """
+        Optimize a topic for SEO using existing tools.
+        """
+        # Optimize title
+        topic.title = self.seo_tools.optimize_title(topic.title)
+        
+        # Generate meta description
+        topic.meta_description = self.seo_tools.generate_meta_description(
+            topic.description
+        )
+        
+        # Add structured data
+        topic.structured_data = self.seo_tools.generate_structured_data(
+            topic.content_type
+        )
+        
+        return topic
+    
+    def get_calendar(self) -> Optional[Calendar]:
+        """
+        Get the current calendar.
+        """
+        self.logger.debug("Getting current calendar")
+        return self._calendar
+    
+    def update_calendar(self, calendar: Calendar) -> None:
+        """
+        Update the current calendar.
+        """
+        self._calendar = calendar
+    
+    def export_calendar(self) -> Optional[Dict[str, Any]]:
+        """Export the current calendar."""
+        self.logger.info("Exporting calendar")
+        if not self._calendar:
+            self.logger.warning("No calendar to export")
+            return None
+        
+        try:
+            calendar_data = self._calendar.export()
+            self.logger.info("Calendar exported successfully")
+            return calendar_data
+        except Exception as e:
+            self.logger.error(f"Error exporting calendar: {str(e)}", exc_info=True)
+            return None
+
+    def save_calendar_to_json(self):
+        calendar = self.get_calendar()
+        if calendar:
+            with open(CALENDAR_JSON_PATH, "w") as f:
+                json.dump(calendar.to_dict(), f, indent=2, default=str)
+
+    def load_calendar_from_json(self):
+        from lib.ai_seo_tools.content_calendar.models.calendar import Calendar
+        if os.path.exists(CALENDAR_JSON_PATH):
+            with open(CALENDAR_JSON_PATH, "r") as f:
+                data = json.load(f)
+            self._calendar = Calendar.from_dict(data) 

lib/ai_seo_tools/content_calendar/core/content_brief.py

@@ -0,0 +1,151 @@
+from typing import Dict, List, Any, Optional
+import logging
+from pathlib import Path
+import sys
+
+# Add parent directory to path to import existing tools
+parent_dir = str(Path(__file__).parent.parent.parent.parent)
+if parent_dir not in sys.path:
+    sys.path.append(parent_dir)
+
+from lib.ai_seo_tools.content_calendar.models.calendar import ContentType, ContentItem, Platform
+from lib.ai_seo_tools.content_calendar.utils.error_handling import handle_calendar_error
+from lib.gpt_providers.text_generation.main_text_generation import llm_text_gen
+from lib.ai_seo_tools.content_gap_analysis.main import ContentGapAnalysis
+from lib.ai_seo_tools.content_title_generator import ai_title_generator
+from lib.ai_seo_tools.meta_desc_generator import metadesc_generator_main
+from .ai_generator import AIGenerator
+
+logger = logging.getLogger(__name__)
+
+class ContentBriefGenerator:
+    """
+    Generates comprehensive content briefs using AI-powered analysis.
+    """
+    
+    def __init__(self):
+        self.logger = logging.getLogger('content_calendar.content_brief')
+        self.logger.info("Initializing ContentBriefGenerator")
+        self._setup_logging()
+        self._load_ai_tools()
+    
+    def _setup_logging(self):
+        """Configure logging for content brief generator."""
+        logger.setLevel(logging.INFO)
+        handler = logging.StreamHandler()
+        formatter = logging.Formatter(
+            '%(asctime)s - %(name)s - %(levelname)s - %(message)s'
+        )
+        handler.setFormatter(formatter)
+        logger.addHandler(handler)
+    
+    def _load_ai_tools(self):
+        """Load and initialize AI tools."""
+        try:
+            # Initialize AI tools
+            self.gap_analyzer = ContentGapAnalysis()
+            self.title_generator = ai_title_generator
+            self.meta_generator = metadesc_generator_main
+            self.ai_generator = AIGenerator()
+            
+        except Exception as e:
+            logger.error(f"Error loading AI tools: {str(e)}")
+            raise
+    
+    @handle_calendar_error
+    def generate_brief(
+        self,
+        content_item: ContentItem,
+        target_audience: Optional[Dict[str, Any]] = None
+    ) -> Dict[str, Any]:
+        """
+        Generate a comprehensive content brief.
+        
+        Args:
+            content_item: Content item to generate brief for
+            target_audience: Optional target audience data
+            
+        Returns:
+            Dictionary containing the content brief
+        """
+        try:
+            logger.info(f"Generating content brief for: {content_item.title}")
+            
+            # Generate content outline
+            outline = self._generate_outline(content_item)
+            
+            # Generate key points
+            key_points = self.ai_generator.generate_key_points(
+                title=content_item.title,
+                content_type=content_item.content_type,
+                context=content_item.context
+            )
+            
+            # Generate content flow
+            content_flow = self.ai_generator.generate_content_flow(
+                title=content_item.title,
+                content_type=content_item.content_type,
+                outline=outline
+            )
+            
+            # Compile the brief
+            brief = {
+                'title': content_item.title,
+                'content_type': content_item.content_type.value,
+                'outline': outline,
+                'key_points': key_points,
+                'content_flow': content_flow,
+                'target_audience': target_audience or {},
+                'seo_data': content_item.seo_data,
+                'platform_specs': content_item.platform_specs
+            }
+            
+            logger.info("Content brief generated successfully")
+            return brief
+            
+        except Exception as e:
+            logger.error(f"Error generating content brief: {str(e)}")
+            raise
+    
+    def _generate_outline(
+        self,
+        content_item: ContentItem
+    ) -> Dict[str, Any]:
+        """
+        Generate content outline with headings and subheadings.
+        
+        Args:
+            content_item: Content item to generate outline for
+            
+        Returns:
+            Dictionary containing the content outline
+        """
+        try:
+            # Generate main headings
+            main_headings = self.ai_generator.generate_headings(
+                title=content_item.title,
+                content_type=content_item.content_type,
+                context=content_item.context
+            )
+            
+            # Generate subheadings for each main heading
+            subheadings = {}
+            for heading in main_headings:
+                heading_subheadings = self.ai_generator.generate_subheadings(
+                    main_heading=heading,
+                    content_type=content_item.content_type,
+                    context=content_item.context
+                )
+                subheadings[heading['title']] = heading_subheadings
+            
+            return {
+                'main_headings': main_headings,
+                'subheadings': subheadings
+            }
+            
+        except Exception as e:
+            logger.error(f"Error generating outline: {str(e)}")
+            return {
+                'main_headings': [],
+                'subheadings': {}
+            } 

lib/ai_seo_tools/content_calendar/core/content_generator.py

@@ -0,0 +1,323 @@
+from typing import Dict, List, Any, Optional
+import logging
+from pathlib import Path
+import sys
+
+# Add parent directory to path to import existing tools
+parent_dir = str(Path(__file__).parent.parent.parent.parent)
+if parent_dir not in sys.path:
+    sys.path.append(parent_dir)
+
+from ..models.calendar import ContentItem, ContentType
+from ..utils.error_handling import handle_calendar_error
+from lib.ai_seo_tools.content_gap_analysis.main import ContentGapAnalysis
+from lib.ai_seo_tools.content_title_generator import ai_title_generator
+from lib.ai_seo_tools.meta_desc_generator import metadesc_generator_main
+
+logger = logging.getLogger(__name__)
+
+class ContentGenerator:
+    """
+    AI-powered content generation for content briefs.
+    """
+    
+    def __init__(self):
+        self.logger = logging.getLogger('content_calendar.content_generator')
+        self.logger.info("Initializing ContentGenerator")
+        self._setup_logging()
+        self._load_ai_tools()
+    
+    def _setup_logging(self):
+        """Configure logging for content generator."""
+        logger.setLevel(logging.INFO)
+        handler = logging.StreamHandler()
+        formatter = logging.Formatter(
+            '%(asctime)s - %(name)s - %(levelname)s - %(message)s'
+        )
+        handler.setFormatter(formatter)
+        logger.addHandler(handler)
+    
+    def _load_ai_tools(self):
+        """Load and initialize AI tools."""
+        try:
+            # Initialize AI tools
+            self.gap_analyzer = ContentGapAnalysis()
+            self.title_generator = ai_title_generator
+            self.meta_generator = metadesc_generator_main
+            
+        except Exception as e:
+            logger.error(f"Error loading AI tools: {str(e)}")
+            raise
+    
+    @handle_calendar_error
+    def generate_headings(
+        self,
+        content_item: ContentItem,
+        context: Dict[str, Any]
+    ) -> List[Dict[str, Any]]:
+        """
+        Generate main headings for content.
+        
+        Args:
+            content_item: Content item to generate headings for
+            context: Content context from gap analysis
+            
+        Returns:
+            List of main headings with metadata
+        """
+        try:
+            # Use AI to generate headings based on content type and context
+            headings = self._generate_ai_headings(
+                title=content_item.title,
+                content_type=content_item.content_type,
+                context=context
+            )
+            
+            # Format and validate headings
+            formatted_headings = []
+            for heading in headings:
+                formatted_heading = {
+                    'title': heading['title'],
+                    'level': heading.get('level', 1),
+                    'keywords': heading.get('keywords', []),
+                    'summary': heading.get('summary', '')
+                }
+                formatted_headings.append(formatted_heading)
+            
+            return formatted_headings
+            
+        except Exception as e:
+            logger.error(f"Error generating headings: {str(e)}")
+            return []
+    
+    @handle_calendar_error
+    def generate_subheadings(
+        self,
+        content_item: ContentItem,
+        main_headings: List[Dict[str, Any]],
+        context: Dict[str, Any]
+    ) -> Dict[str, List[Dict[str, Any]]]:
+        """
+        Generate subheadings for each main heading.
+        
+        Args:
+            content_item: Content item to generate subheadings for
+            main_headings: List of main headings
+            context: Content context from gap analysis
+            
+        Returns:
+            Dictionary mapping main headings to their subheadings
+        """
+        try:
+            subheadings = {}
+            
+            for heading in main_headings:
+                # Generate subheadings for each main heading
+                heading_subheadings = self._generate_ai_subheadings(
+                    main_heading=heading,
+                    content_type=content_item.content_type,
+                    context=context
+                )
+                
+                # Format and validate subheadings
+                formatted_subheadings = []
+                for subheading in heading_subheadings:
+                    formatted_subheading = {
+                        'title': subheading['title'],
+                        'level': subheading.get('level', 2),
+                        'keywords': subheading.get('keywords', []),
+                        'summary': subheading.get('summary', '')
+                    }
+                    formatted_subheadings.append(formatted_subheading)
+                
+                subheadings[heading['title']] = formatted_subheadings
+            
+            return subheadings
+            
+        except Exception as e:
+            logger.error(f"Error generating subheadings: {str(e)}")
+            return {}
+    
+    @handle_calendar_error
+    def generate_key_points(
+        self,
+        content_item: ContentItem,
+        context: Dict[str, Any]
+    ) -> List[Dict[str, Any]]:
+        """
+        Generate key points for the content.
+        
+        Args:
+            content_item: Content item to generate key points for
+            context: Content context from gap analysis
+            
+        Returns:
+            List of key points with supporting information
+        """
+        try:
+            # Generate key points using AI
+            key_points = self._generate_ai_key_points(
+                title=content_item.title,
+                content_type=content_item.content_type,
+                context=context
+            )
+            
+            # Format and validate key points
+            formatted_points = []
+            for point in key_points:
+                formatted_point = {
+                    'point': point['point'],
+                    'importance': point.get('importance', 'medium'),
+                    'supporting_evidence': point.get('evidence', []),
+                    'related_keywords': point.get('keywords', [])
+                }
+                formatted_points.append(formatted_point)
+            
+            return formatted_points
+            
+        except Exception as e:
+            logger.error(f"Error generating key points: {str(e)}")
+            return []
+    
+    @handle_calendar_error
+    def generate_content_flow(
+        self,
+        content_item: ContentItem,
+        outline: Dict[str, Any]
+    ) -> Dict[str, Any]:
+        """
+        Generate content flow and structure.
+        
+        Args:
+            content_item: Content item to generate flow for
+            outline: Content outline with headings and key points
+            
+        Returns:
+            Dictionary containing content flow and structure
+        """
+        try:
+            # Generate content flow using AI
+            flow = self._generate_ai_content_flow(
+                title=content_item.title,
+                content_type=content_item.content_type,
+                outline=outline
+            )
+            
+            return {
+                'introduction': flow.get('introduction', {}),
+                'main_sections': flow.get('main_sections', []),
+                'conclusion': flow.get('conclusion', {}),
+                'transitions': flow.get('transitions', []),
+                'content_pacing': flow.get('pacing', {})
+            }
+            
+        except Exception as e:
+            logger.error(f"Error generating content flow: {str(e)}")
+            return {}
+    
+    def _generate_ai_headings(
+        self,
+        title: str,
+        content_type: ContentType,
+        context: Dict[str, Any]
+    ) -> List[Dict[str, Any]]:
+        """
+        Use AI to generate content headings.
+        """
+        # TODO: Implement AI heading generation
+        # This would use the existing AI tools to generate headings
+        return []
+    
+    def _generate_ai_subheadings(
+        self,
+        main_heading: Dict[str, Any],
+        content_type: ContentType,
+        context: Dict[str, Any]
+    ) -> List[Dict[str, Any]]:
+        """
+        Use AI to generate subheadings.
+        """
+        # TODO: Implement AI subheading generation
+        return []
+    
+    def _generate_ai_key_points(
+        self,
+        title: str,
+        content_type: ContentType,
+        context: Dict[str, Any]
+    ) -> List[Dict[str, Any]]:
+        """
+        Use AI to generate key points.
+        """
+        # TODO: Implement AI key point generation
+        return []
+    
+    def _generate_ai_content_flow(
+        self,
+        title: str,
+        content_type: ContentType,
+        outline: Dict[str, Any]
+    ) -> Dict[str, Any]:
+        """
+        Use AI to generate content flow.
+        """
+        # TODO: Implement AI content flow generation
+        return {}
+    
+    def generate_variation(self, content: Dict[str, Any], variation_type: str) -> Dict[str, Any]:
+        """Generate a variation of the given content.
+        
+        Args:
+            content: Original content to vary
+            variation_type: Type of variation to generate ('tone', 'length', 'style', etc.)
+            
+        Returns:
+            Dictionary containing the varied content
+        """
+        try:
+            self.logger.info(f"Generating {variation_type} variation for content")
+            
+            # Generate variation based on type
+            variation = {
+                'title': f"{content.get('title', '')} - {variation_type.title()} Variation",
+                'content_flow': {
+                    'introduction': {
+                        'summary': f"Varied introduction for {content.get('title', '')}",
+                        'key_points': [
+                            f"Varied key point 1 for {variation_type}",
+                            f"Varied key point 2 for {variation_type}",
+                            f"Varied key point 3 for {variation_type}"
+                        ]
+                    },
+                    'main_content': {
+                        'sections': [
+                            {
+                                'title': f"Varied Section 1: {variation_type.title()} Approach",
+                                'content': f"Varied content for {variation_type}",
+                                'subsections': []
+                            },
+                            {
+                                'title': f"Varied Section 2: {variation_type.title()} Details",
+                                'content': "Varied details and information",
+                                'subsections': []
+                            }
+                        ]
+                    },
+                    'conclusion': {
+                        'summary': f"Varied conclusion for {variation_type}",
+                        'call_to_action': "Varied call to action"
+                    }
+                },
+                'metadata': {
+                    'variation_type': variation_type,
+                    'original_content': content.get('title', ''),
+                    'platform': content.get('metadata', {}).get('platform', 'Unknown'),
+                    'content_type': content.get('metadata', {}).get('content_type', 'Unknown')
+                }
+            }
+            
+            return variation
+            
+        except Exception as e:
+            self.logger.error(f"Error generating variation: {str(e)}")
+            return {} 

lib/ai_seo_tools/content_calendar/examples/calendar_usage.py

@@ -0,0 +1,80 @@
+from datetime import datetime
+from typing import List, Dict, Any
+
+from ..core.calendar_manager import CalendarManager
+from ..models.calendar import ContentType, Platform
+
+def create_content_calendar(
+    website_url: str,
+    start_date: datetime,
+    duration: str,
+    platforms: List[str]
+) -> Dict[str, Any]:
+    """
+    Example of creating a content calendar.
+    
+    Args:
+        website_url: URL of the website to analyze
+        start_date: When to start the calendar
+        duration: How long the calendar should span
+        platforms: List of platforms to create content for
+        
+    Returns:
+        Dictionary containing the calendar data
+    """
+    # Initialize calendar manager
+    calendar_manager = CalendarManager()
+    
+    # Create calendar
+    calendar = calendar_manager.create_calendar(
+        start_date=start_date,
+        duration=duration,
+        platforms=platforms,
+        website_url=website_url
+    )
+    
+    # Export calendar
+    calendar_data = calendar_manager.export_calendar()
+    
+    return calendar_data
+
+def main():
+    """Example usage of the content calendar system."""
+    # Example parameters
+    website_url = "https://example.com"
+    start_date = datetime.now()
+    duration = "monthly"
+    platforms = [
+        Platform.WEBSITE.value,
+        Platform.FACEBOOK.value,
+        Platform.TWITTER.value,
+        Platform.LINKEDIN.value
+    ]
+    
+    try:
+        # Create calendar
+        calendar_data = create_content_calendar(
+            website_url=website_url,
+            start_date=start_date,
+            duration=duration,
+            platforms=platforms
+        )
+        
+        # Print calendar summary
+        print("\nContent Calendar Summary:")
+        print(f"Duration: {calendar_data['duration']}")
+        print(f"Platforms: {', '.join(calendar_data['platforms'])}")
+        print("\nScheduled Content:")
+        
+        for date, items in calendar_data['schedule'].items():
+            print(f"\n{date}:")
+            for item in items:
+                print(f"- {item['title']} ({item['content_type']})")
+                print(f"  Platforms: {', '.join(item['platforms'])}")
+                print(f"  Status: {item['status']}")
+        
+    except Exception as e:
+        print(f"Error creating calendar: {str(e)}")
+
+if __name__ == "__main__":
+    main() 

lib/ai_seo_tools/content_calendar/examples/generate_content_brief.py

@@ -0,0 +1,138 @@
+from datetime import datetime
+from typing import Dict, Any
+
+from ..models.calendar import ContentItem, ContentType, Platform, SEOData
+from ..core.content_brief import ContentBriefGenerator
+
+def create_content_brief(
+    title: str,
+    content_type: ContentType,
+    platforms: list[Platform],
+    website_url: str,
+    target_audience: Dict[str, Any]
+) -> Dict[str, Any]:
+    """
+    Create a content brief for the given content.
+    
+    Args:
+        title: Content title
+        content_type: Type of content
+        platforms: List of platforms to publish on
+        website_url: Website URL for context
+        target_audience: Target audience information
+        
+    Returns:
+        Dictionary containing the content brief
+    """
+    # Create content item
+    content_item = ContentItem(
+        id=f"content-{datetime.now().strftime('%Y%m%d%H%M%S')}",
+        title=title,
+        description=f"Content brief for {title}",
+        content_type=content_type,
+        platforms=platforms,
+        publish_date=datetime.now(),
+        seo_data=SEOData(
+            keywords=[],  # Will be generated by SEO tools
+            meta_description="",  # Will be generated by SEO tools
+            structured_data={}
+        ),
+        platform_specs={},  # Will be generated based on platforms
+        context={
+            "website_url": website_url,
+            "target_audience": target_audience.get("demographics", {}).get("profession", ""),
+            "content_goals": ["educate", "generate leads"]
+        }
+    )
+    
+    # Initialize content brief generator
+    generator = ContentBriefGenerator()
+    
+    # Generate brief
+    brief = generator.generate_brief(
+        content_item=content_item,
+        target_audience=target_audience
+    )
+    
+    return brief
+
+def main():
+    """Example usage of content brief generation."""
+    # Example content details
+    title = "10 Ways to Improve Your SEO Strategy"
+    content_type = ContentType.BLOG_POST
+    platforms = [Platform.WEBSITE, Platform.LINKEDIN]
+    website_url = "https://example.com"
+    
+    # Example target audience
+    target_audience = {
+        "demographics": {
+            "age_range": "25-45",
+            "profession": "digital marketers",
+            "experience_level": "intermediate"
+        },
+        "interests": [
+            "SEO",
+            "content marketing",
+            "digital strategy",
+            "search engine optimization"
+        ],
+        "pain_points": [
+            "low search rankings",
+            "poor content performance",
+            "lack of organic traffic",
+            "difficulty in keyword research"
+        ],
+        "goals": [
+            "improve search rankings",
+            "increase organic traffic",
+            "generate more leads",
+            "build brand authority"
+        ]
+    }
+    
+    try:
+        # Generate content brief
+        brief = create_content_brief(
+            title=title,
+            content_type=content_type,
+            platforms=platforms,
+            website_url=website_url,
+            target_audience=target_audience
+        )
+        
+        # Print brief summary
+        print("\nContent Brief Summary:")
+        print(f"Title: {brief['title']}")
+        print(f"Content Type: {brief['content_type']}")
+        
+        print("\nOutline:")
+        for heading in brief['outline']['main_headings']:
+            print(f"\n- {heading['title']}")
+            print(f"  Keywords: {', '.join(heading['keywords'])}")
+            print(f"  Summary: {heading['summary']}")
+            
+            # Print subheadings
+            subheadings = brief['outline']['subheadings'].get(heading['title'], [])
+            for subheading in subheadings:
+                print(f"  - {subheading['title']}")
+                print(f"    Keywords: {', '.join(subheading['keywords'])}")
+        
+        print("\nKey Points:")
+        for point in brief['key_points']:
+            print(f"\n- {point['point']}")
+            print(f"  Importance: {point['importance']}")
+            print(f"  Evidence: {', '.join(point['supporting_evidence'])}")
+        
+        print("\nContent Flow:")
+        flow = brief['content_flow']
+        print(f"Introduction: {flow['introduction'].get('summary', '')}")
+        print(f"Main Sections: {len(flow['main_sections'])} sections")
+        print(f"Conclusion: {flow['conclusion'].get('summary', '')}")
+        print(f"Transitions: {len(flow['transitions'])} transition points")
+        
+    except Exception as e:
+        print(f"Error generating content brief: {str(e)}")
+
+if __name__ == '__main__':
+    main() 

lib/ai_seo_tools/content_calendar/examples/integration_example.py

@@ -0,0 +1,196 @@
+import logging
+from datetime import datetime, timedelta
+from typing import Dict, Any, List
+
+from ..integrations.integration_manager import IntegrationManager
+
+# Set up logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+def create_cross_platform_content(
+    title: str,
+    content: str,
+    platforms: List[str],
+    content_type: str,
+    target_audience: Dict[str, Any],
+    industry: str,
+    keywords: List[str]
+) -> Dict[str, Any]:
+    """Create and optimize content for multiple platforms."""
+    try:
+        # Initialize integration manager
+        integration_manager = IntegrationManager()
+        
+        # Prepare content item
+        content_item = {
+            'title': title,
+            'content': content,
+            'content_type': content_type,
+            'keywords': keywords,
+            'target_audience': target_audience,
+            'industry': industry
+        }
+        
+        # Get platform suggestions
+        suggestions = integration_manager.get_platform_suggestions(
+            content=content_item,
+            platforms=platforms
+        )
+        
+        # Validate content for each platform
+        validation_results = {}
+        for platform in platforms:
+            validation = integration_manager.validate_platform_content(
+                content=content_item,
+                platform=platform
+            )
+            validation_results[platform] = validation
+        
+        # Optimize content for each platform
+        optimized_content = integration_manager.optimize_cross_platform_content(
+            content=content_item,
+            platforms=platforms
+        )
+        
+        return {
+            'original_content': content_item,
+            'platform_suggestions': suggestions,
+            'validation_results': validation_results,
+            'optimized_content': optimized_content
+        }
+        
+    except Exception as e:
+        logger.error(f"Error creating cross-platform content: {str(e)}")
+        return {
+            'error': str(e)
+        }
+
+def create_content_calendar(
+    start_date: datetime,
+    end_date: datetime,
+    platforms: List[str],
+    content_types: List[str],
+    target_audience: Dict[str, Any],
+    industry: str,
+    keywords: List[str]
+) -> Dict[str, Any]:
+    """Create a cross-platform content calendar."""
+    try:
+        # Initialize integration manager
+        integration_manager = IntegrationManager()
+        
+        # Create calendar
+        calendar = integration_manager.create_cross_platform_calendar(
+            start_date=start_date,
+            end_date=end_date,
+            platforms=platforms,
+            content_types=content_types,
+            target_audience=target_audience,
+            industry=industry,
+            keywords=keywords
+        )
+        
+        return calendar
+        
+    except Exception as e:
+        logger.error(f"Error creating content calendar: {str(e)}")
+        return {
+            'error': str(e)
+        }
+
+def main():
+    """Main function to demonstrate integration manager usage."""
+    # Example content details
+    title = "The Future of AI in Content Marketing"
+    content = """
+    Artificial Intelligence is revolutionizing the way we approach content marketing.
+    From automated content generation to personalized recommendations, AI tools are
+    helping marketers create more engaging and effective content strategies.
+    
+    Key points:
+    1. AI-powered content generation
+    2. Personalized content recommendations
+    3. Automated content optimization
+    4. Data-driven content strategy
+    5. Future trends in AI marketing
+    """
+    
+    # Platform and content settings
+    platforms = ['instagram', 'twitter', 'linkedin', 'blog', 'facebook']
+    content_type = 'article'
+    target_audience = {
+        'age_range': '25-34',
+        'interests': ['technology', 'marketing', 'AI'],
+        'location': 'global',
+        'profession': 'marketing professionals'
+    }
+    industry = 'technology'
+    keywords = ['AI', 'content marketing', 'automation', 'personalization']
+    
+    # Create cross-platform content
+    logger.info("Creating cross-platform content...")
+    content_result = create_cross_platform_content(
+        title=title,
+        content=content,
+        platforms=platforms,
+        content_type=content_type,
+        target_audience=target_audience,
+        industry=industry,
+        keywords=keywords
+    )
+    
+    # Print content results
+    logger.info("\nCross-Platform Content Results:")
+    logger.info("===============================")
+    
+    # Print platform suggestions
+    logger.info("\nPlatform Suggestions:")
+    for platform, suggestions in content_result['platform_suggestions'].items():
+        logger.info(f"\n{platform.upper()}:")
+        for key, value in suggestions.items():
+            logger.info(f"  {key}: {value}")
+    
+    # Print validation results
+    logger.info("\nValidation Results:")
+    for platform, validation in content_result['validation_results'].items():
+        logger.info(f"\n{platform.upper()}:")
+        logger.info(f"  Valid: {validation['is_valid']}")
+        if not validation['is_valid']:
+            logger.info(f"  Error: {validation.get('error', 'N/A')}")
+    
+    # Print optimized content
+    logger.info("\nOptimized Content:")
+    for platform, optimized in content_result['optimized_content'].items():
+        logger.info(f"\n{platform.upper()}:")
+        for key, value in optimized.items():
+            logger.info(f"  {key}: {value}")
+    
+    # Create content calendar
+    logger.info("\nCreating content calendar...")
+    start_date = datetime.now()
+    end_date = start_date + timedelta(days=30)
+    calendar_result = create_content_calendar(
+        start_date=start_date,
+        end_date=end_date,
+        platforms=platforms,
+        content_types=[content_type],
+        target_audience=target_audience,
+        industry=industry,
+        keywords=keywords
+    )
+    
+    # Print calendar results
+    logger.info("\nContent Calendar Results:")
+    logger.info("========================")
+    
+    # Print platform calendars
+    logger.info("\nPlatform Calendars:")
+    for platform, calendar in calendar_result['platform_calendars'].items():
+        logger.info(f"\n{platform.upper()}:")
+        logger.info(f"  Content Items: {len(calendar['content_items'])}")
+        for item in calendar['content_items']:
+            logger.info(f"  - {item['original_item']['title']}")
+
+if __name__ == '__main__':
+    main() 

lib/ai_seo_tools/content_calendar/examples/platform_adaptation_example.py

@@ -0,0 +1,142 @@
+from typing import Dict, Any
+from datetime import datetime
+
+from ..integrations.platform_adapters import UnifiedPlatformAdapter
+
+def create_platform_content(
+    title: str,
+    content: str,
+    platforms: list,
+    context: Dict[str, Any] = None
+) -> Dict[str, Any]:
+    """
+    Create platform-specific content using the UnifiedPlatformAdapter.
+    
+    Args:
+        title: The title of the content
+        content: The main content to be adapted
+        platforms: List of platforms to adapt content for
+        context: Additional context for content adaptation
+    
+    Returns:
+        Dict containing adapted content for each platform
+    """
+    # Initialize the platform adapter
+    adapter = UnifiedPlatformAdapter()
+    
+    # Prepare base content
+    base_content = {
+        'title': title,
+        'content': content,
+        'keywords': ['content', 'marketing', 'social media'],
+        'tone': 'professional',
+        'cta': 'Learn More',
+        'audience': 'For All',
+        'language': 'English',
+        'industry': 'technology',
+        'word_count': 1000
+    }
+    
+    # Adapt content for each platform
+    adapted_content = {}
+    for platform in platforms:
+        try:
+            platform_content = adapter.adapt_content(
+                content=base_content,
+                platform=platform,
+                context=context
+            )
+            adapted_content[platform] = platform_content
+        except Exception as e:
+            print(f"Error adapting content for {platform}: {str(e)}")
+            adapted_content[platform] = {'error': str(e)}
+    
+    return adapted_content
+
+def main():
+    """Example usage of platform content adaptation."""
+    # Example content
+    title = "The Future of AI in Content Marketing"
+    content = """
+    Artificial Intelligence is revolutionizing content marketing in unprecedented ways.
+    From automated content generation to personalized user experiences, AI is becoming
+    an indispensable tool for marketers. This article explores the latest trends and
+    innovations in AI-powered content marketing.
+    """
+    
+    # Example context
+    context = {
+        'target_audience': 'marketing professionals',
+        'campaign_goals': ['awareness', 'engagement', 'lead generation'],
+        'brand_voice': 'authoritative yet approachable',
+        'content_theme': 'technology and innovation'
+    }
+    
+    # Platforms to adapt content for
+    platforms = ['instagram', 'twitter', 'linkedin', 'blog', 'facebook']
+    
+    # Generate platform-specific content
+    adapted_content = create_platform_content(
+        title=title,
+        content=content,
+        platforms=platforms,
+        context=context
+    )
+    
+    # Print results
+    print("\nPlatform-Specific Content Adaptation Results:")
+    print("=" * 50)
+    
+    for platform, content in adapted_content.items():
+        print(f"\n{platform.upper()} Content:")
+        print("-" * 30)
+        
+        if 'error' in content:
+            print(f"Error: {content['error']}")
+            continue
+        
+        # Print platform-specific content
+        if platform == 'instagram':
+            print("\nCaptions:")
+            for caption in content['captions']:
+                print(f"- {caption}")
+            print("\nHashtags:")
+            print(content['hashtags'])
+        
+        elif platform == 'twitter':
+            print("\nTweets:")
+            for tweet in content['tweets']:
+                print(f"- {tweet}")
+            print("\nThread Structure:")
+            print(content['thread_structure'])
+        
+        elif platform == 'linkedin':
+            print("\nPost:")
+            print(content['post'])
+            print("\nEngagement Optimization:")
+            print(content['engagement_optimization'])
+        
+        elif platform == 'blog':
+            print("\nPost:")
+            print(content['post'])
+            print("\nSEO Optimization:")
+            print(content['seo_optimization'])
+        
+        elif platform == 'facebook':
+            print("\nPost:")
+            print(content['post'])
+            print("\nEngagement Optimization:")
+            print(content['engagement_optimization'])
+        
+        # Print media suggestions
+        print("\nMedia Suggestions:")
+        for media in content['media_suggestions']:
+            print(f"- {media['type']}: {media['description']}")
+        
+        # Print platform-specific recommendations
+        print("\nPlatform-Specific Recommendations:")
+        for key, value in content['platform_specific'].items():
+            print(f"- {key}: {value}")
+
+if __name__ == '__main__':
+    main() 

lib/ai_seo_tools/content_calendar/integrations/gap_analyzer.py

@@ -0,0 +1,127 @@
+"""
+Gap analyzer integration for content calendar.
+"""
+
+import streamlit as st
+from typing import Dict, Any, List, Optional
+from loguru import logger
+from lib.utils.website_analyzer.analyzer import WebsiteAnalyzer
+from lib.ai_seo_tools.content_gap_analysis.main import ContentGapAnalysis
+import asyncio
+import sys
+import os
+import json
+from datetime import datetime
+
+# Configure logger for content calendar debugging
+logger.remove()  # Remove default handler
+logger.add(
+    sys.stdout,
+    level="DEBUG",
+    format="<green>{time:YYYY-MM-DD HH:mm:ss}</green> | <level>{level: <8}</level> | <cyan>{name}</cyan> | <yellow>{function}</yellow> | {message}",
+    filter=lambda record: "content_calendar" in record["name"].lower()
+)
+
+class GapAnalyzerIntegration:
+    """Integrates content gap analysis with content calendar."""
+    
+    def __init__(self):
+        """Initialize the gap analyzer integration."""
+        self.gap_analyzer = ContentGapAnalysis()
+        logger.debug("GapAnalyzerIntegration initialized for content calendar")
+    
+    def analyze_gaps(self, data: Dict[str, Any]) -> Dict[str, Any]:
+        """
+        Analyze content gaps.
+        
+        Args:
+            data: Dictionary containing content data
+            
+        Returns:
+            Dictionary containing gap analysis results
+        """
+        try:
+            logger.debug(f"Starting gap analysis with data: {json.dumps(data, indent=2)}")
+            # Run gap analysis
+            results = self.gap_analyzer.analyze(data)
+            logger.debug(f"Gap analysis completed with results: {json.dumps(results, indent=2)}")
+            return results
+            
+        except Exception as e:
+            error_msg = f"Error analyzing content gaps: {str(e)}"
+            logger.error(error_msg, exc_info=True)
+            return {
+                'error': error_msg,
+                'gaps': [],
+                'recommendations': []
+            }
+    
+    def get_topic_suggestions(
+        self,
+        gap_analysis: Dict[str, Any],
+        platform: str,
+        count: int = 5
+    ) -> List[Dict[str, Any]]:
+        """
+        Get topic suggestions for a specific platform based on gap analysis.
+        
+        Args:
+            gap_analysis: Results from gap analysis
+            platform: Target platform for content
+            count: Number of suggestions to generate
+            
+        Returns:
+            List of topic suggestions
+        """
+        try:
+            logger.debug(f"Generating topic suggestions for platform: {platform}, count: {count}")
+            suggestions = []
+            
+            for gap in gap_analysis.get('processed_gaps', []):
+                # Generate platform-specific topics
+                platform_topics = self.ai_processor.generate_platform_topics(
+                    gap=gap,
+                    platform=platform,
+                    count=count
+                )
+                logger.debug(f"Generated topics for gap: {json.dumps(platform_topics, indent=2)}")
+                suggestions.extend(platform_topics)
+            
+            logger.debug(f"Total suggestions generated: {len(suggestions)}")
+            return suggestions
+            
+        except Exception as e:
+            logger.error(f"Error generating topic suggestions: {str(e)}")
+            return []
+    
+    def analyze_topic_relevance(
+        self,
+        topic: Dict[str, Any],
+        gap_analysis: Dict[str, Any]
+    ) -> Dict[str, Any]:
+        """
+        Analyze how well a topic addresses content gaps.
+        
+        Args:
+            topic: Topic to analyze
+            gap_analysis: Results from gap analysis
+            
+        Returns:
+            Dictionary containing relevance analysis
+        """
+        try:
+            logger.debug(f"Analyzing topic relevance: {json.dumps(topic, indent=2)}")
+            relevance = self.ai_processor.analyze_topic_relevance(
+                topic=topic,
+                gaps=gap_analysis.get('gaps', [])
+            )
+            
+            logger.debug(f"Topic relevance analysis completed: {json.dumps(relevance, indent=2)}")
+            return relevance
+            
+        except Exception as e:
+            logger.error(f"Error analyzing topic relevance: {str(e)}")
+            return {
+                'error': str(e),
+                'score': 0
+            } 

lib/ai_seo_tools/content_calendar/integrations/integration_manager.py

@@ -0,0 +1,196 @@
+import logging
+from typing import Dict, List, Any, Optional
+from datetime import datetime, timedelta
+
+from ..core.calendar_manager import CalendarManager
+from ..core.content_brief import ContentBriefGenerator
+from .platform_adapters import UnifiedPlatformAdapter
+
+logger = logging.getLogger(__name__)
+
+class IntegrationManager:
+    """Manages integration between content calendar and platform adapters."""
+    
+    def __init__(self):
+        """Initialize the integration manager."""
+        self.calendar_manager = CalendarManager()
+        self.content_brief_generator = ContentBriefGenerator()
+        self.platform_adapter = UnifiedPlatformAdapter()
+        
+    def create_cross_platform_calendar(
+        self,
+        start_date: datetime,
+        end_date: datetime,
+        platforms: List[str],
+        content_types: List[str],
+        target_audience: Optional[Dict[str, Any]] = None,
+        industry: Optional[str] = None,
+        keywords: Optional[List[str]] = None
+    ) -> Dict[str, Any]:
+        """Create a cross-platform content calendar."""
+        try:
+            # Generate base calendar
+            calendar = self.calendar_manager.create_calendar(
+                start_date=start_date,
+                end_date=end_date,
+                content_types=content_types,
+                target_audience=target_audience,
+                industry=industry,
+                keywords=keywords
+            )
+            
+            # Adapt content for each platform
+            platform_calendars = {}
+            for platform in platforms:
+                platform_calendars[platform] = self._adapt_calendar_for_platform(
+                    calendar=calendar,
+                    platform=platform
+                )
+            
+            return {
+                'base_calendar': calendar,
+                'platform_calendars': platform_calendars,
+                'metadata': {
+                    'start_date': start_date,
+                    'end_date': end_date,
+                    'platforms': platforms,
+                    'content_types': content_types,
+                    'industry': industry,
+                    'keywords': keywords
+                }
+            }
+            
+        except Exception as e:
+            logger.error(f"Error creating cross-platform calendar: {str(e)}")
+            raise
+    
+    def _adapt_calendar_for_platform(
+        self,
+        calendar: Dict[str, Any],
+        platform: str
+    ) -> Dict[str, Any]:
+        """Adapt calendar content for a specific platform."""
+        try:
+            adapted_calendar = {
+                'platform': platform,
+                'content_items': [],
+                'metadata': calendar.get('metadata', {})
+            }
+            
+            # Adapt each content item
+            for item in calendar.get('content_items', []):
+                adapted_item = self._adapt_content_item(item, platform)
+                if adapted_item:
+                    adapted_calendar['content_items'].append(adapted_item)
+            
+            return adapted_calendar
+            
+        except Exception as e:
+            logger.error(f"Error adapting calendar for platform {platform}: {str(e)}")
+            return {
+                'platform': platform,
+                'content_items': [],
+                'error': str(e)
+            }
+    
+    def _adapt_content_item(
+        self,
+        item: Dict[str, Any],
+        platform: str
+    ) -> Optional[Dict[str, Any]]:
+        """Adapt a content item for a specific platform."""
+        try:
+            # Generate content brief if not exists
+            if 'brief' not in item:
+                item['brief'] = self.content_brief_generator.generate_brief(item)
+            
+            # Adapt content for platform
+            adapted_content = self.platform_adapter.adapt_content(
+                content=item,
+                platform=platform
+            )
+            
+            if adapted_content:
+                return {
+                    'original_item': item,
+                    'adapted_content': adapted_content,
+                    'platform_specifics': self.platform_adapter.get_platform_specs(platform)
+                }
+            
+            return None
+            
+        except Exception as e:
+            logger.error(f"Error adapting content item for platform {platform}: {str(e)}")
+            return None
+    
+    def get_platform_suggestions(
+        self,
+        content: Dict[str, Any],
+        platforms: List[str]
+    ) -> Dict[str, Any]:
+        """Get platform-specific suggestions for content."""
+        try:
+            suggestions = {}
+            
+            for platform in platforms:
+                platform_suggestions = self.platform_adapter.get_platform_suggestions(
+                    content=content,
+                    platform=platform
+                )
+                if platform_suggestions:
+                    suggestions[platform] = platform_suggestions
+            
+            return suggestions
+            
+        except Exception as e:
+            logger.error(f"Error getting platform suggestions: {str(e)}")
+            return {}
+    
+    def validate_platform_content(
+        self,
+        content: Dict[str, Any],
+        platform: str
+    ) -> Dict[str, Any]:
+        """Validate content for a specific platform."""
+        try:
+            validation_result = self.platform_adapter.validate_content(
+                content=content,
+                platform=platform
+            )
+            
+            return {
+                'platform': platform,
+                'is_valid': validation_result,
+                'specifications': self.platform_adapter.get_platform_specs(platform)
+            }
+            
+        except Exception as e:
+            logger.error(f"Error validating platform content: {str(e)}")
+            return {
+                'platform': platform,
+                'is_valid': False,
+                'error': str(e)
+            }
+    
+    def optimize_cross_platform_content(
+        self,
+        content: Dict[str, Any],
+        platforms: List[str]
+    ) -> Dict[str, Any]:
+        """Optimize content for multiple platforms."""
+        try:
+            optimized_content = {}
+            
+            for platform in platforms:
+                platform_optimized = self.platform_adapter.optimize_content(
+                    content=content,
+                    platform=platform
+                )
+                if platform_optimized:
+                    optimized_content[platform] = platform_optimized
+            
+            return optimized_content
+            
+        except Exception as e:
+            logger.error(f"Error optimizing cross-platform content: {str(e)}")
+            return {} 

lib/ai_seo_tools/content_calendar/integrations/platform_adapters.py

@@ -0,0 +1,137 @@
+"""
+Platform adapters for content calendar.
+"""
+
+import streamlit as st
+from typing import Dict, Any, List, Optional
+from loguru import logger
+from lib.utils.website_analyzer.analyzer import WebsiteAnalyzer
+from lib.ai_seo_tools.content_gap_analysis.main import ContentGapAnalysis
+from lib.ai_seo_tools.content_title_generator import ai_title_generator
+from lib.ai_seo_tools.meta_desc_generator import metadesc_generator_main
+from lib.ai_seo_tools.seo_structured_data import ai_structured_data
+import asyncio
+import sys
+import os
+import json
+
+# Configure logger
+logger.remove()  # Remove default handler
+logger.add(
+    "logs/platform_adapters.log",
+    rotation="50 MB",
+    retention="10 days",
+    level="DEBUG",
+    format="{time:YYYY-MM-DD HH:mm:ss} | {level} | {message}"
+)
+logger.add(
+    sys.stdout,
+    level="INFO",
+    format="<green>{time:YYYY-MM-DD HH:mm:ss}</green> | <level>{level: <8}</level> | <cyan>{message}</cyan>"
+)
+
+# Ensure logs directory exists
+os.makedirs("logs", exist_ok=True)
+
+class UnifiedPlatformAdapter:
+    """Unified adapter for different social media platforms."""
+    
+    def __init__(self):
+        """Initialize the platform adapter."""
+        self.platform_handlers = {
+            'instagram': self._handle_instagram,
+            'linkedin': self._handle_linkedin,
+            'twitter': self._handle_twitter,
+            'facebook': self._handle_facebook
+        }
+        logger.info("UnifiedPlatformAdapter initialized")
+    
+    def generate_content(self, platform: str, data: Dict[str, Any]) -> Dict[str, Any]:
+        """
+        Generate content for a specific platform.
+        
+        Args:
+            platform: Target platform
+            data: Content data
+            
+        Returns:
+            Dictionary containing generated content
+        """
+        try:
+            handler = self.platform_handlers.get(platform.lower())
+            if not handler:
+                raise ValueError(f"Unsupported platform: {platform}")
+            
+            return handler(data)
+            
+        except Exception as e:
+            error_msg = f"Error generating content for {platform}: {str(e)}"
+            logger.error(error_msg, exc_info=True)
+            return {
+                'error': error_msg,
+                'content': None
+            }
+    
+    def _handle_instagram(self, data: Dict[str, Any]) -> Dict[str, Any]:
+        """Handle Instagram content generation."""
+        try:
+            # Use content title generator for Instagram captions
+            caption = ai_title_generator(data)
+            return {
+                'platform': 'instagram',
+                'content': caption
+            }
+        except Exception as e:
+            logger.error(f"Error generating Instagram content: {str(e)}")
+            return {
+                'platform': 'instagram',
+                'error': str(e)
+            }
+    
+    def _handle_linkedin(self, data: Dict[str, Any]) -> Dict[str, Any]:
+        """Handle LinkedIn content generation."""
+        try:
+            # Use meta description generator for LinkedIn posts
+            post = metadesc_generator_main(data)
+            return {
+                'platform': 'linkedin',
+                'content': post
+            }
+        except Exception as e:
+            logger.error(f"Error generating LinkedIn content: {str(e)}")
+            return {
+                'platform': 'linkedin',
+                'error': str(e)
+            }
+    
+    def _handle_twitter(self, data: Dict[str, Any]) -> Dict[str, Any]:
+        """Handle Twitter content generation."""
+        try:
+            # Use content title generator for tweets
+            tweet = ai_title_generator(data)
+            return {
+                'platform': 'twitter',
+                'content': tweet
+            }
+        except Exception as e:
+            logger.error(f"Error generating Twitter content: {str(e)}")
+            return {
+                'platform': 'twitter',
+                'error': str(e)
+            }
+    
+    def _handle_facebook(self, data: Dict[str, Any]) -> Dict[str, Any]:
+        """Handle Facebook content generation."""
+        try:
+            # Use meta description generator for Facebook posts
+            post = metadesc_generator_main(data)
+            return {
+                'platform': 'facebook',
+                'content': post
+            }
+        except Exception as e:
+            logger.error(f"Error generating Facebook content: {str(e)}")
+            return {
+                'platform': 'facebook',
+                'error': str(e)
+            } 

lib/ai_seo_tools/content_calendar/integrations/seo_optimizer.py

@@ -0,0 +1,219 @@
+import logging
+from typing import Dict, Any, List, Optional
+from datetime import datetime
+
+from ...meta_desc_generator import generate_blog_metadesc
+from ...content_title_generator import generate_blog_titles
+from ...seo_structured_data import generate_json_data
+
+logger = logging.getLogger(__name__)
+
+class SEOOptimizer:
+    """Integrates SEO tools with content calendar system."""
+    
+    def __init__(self):
+        """Initialize the SEO optimizer."""
+        self._setup_logging()
+    
+    def _setup_logging(self):
+        """Configure logging for SEO optimizer."""
+        logger.setLevel(logging.INFO)
+        handler = logging.StreamHandler()
+        formatter = logging.Formatter(
+            '%(asctime)s - %(name)s - %(levelname)s - %(message)s'
+        )
+        handler.setFormatter(formatter)
+        logger.addHandler(handler)
+    
+    def optimize_content(
+        self,
+        content: Dict[str, Any],
+        content_type: str = 'article',
+        language: str = 'English',
+        search_intent: str = 'Informational Intent'
+    ) -> Dict[str, Any]:
+        """
+        Optimize content for SEO using existing tools.
+        
+        Args:
+            content: Content to optimize
+            content_type: Type of content (article, product, etc.)
+            language: Content language
+            search_intent: Search intent type
+            
+        Returns:
+            Optimized content with SEO elements
+        """
+        try:
+            # Extract content details
+            title = content.get('title', '')
+            keywords = content.get('keywords', [])
+            content_text = content.get('content', '')
+            
+            # Generate SEO elements
+            optimized_title = self._optimize_title(
+                title=title,
+                keywords=keywords,
+                content_type=content_type,
+                language=language,
+                search_intent=search_intent
+            )
+            
+            meta_description = self._generate_meta_description(
+                keywords=keywords,
+                content_type=content_type,
+                language=language,
+                search_intent=search_intent
+            )
+            
+            structured_data = self._generate_structured_data(
+                content=content,
+                content_type=content_type
+            )
+            
+            return {
+                'original_content': content,
+                'seo_optimized': {
+                    'title': optimized_title,
+                    'meta_description': meta_description,
+                    'structured_data': structured_data,
+                    'keywords': keywords,
+                    'content_type': content_type,
+                    'language': language,
+                    'search_intent': search_intent
+                }
+            }
+            
+        except Exception as e:
+            logger.error(f"Error optimizing content: {str(e)}")
+            return {
+                'error': str(e)
+            }
+    
+    def _optimize_title(
+        self,
+        title: str,
+        keywords: List[str],
+        content_type: str,
+        language: str,
+        search_intent: str
+    ) -> List[str]:
+        """Generate SEO-optimized titles."""
+        try:
+            # Convert keywords list to comma-separated string
+            keywords_str = ', '.join(keywords)
+            
+            # Generate titles using existing tool
+            titles = generate_blog_titles(
+                input_blog_keywords=keywords_str,
+                input_blog_content=title,
+                input_title_type=content_type,
+                input_title_intent=search_intent,
+                input_language=language
+            )
+            
+            return titles.split('\n') if titles else []
+            
+        except Exception as e:
+            logger.error(f"Error optimizing title: {str(e)}")
+            return []
+    
+    def _generate_meta_description(
+        self,
+        keywords: List[str],
+        content_type: str,
+        language: str,
+        search_intent: str
+    ) -> List[str]:
+        """Generate SEO-optimized meta descriptions."""
+        try:
+            # Convert keywords list to comma-separated string
+            keywords_str = ', '.join(keywords)
+            
+            # Generate meta descriptions using existing tool
+            descriptions = generate_blog_metadesc(
+                keywords=keywords_str,
+                tone='Informative',
+                search_type=search_intent,
+                language=language
+            )
+            
+            return descriptions.split('\n') if descriptions else []
+            
+        except Exception as e:
+            logger.error(f"Error generating meta description: {str(e)}")
+            return []
+    
+    def _generate_structured_data(
+        self,
+        content: Dict[str, Any],
+        content_type: str
+    ) -> Optional[Dict[str, Any]]:
+        """Generate structured data for content."""
+        try:
+            # Prepare content details for structured data
+            details = {
+                'Headline': content.get('title', ''),
+                'Author': content.get('author', ''),
+                'Date Published': content.get('publish_date', datetime.now().isoformat()),
+                'Keywords': ', '.join(content.get('keywords', [])),
+                'Description': content.get('description', ''),
+                'Image URL': content.get('image_url', '')
+            }
+            
+            # Generate structured data using existing tool
+            structured_data = generate_json_data(
+                content_type=content_type,
+                details=details,
+                url=content.get('url', '')
+            )
+            
+            return structured_data
+            
+        except Exception as e:
+            logger.error(f"Error generating structured data: {str(e)}")
+            return None
+    
+    def optimize_calendar_content(
+        self,
+        calendar: Dict[str, Any],
+        content_type: str = 'article',
+        language: str = 'English',
+        search_intent: str = 'Informational Intent'
+    ) -> Dict[str, Any]:
+        """
+        Optimize all content in calendar for SEO.
+        
+        Args:
+            calendar: Content calendar to optimize
+            content_type: Type of content
+            language: Content language
+            search_intent: Search intent type
+            
+        Returns:
+            Calendar with SEO-optimized content
+        """
+        try:
+            optimized_calendar = {
+                'metadata': calendar.get('metadata', {}),
+                'content_items': []
+            }
+            
+            # Optimize each content item
+            for item in calendar.get('content_items', []):
+                optimized_item = self.optimize_content(
+                    content=item,
+                    content_type=content_type,
+                    language=language,
+                    search_intent=search_intent
+                )
+                if optimized_item:
+                    optimized_calendar['content_items'].append(optimized_item)
+            
+            return optimized_calendar
+            
+        except Exception as e:
+            logger.error(f"Error optimizing calendar content: {str(e)}")
+            return {
+                'error': str(e)
+            } 

lib/ai_seo_tools/content_calendar/integrations/seo_tools.py

@@ -0,0 +1,143 @@
+"""SEO tools integration for content calendar."""
+
+import streamlit as st
+from loguru import logger
+from typing import Dict, Any, List, Optional
+import asyncio
+import sys
+import os
+from lib.ai_seo_tools.content_title_generator import ai_title_generator
+from lib.utils.website_analyzer.analyzer import WebsiteAnalyzer
+from lib.gpt_providers.text_generation.main_text_generation import llm_text_gen
+
+# Configure logger
+logger.remove()  # Remove default handler
+logger.add(
+    "logs/seo_tools_integration.log",
+    rotation="50 MB",
+    retention="10 days",
+    level="DEBUG",
+    format="{time:YYYY-MM-DD HH:mm:ss} | {level} | {message}"
+)
+logger.add(
+    sys.stdout,
+    level="INFO",
+    format="<green>{time:YYYY-MM-DD HH:mm:ss}</green> | <level>{level: <8}</level> | <cyan>{message}</cyan>"
+)
+
+# Ensure logs directory exists
+os.makedirs("logs", exist_ok=True)
+
+class SEOToolsIntegration:
+    """Integration with SEO tools for content calendar."""
+    
+    def __init__(self):
+        """Initialize the SEO tools integration."""
+        self.website_analyzer = WebsiteAnalyzer()
+        logger.info("SEOToolsIntegration initialized")
+    
+    def analyze_content(self, url: str) -> Dict[str, Any]:
+        """
+        Analyze content for SEO optimization.
+        
+        Args:
+            url: The URL to analyze
+            
+        Returns:
+            Dictionary containing SEO analysis results
+        """
+        try:
+            # Analyze website
+            analysis = self.website_analyzer.analyze_website(url)
+            if not analysis.get('success', False):
+                return {
+                    'error': analysis.get('error', 'Unknown error in analysis'),
+                    'seo_score': 0,
+                    'recommendations': []
+                }
+            
+            # Extract SEO information
+            seo_info = analysis['data']['analysis']['seo_info']
+            
+            return {
+                'seo_score': seo_info.get('overall_score', 0),
+                'meta_tags': seo_info.get('meta_tags', {}),
+                'content': seo_info.get('content', {}),
+                'recommendations': seo_info.get('recommendations', [])
+            }
+            
+        except Exception as e:
+            error_msg = f"Error analyzing content: {str(e)}"
+            logger.error(error_msg, exc_info=True)
+            return {
+                'error': error_msg,
+                'seo_score': 0,
+                'recommendations': []
+            }
+    
+    def generate_title(self, url: str) -> Dict[str, Any]:
+        """
+        Generate SEO-optimized title.
+        
+        Args:
+            url: The URL to analyze
+            
+        Returns:
+            Dictionary containing title suggestions
+        """
+        return ai_title_generator(url)
+    
+    def optimize_content(self, content: str, keywords: List[str]) -> Dict[str, Any]:
+        """
+        Optimize content for SEO.
+        
+        Args:
+            content: The content to optimize
+            keywords: List of target keywords
+            
+        Returns:
+            Dictionary containing optimization suggestions
+        """
+        try:
+            # Prepare prompt for content optimization
+            prompt = f"""Optimize the following content for SEO:
+            
+            Content: {content}
+            Target Keywords: {', '.join(keywords)}
+            
+            Provide optimization suggestions for:
+            1. Keyword usage and placement
+            2. Content structure and readability
+            3. Meta information
+            4. Internal linking opportunities
+            5. Content length and depth
+            
+            Format the response as JSON with 'suggestions' and 'score' keys."""
+            
+            # Get AI optimization suggestions
+            suggestions = llm_text_gen(
+                prompt=prompt,
+                system_prompt="You are an SEO expert specializing in content optimization.",
+                response_format="json_object"
+            )
+            
+            if not suggestions:
+                return {
+                    'error': 'Failed to generate optimization suggestions',
+                    'suggestions': [],
+                    'score': 0
+                }
+            
+            return {
+                'suggestions': suggestions.get('suggestions', []),
+                'score': suggestions.get('score', 0)
+            }
+            
+        except Exception as e:
+            error_msg = f"Error optimizing content: {str(e)}"
+            logger.error(error_msg, exc_info=True)
+            return {
+                'error': error_msg,
+                'suggestions': [],
+                'score': 0
+            } 

lib/ai_seo_tools/content_calendar/models/calendar.py

@@ -0,0 +1,237 @@
+import logging
+import sys
+
+logging.basicConfig(
+    level=logging.DEBUG,
+    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
+    handlers=[
+        logging.StreamHandler(sys.stdout),
+        logging.FileHandler('content_calendar_debug.log', mode='a')
+    ]
+)
+logger = logging.getLogger(__name__)
+
+from datetime import datetime
+from typing import Dict, List, Any, Optional
+from dataclasses import dataclass, field
+from enum import Enum
+import pandas as pd
+
+class ContentType(Enum):
+    """Types of content that can be scheduled."""
+    BLOG_POST = "blog_post"
+    SOCIAL_MEDIA = "social_media"
+    VIDEO = "video"
+    PODCAST = "podcast"
+    NEWSLETTER = "newsletter"
+    LANDING_PAGE = "landing_page"
+
+class Platform(Enum):
+    """Supported content platforms."""
+    WEBSITE = "website"
+    FACEBOOK = "facebook"
+    TWITTER = "twitter"
+    LINKEDIN = "linkedin"
+    INSTAGRAM = "instagram"
+    YOUTUBE = "youtube"
+    MEDIUM = "medium"
+
+@dataclass
+class SEOData:
+    """SEO-related data for content."""
+    title: str
+    meta_description: str
+    keywords: List[str]
+    structured_data: Dict[str, Any]
+    canonical_url: Optional[str] = None
+    og_tags: Optional[Dict[str, str]] = None
+    twitter_cards: Optional[Dict[str, str]] = None
+
+    @staticmethod
+    def from_dict(data):
+        return SEOData(
+            title=data.get('title', ''),
+            meta_description=data.get('meta_description', ''),
+            keywords=data.get('keywords', []),
+            structured_data=data.get('structured_data', {}),
+            canonical_url=data.get('canonical_url'),
+            og_tags=data.get('og_tags'),
+            twitter_cards=data.get('twitter_cards')
+        )
+
+@dataclass
+class ContentItem:
+    """Represents a single content item in the calendar."""
+    title: str
+    description: str
+    content_type: ContentType
+    platforms: List[Platform]
+    publish_date: datetime
+    seo_data: SEOData
+    status: str = "draft"
+    author: Optional[str] = None
+    tags: List[str] = field(default_factory=list)
+    notes: Optional[str] = None
+    
+    def to_dict(self) -> Dict[str, Any]:
+        """Convert content item to dictionary."""
+        return {
+            'title': self.title,
+            'description': self.description,
+            'content_type': self.content_type.value,
+            'platforms': [p.value for p in self.platforms],
+            'publish_date': self.publish_date.isoformat(),
+            'seo_data': {
+                'title': self.seo_data.title,
+                'meta_description': self.seo_data.meta_description,
+                'keywords': self.seo_data.keywords,
+                'structured_data': self.seo_data.structured_data,
+                'canonical_url': self.seo_data.canonical_url,
+                'og_tags': self.seo_data.og_tags,
+                'twitter_cards': self.seo_data.twitter_cards
+            },
+            'status': self.status,
+            'author': self.author,
+            'tags': self.tags,
+            'notes': self.notes
+        }
+
+    @staticmethod
+    def from_dict(data):
+        from .calendar import ContentType, Platform, SEOData
+        return ContentItem(
+            title=data['title'],
+            description=data.get('description', ''),
+            content_type=ContentType(data['content_type']),
+            platforms=[Platform(p) for p in data['platforms']],
+            publish_date=pd.to_datetime(data['publish_date']),
+            seo_data=SEOData.from_dict(data.get('seo_data', {})),
+            status=data.get('status', 'draft'),
+            author=data.get('author'),
+            tags=data.get('tags', []),
+            notes=data.get('notes')
+        )
+
+@dataclass
+class Calendar:
+    """Represents a content calendar."""
+    start_date: datetime
+    duration: str  # 'weekly', 'monthly', 'quarterly'
+    platforms: List[Platform]
+    schedule: Dict[str, List[ContentItem]]
+    name: Optional[str] = None
+    description: Optional[str] = None
+    
+    def __init__(self, start_date: datetime, duration: str, platforms: List[Platform], 
+                 schedule: Dict[str, List[ContentItem]], name: Optional[str] = None, 
+                 description: Optional[str] = None):
+        """Initialize a new calendar.
+        
+        Args:
+            start_date: Start date of the calendar
+            duration: Duration of the calendar ('weekly', 'monthly', 'quarterly')
+            platforms: List of platforms to schedule content for
+            schedule: Dictionary mapping dates to content items
+            name: Optional name for the calendar
+            description: Optional description of the calendar
+        """
+        self.start_date = start_date
+        self.duration = duration
+        self.platforms = platforms
+        self.schedule = schedule
+        self.name = name
+        self.description = description
+        self.content_items: List[ContentItem] = []
+        self.logger = logging.getLogger('content_calendar.calendar')
+        
+        # Initialize content_items from schedule
+        for items in self.schedule.values():
+            self.content_items.extend(items)
+    
+    def get_all_content(self) -> List[ContentItem]:
+        """Get all content items in the calendar.
+        
+        Returns:
+            List of all ContentItem objects in the calendar
+        """
+        try:
+            self.logger.debug(f"Getting all content items. Count: {len(self.content_items)}")
+            return self.content_items
+        except Exception as e:
+            self.logger.error(f"Error getting all content: {str(e)}")
+            return []
+    
+    def to_dict(self) -> Dict[str, Any]:
+        """Convert calendar to dictionary."""
+        return {
+            'name': self.name,
+            'description': self.description,
+            'start_date': self.start_date.isoformat(),
+            'duration': self.duration,
+            'platforms': [p.value for p in self.platforms],
+            'schedule': {
+                date: [item.to_dict() for item in items]
+                for date, items in self.schedule.items()
+            }
+        }
+    
+    def export(self, format: str = 'json') -> Dict[str, Any]:
+        """
+        Export calendar in specified format.
+        Currently only supports JSON format.
+        """
+        if format.lower() != 'json':
+            raise ValueError(f"Unsupported export format: {format}")
+        
+        return self.to_dict()
+    
+    def get_content_for_date(self, date: datetime) -> List[ContentItem]:
+        """Get all content items scheduled for a specific date."""
+        date_str = date.strftime('%Y-%m-%d')
+        return self.schedule.get(date_str, [])
+    
+    def get_content_for_platform(
+        self,
+        platform: Platform
+    ) -> List[ContentItem]:
+        """Get all content items for a specific platform."""
+        all_content = []
+        for items in self.schedule.values():
+            platform_content = [
+                item for item in items
+                if platform in item.platforms
+            ]
+            all_content.extend(platform_content)
+        return all_content
+    
+    def add_content(self, content: ContentItem) -> None:
+        """Add a new content item to the calendar."""
+        date_str = content.publish_date.strftime('%Y-%m-%d')
+        if date_str not in self.schedule:
+            self.schedule[date_str] = []
+        self.schedule[date_str].append(content)
+    
+    def remove_content(self, content: ContentItem) -> None:
+        """Remove a content item from the calendar."""
+        date_str = content.publish_date.strftime('%Y-%m-%d')
+        if date_str in self.schedule:
+            self.schedule[date_str] = [
+                item for item in self.schedule[date_str]
+                if item != content
+            ]
+
+    @staticmethod
+    def from_dict(data):
+        from .calendar import ContentItem, Platform
+        schedule = {
+            date: [ContentItem.from_dict(item) for item in items]
+            for date, items in data.get('schedule', {}).items()
+        }
+        return Calendar(
+            start_date=pd.to_datetime(data['start_date']),
+            duration=data['duration'],
+            platforms=[Platform(p) for p in data['platforms']],
+            schedule=schedule,
+            name=data.get('name'),
+            description=data.get('description')
+        ) 

lib/ai_seo_tools/content_calendar/tests/test_ai_generator.py

@@ -0,0 +1,185 @@
+import unittest
+from typing import Dict, Any
+
+from ..models.calendar import ContentType
+from ..core.ai_generator import AIContentGenerator
+
+class TestAIContentGenerator(unittest.TestCase):
+    """Test cases for AIContentGenerator."""
+    
+    def setUp(self):
+        """Set up test cases."""
+        self.generator = AIContentGenerator()
+        self.test_title = "10 Ways to Improve Your SEO Strategy"
+        self.test_content_type = ContentType.BLOG_POST
+        self.test_context = {
+            "website_url": "https://example.com",
+            "target_audience": "digital marketers",
+            "content_goals": ["educate", "generate leads"]
+        }
+    
+    def test_generate_headings(self):
+        """Test heading generation."""
+        headings = self.generator.generate_headings(
+            title=self.test_title,
+            content_type=self.test_content_type,
+            context=self.test_context
+        )
+        
+        self.assertIsInstance(headings, list)
+        for heading in headings:
+            self.assertIn('title', heading)
+            self.assertIn('level', heading)
+            self.assertIn('keywords', heading)
+            self.assertIn('summary', heading)
+            
+            # Verify heading level
+            self.assertEqual(heading['level'], 1)
+            
+            # Verify heading content
+            self.assertIsInstance(heading['title'], str)
+            self.assertIsInstance(heading['keywords'], list)
+            self.assertIsInstance(heading['summary'], str)
+    
+    def test_generate_subheadings(self):
+        """Test subheading generation."""
+        main_heading = {
+            'title': 'Understanding SEO Basics',
+            'level': 1,
+            'keywords': ['SEO', 'basics', 'fundamentals'],
+            'summary': 'Introduction to core SEO concepts'
+        }
+        
+        subheadings = self.generator.generate_subheadings(
+            main_heading=main_heading,
+            content_type=self.test_content_type,
+            context=self.test_context
+        )
+        
+        self.assertIsInstance(subheadings, list)
+        for subheading in subheadings:
+            self.assertIn('title', subheading)
+            self.assertIn('level', subheading)
+            self.assertIn('keywords', subheading)
+            self.assertIn('summary', subheading)
+            
+            # Verify subheading level
+            self.assertEqual(subheading['level'], 2)
+            
+            # Verify subheading content
+            self.assertIsInstance(subheading['title'], str)
+            self.assertIsInstance(subheading['keywords'], list)
+            self.assertIsInstance(subheading['summary'], str)
+    
+    def test_generate_key_points(self):
+        """Test key points generation."""
+        key_points = self.generator.generate_key_points(
+            title=self.test_title,
+            content_type=self.test_content_type,
+            context=self.test_context
+        )
+        
+        self.assertIsInstance(key_points, list)
+        for point in key_points:
+            self.assertIn('point', point)
+            self.assertIn('importance', point)
+            self.assertIn('supporting_evidence', point)
+            self.assertIn('related_keywords', point)
+            
+            # Verify point content
+            self.assertIsInstance(point['point'], str)
+            self.assertIn(point['importance'], ['high', 'medium', 'low'])
+            self.assertIsInstance(point['supporting_evidence'], list)
+            self.assertIsInstance(point['related_keywords'], list)
+    
+    def test_generate_content_flow(self):
+        """Test content flow generation."""
+        outline = {
+            'main_headings': [
+                {
+                    'title': 'Introduction',
+                    'level': 1,
+                    'keywords': ['SEO', 'introduction'],
+                    'summary': 'Overview of SEO importance'
+                }
+            ],
+            'subheadings': {
+                'Introduction': [
+                    {
+                        'title': 'What is SEO?',
+                        'level': 2,
+                        'keywords': ['definition', 'basics'],
+                        'summary': 'Basic definition of SEO'
+                    }
+                ]
+            }
+        }
+        
+        flow = self.generator.generate_content_flow(
+            title=self.test_title,
+            content_type=self.test_content_type,
+            outline=outline
+        )
+        
+        self.assertIsInstance(flow, dict)
+        self.assertIn('introduction', flow)
+        self.assertIn('main_sections', flow)
+        self.assertIn('conclusion', flow)
+        self.assertIn('transitions', flow)
+        self.assertIn('content_pacing', flow)
+        
+        # Verify flow content
+        self.assertIsInstance(flow['introduction'], dict)
+        self.assertIsInstance(flow['main_sections'], list)
+        self.assertIsInstance(flow['conclusion'], dict)
+        self.assertIsInstance(flow['transitions'], list)
+        self.assertIsInstance(flow['content_pacing'], dict)
+    
+    def test_prompt_creation(self):
+        """Test prompt creation methods."""
+        # Test heading prompt
+        heading_prompt = self.generator._create_heading_prompt(
+            title=self.test_title,
+            content_type=self.test_content_type,
+            gaps={'opportunities': ['keyword research', 'content optimization']}
+        )
+        self.assertIsInstance(heading_prompt, str)
+        self.assertIn(self.test_title, heading_prompt)
+        self.assertIn(self.test_content_type.value, heading_prompt)
+        
+        # Test subheading prompt
+        main_heading = {
+            'title': 'Understanding SEO Basics',
+            'level': 1,
+            'keywords': ['SEO', 'basics'],
+            'summary': 'Introduction to SEO'
+        }
+        subheading_prompt = self.generator._create_subheading_prompt(
+            main_heading=main_heading,
+            content_type=self.test_content_type,
+            context=self.test_context
+        )
+        self.assertIsInstance(subheading_prompt, str)
+        self.assertIn(main_heading['title'], subheading_prompt)
+        
+        # Test key points prompt
+        key_points_prompt = self.generator._create_key_points_prompt(
+            title=self.test_title,
+            content_type=self.test_content_type,
+            seo_data={'keywords': ['SEO', 'strategy']},
+            context=self.test_context
+        )
+        self.assertIsInstance(key_points_prompt, str)
+        self.assertIn(self.test_title, key_points_prompt)
+        
+        # Test flow prompt
+        flow_prompt = self.generator._create_flow_prompt(
+            title=self.test_title,
+            content_type=self.test_content_type,
+            outline={'main_headings': []}
+        )
+        self.assertIsInstance(flow_prompt, str)
+        self.assertIn(self.test_title, flow_prompt)
+
+if __name__ == '__main__':
+    unittest.main() 

lib/ai_seo_tools/content_calendar/tests/test_content_brief.py

@@ -0,0 +1,132 @@
+import unittest
+from datetime import datetime
+from typing import Dict, Any
+
+from ..models.calendar import ContentItem, ContentType, Platform, SEOData
+from ..core.content_brief import ContentBriefGenerator
+
+class TestContentBriefGenerator(unittest.TestCase):
+    """Test cases for ContentBriefGenerator."""
+    
+    def setUp(self):
+        """Set up test cases."""
+        self.generator = ContentBriefGenerator()
+        self.test_content_item = self._create_test_content_item()
+    
+    def _create_test_content_item(self) -> ContentItem:
+        """Create a test content item."""
+        return ContentItem(
+            id="test-001",
+            title="10 Ways to Improve Your SEO Strategy",
+            description="A comprehensive guide to enhancing your website's SEO performance",
+            content_type=ContentType.BLOG_POST,
+            platforms=[Platform.WEBSITE, Platform.LINKEDIN],
+            publish_date=datetime.now(),
+            seo_data=SEOData(
+                keywords=["SEO", "search engine optimization", "digital marketing"],
+                meta_description="Learn effective SEO strategies to boost your website's visibility",
+                structured_data={}
+            ),
+            platform_specs={
+                "website": {
+                    "format": "blog post",
+                    "min_length": 1500
+                },
+                "linkedin": {
+                    "format": "article",
+                    "min_length": 800
+                }
+            },
+            context={
+                "website_url": "https://example.com",
+                "target_audience": "digital marketers",
+                "content_goals": ["educate", "generate leads"]
+            }
+        )
+    
+    def test_generate_brief(self):
+        """Test content brief generation."""
+        # Generate brief
+        brief = self.generator.generate_brief(
+            content_item=self.test_content_item,
+            target_audience={
+                "demographics": {
+                    "age_range": "25-45",
+                    "profession": "digital marketers"
+                },
+                "interests": ["SEO", "content marketing", "digital strategy"],
+                "pain_points": [
+                    "low search rankings",
+                    "poor content performance",
+                    "lack of organic traffic"
+                ]
+            }
+        )
+        
+        # Verify brief structure
+        self.assertIsInstance(brief, dict)
+        self.assertIn('title', brief)
+        self.assertIn('content_type', brief)
+        self.assertIn('outline', brief)
+        self.assertIn('key_points', brief)
+        self.assertIn('content_flow', brief)
+        self.assertIn('target_audience', brief)
+        self.assertIn('seo_data', brief)
+        self.assertIn('platform_specs', brief)
+        
+        # Verify outline structure
+        outline = brief['outline']
+        self.assertIn('main_headings', outline)
+        self.assertIn('subheadings', outline)
+        
+        # Verify key points
+        self.assertIsInstance(brief['key_points'], list)
+        
+        # Verify content flow
+        flow = brief['content_flow']
+        self.assertIn('introduction', flow)
+        self.assertIn('main_sections', flow)
+        self.assertIn('conclusion', flow)
+        self.assertIn('transitions', flow)
+        self.assertIn('content_pacing', flow)
+    
+    def test_generate_brief_without_audience(self):
+        """Test content brief generation without target audience data."""
+        brief = self.generator.generate_brief(
+            content_item=self.test_content_item
+        )
+        
+        self.assertIsInstance(brief, dict)
+        self.assertIn('target_audience', brief)
+        self.assertEqual(brief['target_audience'], {})
+    
+    def test_generate_outline(self):
+        """Test outline generation."""
+        outline = self.generator._generate_outline(self.test_content_item)
+        
+        self.assertIsInstance(outline, dict)
+        self.assertIn('main_headings', outline)
+        self.assertIn('subheadings', outline)
+        
+        # Verify main headings
+        main_headings = outline['main_headings']
+        self.assertIsInstance(main_headings, list)
+        for heading in main_headings:
+            self.assertIn('title', heading)
+            self.assertIn('level', heading)
+            self.assertIn('keywords', heading)
+            self.assertIn('summary', heading)
+        
+        # Verify subheadings
+        subheadings = outline['subheadings']
+        self.assertIsInstance(subheadings, dict)
+        for heading_title, heading_subheadings in subheadings.items():
+            self.assertIsInstance(heading_subheadings, list)
+            for subheading in heading_subheadings:
+                self.assertIn('title', subheading)
+                self.assertIn('level', subheading)
+                self.assertIn('keywords', subheading)
+                self.assertIn('summary', subheading)
+
+if __name__ == '__main__':
+    unittest.main() 

lib/ai_seo_tools/content_calendar/tests/test_integration_manager.py

@@ -0,0 +1,171 @@
+import unittest
+from datetime import datetime, timedelta
+from typing import Dict, Any
+
+from ..integrations.integration_manager import IntegrationManager
+
+class TestIntegrationManager(unittest.TestCase):
+    """Test cases for the IntegrationManager class."""
+    
+    def setUp(self):
+        """Set up test fixtures."""
+        self.integration_manager = IntegrationManager()
+        self.start_date = datetime.now()
+        self.end_date = self.start_date + timedelta(days=30)
+        self.platforms = ['instagram', 'twitter', 'linkedin', 'blog', 'facebook']
+        self.content_types = ['article', 'social', 'video']
+        self.target_audience = {
+            'age_range': '25-34',
+            'interests': ['technology', 'marketing'],
+            'location': 'global'
+        }
+        self.industry = 'technology'
+        self.keywords = ['AI', 'content marketing', 'social media']
+        
+        # Sample content item
+        self.sample_content = {
+            'title': 'The Future of AI in Content Marketing',
+            'content': 'AI is revolutionizing content marketing...',
+            'content_type': 'article',
+            'keywords': ['AI', 'content marketing', 'automation'],
+            'target_audience': self.target_audience,
+            'industry': self.industry
+        }
+    
+    def test_create_cross_platform_calendar(self):
+        """Test creating a cross-platform content calendar."""
+        calendar = self.integration_manager.create_cross_platform_calendar(
+            start_date=self.start_date,
+            end_date=self.end_date,
+            platforms=self.platforms,
+            content_types=self.content_types,
+            target_audience=self.target_audience,
+            industry=self.industry,
+            keywords=self.keywords
+        )
+        
+        # Check basic structure
+        self.assertIn('base_calendar', calendar)
+        self.assertIn('platform_calendars', calendar)
+        self.assertIn('metadata', calendar)
+        
+        # Check platform calendars
+        platform_calendars = calendar['platform_calendars']
+        self.assertEqual(len(platform_calendars), len(self.platforms))
+        
+        for platform in self.platforms:
+            self.assertIn(platform, platform_calendars)
+            platform_calendar = platform_calendars[platform]
+            self.assertIn('content_items', platform_calendar)
+            self.assertIn('metadata', platform_calendar)
+    
+    def test_adapt_calendar_for_platform(self):
+        """Test adapting calendar for a specific platform."""
+        # Create base calendar
+        calendar = self.integration_manager.create_cross_platform_calendar(
+            start_date=self.start_date,
+            end_date=self.end_date,
+            platforms=[self.platforms[0]],  # Test with just Instagram
+            content_types=self.content_types,
+            target_audience=self.target_audience,
+            industry=self.industry,
+            keywords=self.keywords
+        )
+        
+        # Get platform calendar
+        platform_calendar = calendar['platform_calendars'][self.platforms[0]]
+        
+        # Check structure
+        self.assertIn('content_items', platform_calendar)
+        self.assertIn('metadata', platform_calendar)
+        
+        # Check content items
+        for item in platform_calendar['content_items']:
+            self.assertIn('original_item', item)
+            self.assertIn('adapted_content', item)
+            self.assertIn('platform_specifics', item)
+    
+    def test_adapt_content_item(self):
+        """Test adapting a content item for a platform."""
+        adapted_item = self.integration_manager._adapt_content_item(
+            item=self.sample_content,
+            platform='instagram'
+        )
+        
+        # Check structure
+        self.assertIsNotNone(adapted_item)
+        self.assertIn('original_item', adapted_item)
+        self.assertIn('adapted_content', adapted_item)
+        self.assertIn('platform_specifics', adapted_item)
+        
+        # Check content adaptation
+        adapted_content = adapted_item['adapted_content']
+        self.assertIn('captions', adapted_content)
+        self.assertIn('hashtags', adapted_content)
+        self.assertIn('media_suggestions', adapted_content)
+    
+    def test_get_platform_suggestions(self):
+        """Test getting platform-specific suggestions."""
+        suggestions = self.integration_manager.get_platform_suggestions(
+            content=self.sample_content,
+            platforms=self.platforms
+        )
+        
+        # Check structure
+        self.assertEqual(len(suggestions), len(self.platforms))
+        
+        for platform in self.platforms:
+            self.assertIn(platform, suggestions)
+            platform_suggestions = suggestions[platform]
+            self.assertIsInstance(platform_suggestions, dict)
+    
+    def test_validate_platform_content(self):
+        """Test validating content for a platform."""
+        validation = self.integration_manager.validate_platform_content(
+            content=self.sample_content,
+            platform='instagram'
+        )
+        
+        # Check structure
+        self.assertIn('platform', validation)
+        self.assertIn('is_valid', validation)
+        self.assertIn('specifications', validation)
+        
+        # Check validation result
+        self.assertIsInstance(validation['is_valid'], bool)
+    
+    def test_optimize_cross_platform_content(self):
+        """Test optimizing content for multiple platforms."""
+        optimized = self.integration_manager.optimize_cross_platform_content(
+            content=self.sample_content,
+            platforms=self.platforms
+        )
+        
+        # Check structure
+        self.assertEqual(len(optimized), len(self.platforms))
+        
+        for platform in self.platforms:
+            self.assertIn(platform, optimized)
+            platform_optimized = optimized[platform]
+            self.assertIsInstance(platform_optimized, dict)
+    
+    def test_error_handling(self):
+        """Test error handling with invalid inputs."""
+        # Test with invalid platform
+        with self.assertRaises(Exception):
+            self.integration_manager.validate_platform_content(
+                content=self.sample_content,
+                platform='invalid_platform'
+            )
+        
+        # Test with invalid content
+        invalid_content = {'title': 'Invalid Content'}
+        validation = self.integration_manager.validate_platform_content(
+            content=invalid_content,
+            platform='instagram'
+        )
+        self.assertFalse(validation['is_valid'])
+        self.assertIn('error', validation)
+
+if __name__ == '__main__':
+    unittest.main() 

lib/ai_seo_tools/content_calendar/tests/test_platform_adapters.py

@@ -0,0 +1,186 @@
+import unittest
+from typing import Dict, Any
+from datetime import datetime
+
+from ..integrations.platform_adapters import UnifiedPlatformAdapter
+
+class TestUnifiedPlatformAdapter(unittest.TestCase):
+    """Test cases for the UnifiedPlatformAdapter."""
+    
+    def setUp(self):
+        """Set up test cases."""
+        self.adapter = UnifiedPlatformAdapter()
+        self.test_content = {
+            'title': 'Test Content',
+            'content': 'This is a test content for platform adaptation.',
+            'keywords': ['test', 'content', 'platform'],
+            'tone': 'professional',
+            'cta': 'Learn More',
+            'audience': 'For All',
+            'language': 'English',
+            'industry': 'technology',
+            'word_count': 1000
+        }
+    
+    def test_adapt_instagram_content(self):
+        """Test Instagram content adaptation."""
+        adapted_content = self.adapter.adapt_content(
+            content=self.test_content,
+            platform='instagram'
+        )
+        
+        self.assertIsInstance(adapted_content, dict)
+        self.assertIn('captions', adapted_content)
+        self.assertIn('hashtags', adapted_content)
+        self.assertIn('media_suggestions', adapted_content)
+        self.assertIn('platform_specific', adapted_content)
+    
+    def test_adapt_twitter_content(self):
+        """Test Twitter content adaptation."""
+        adapted_content = self.adapter.adapt_content(
+            content=self.test_content,
+            platform='twitter'
+        )
+        
+        self.assertIsInstance(adapted_content, dict)
+        self.assertIn('tweets', adapted_content)
+        self.assertIn('thread_structure', adapted_content)
+        self.assertIn('media_suggestions', adapted_content)
+        self.assertIn('platform_specific', adapted_content)
+    
+    def test_adapt_linkedin_content(self):
+        """Test LinkedIn content adaptation."""
+        adapted_content = self.adapter.adapt_content(
+            content=self.test_content,
+            platform='linkedin'
+        )
+        
+        self.assertIsInstance(adapted_content, dict)
+        self.assertIn('post', adapted_content)
+        self.assertIn('engagement_optimization', adapted_content)
+        self.assertIn('media_suggestions', adapted_content)
+        self.assertIn('platform_specific', adapted_content)
+    
+    def test_adapt_blog_content(self):
+        """Test blog content adaptation."""
+        adapted_content = self.adapter.adapt_content(
+            content=self.test_content,
+            platform='blog'
+        )
+        
+        self.assertIsInstance(adapted_content, dict)
+        self.assertIn('post', adapted_content)
+        self.assertIn('seo_optimization', adapted_content)
+        self.assertIn('media_suggestions', adapted_content)
+        self.assertIn('platform_specific', adapted_content)
+    
+    def test_adapt_facebook_content(self):
+        """Test Facebook content adaptation."""
+        adapted_content = self.adapter.adapt_content(
+            content=self.test_content,
+            platform='facebook'
+        )
+        
+        self.assertIsInstance(adapted_content, dict)
+        self.assertIn('post', adapted_content)
+        self.assertIn('engagement_optimization', adapted_content)
+        self.assertIn('media_suggestions', adapted_content)
+        self.assertIn('platform_specific', adapted_content)
+    
+    def test_validate_content(self):
+        """Test content validation."""
+        # Test valid content
+        self.assertTrue(
+            self.adapter.validate_content(
+                self.test_content,
+                'instagram'
+            )
+        )
+        
+        # Test invalid content (missing required fields)
+        invalid_content = {
+            'title': 'Test Content',
+            'content': 'This is a test content.'
+        }
+        self.assertFalse(
+            self.adapter.validate_content(
+                invalid_content,
+                'instagram'
+            )
+        )
+    
+    def test_unsupported_platform(self):
+        """Test handling of unsupported platform."""
+        with self.assertRaises(ValueError):
+            self.adapter.adapt_content(
+                content=self.test_content,
+                platform='unsupported_platform'
+            )
+    
+    def test_content_adaptation_with_context(self):
+        """Test content adaptation with additional context."""
+        context = {
+            'target_audience': 'professionals',
+            'campaign_goals': ['awareness', 'engagement'],
+            'brand_voice': 'authoritative'
+        }
+        
+        adapted_content = self.adapter.adapt_content(
+            content=self.test_content,
+            platform='linkedin',
+            context=context
+        )
+        
+        self.assertIsInstance(adapted_content, dict)
+        self.assertIn('post', adapted_content)
+        self.assertIn('engagement_optimization', adapted_content)
+    
+    def test_error_handling(self):
+        """Test error handling in content adaptation."""
+        # Test with invalid content structure
+        invalid_content = {
+            'title': 123,  # Invalid type
+            'content': None  # Missing required field
+        }
+        
+        adapted_content = self.adapter.adapt_content(
+            content=invalid_content,
+            platform='blog'
+        )
+        
+        self.assertIn('error', adapted_content)
+    
+    def test_platform_specs(self):
+        """Test platform specifications."""
+        specs = self.adapter.platform_specs
+        
+        # Check Instagram specs
+        self.assertIn('instagram', specs)
+        self.assertIn('max_caption_length', specs['instagram'])
+        self.assertIn('max_hashtags', specs['instagram'])
+        self.assertIn('required_fields', specs['instagram'])
+        
+        # Check Twitter specs
+        self.assertIn('twitter', specs)
+        self.assertIn('max_tweet_length', specs['twitter'])
+        self.assertIn('max_thread_length', specs['twitter'])
+        self.assertIn('required_fields', specs['twitter'])
+        
+        # Check LinkedIn specs
+        self.assertIn('linkedin', specs)
+        self.assertIn('max_post_length', specs['linkedin'])
+        self.assertIn('required_fields', specs['linkedin'])
+        
+        # Check blog specs
+        self.assertIn('blog', specs)
+        self.assertIn('min_word_count', specs['blog'])
+        self.assertIn('max_word_count', specs['blog'])
+        self.assertIn('required_fields', specs['blog'])
+        
+        # Check Facebook specs
+        self.assertIn('facebook', specs)
+        self.assertIn('max_post_length', specs['facebook'])
+        self.assertIn('required_fields', specs['facebook'])
+
+if __name__ == '__main__':
+    unittest.main() 

lib/ai_seo_tools/content_calendar/tests/test_seo_optimizer.py

@@ -0,0 +1,132 @@
+import unittest
+from datetime import datetime
+from typing import Dict, Any
+
+from ..integrations.seo_optimizer import SEOOptimizer
+
+class TestSEOOptimizer(unittest.TestCase):
+    """Test cases for the SEOOptimizer class."""
+    
+    def setUp(self):
+        """Set up test fixtures."""
+        self.seo_optimizer = SEOOptimizer()
+        
+        # Sample content for testing
+        self.sample_content = {
+            'title': 'The Future of AI in Content Marketing',
+            'content': 'AI is revolutionizing content marketing...',
+            'keywords': ['AI', 'content marketing', 'automation'],
+            'author': 'John Doe',
+            'publish_date': datetime.now().isoformat(),
+            'description': 'An in-depth look at AI in content marketing',
+            'image_url': 'https://example.com/image.jpg',
+            'url': 'https://example.com/article'
+        }
+        
+        # Sample calendar for testing
+        self.sample_calendar = {
+            'metadata': {
+                'start_date': datetime.now().isoformat(),
+                'end_date': datetime.now().isoformat(),
+                'platforms': ['blog', 'social'],
+                'content_types': ['article']
+            },
+            'content_items': [self.sample_content]
+        }
+    
+    def test_optimize_content(self):
+        """Test content optimization."""
+        optimized = self.seo_optimizer.optimize_content(
+            content=self.sample_content,
+            content_type='article',
+            language='English',
+            search_intent='Informational Intent'
+        )
+        
+        # Check structure
+        self.assertIn('original_content', optimized)
+        self.assertIn('seo_optimized', optimized)
+        
+        # Check SEO elements
+        seo_elements = optimized['seo_optimized']
+        self.assertIn('title', seo_elements)
+        self.assertIn('meta_description', seo_elements)
+        self.assertIn('structured_data', seo_elements)
+        self.assertIn('keywords', seo_elements)
+    
+    def test_optimize_title(self):
+        """Test title optimization."""
+        titles = self.seo_optimizer._optimize_title(
+            title=self.sample_content['title'],
+            keywords=self.sample_content['keywords'],
+            content_type='article',
+            language='English',
+            search_intent='Informational Intent'
+        )
+        
+        # Check titles
+        self.assertIsInstance(titles, list)
+        self.assertTrue(len(titles) > 0)
+    
+    def test_generate_meta_description(self):
+        """Test meta description generation."""
+        descriptions = self.seo_optimizer._generate_meta_description(
+            keywords=self.sample_content['keywords'],
+            content_type='article',
+            language='English',
+            search_intent='Informational Intent'
+        )
+        
+        # Check descriptions
+        self.assertIsInstance(descriptions, list)
+        self.assertTrue(len(descriptions) > 0)
+    
+    def test_generate_structured_data(self):
+        """Test structured data generation."""
+        structured_data = self.seo_optimizer._generate_structured_data(
+            content=self.sample_content,
+            content_type='article'
+        )
+        
+        # Check structured data
+        self.assertIsNotNone(structured_data)
+    
+    def test_optimize_calendar_content(self):
+        """Test calendar content optimization."""
+        optimized_calendar = self.seo_optimizer.optimize_calendar_content(
+            calendar=self.sample_calendar,
+            content_type='article',
+            language='English',
+            search_intent='Informational Intent'
+        )
+        
+        # Check structure
+        self.assertIn('metadata', optimized_calendar)
+        self.assertIn('content_items', optimized_calendar)
+        
+        # Check content items
+        self.assertTrue(len(optimized_calendar['content_items']) > 0)
+        for item in optimized_calendar['content_items']:
+            self.assertIn('original_content', item)
+            self.assertIn('seo_optimized', item)
+    
+    def test_error_handling(self):
+        """Test error handling with invalid inputs."""
+        # Test with invalid content
+        invalid_content = {'title': 'Invalid Content'}
+        optimized = self.seo_optimizer.optimize_content(
+            content=invalid_content,
+            content_type='article'
+        )
+        self.assertIn('error', optimized)
+        
+        # Test with invalid calendar
+        invalid_calendar = {'metadata': {}}
+        optimized_calendar = self.seo_optimizer.optimize_calendar_content(
+            calendar=invalid_calendar,
+            content_type='article'
+        )
+        self.assertIn('error', optimized_calendar)
+
+if __name__ == '__main__':
+    unittest.main() 

lib/ai_seo_tools/content_calendar/ui/add_content_modal.py

@@ -0,0 +1,21 @@
+import streamlit as st
+
+def render_add_content_modal(selected_date, on_add_content, on_generate_with_ai):
+    if st.button("+ Add Content", key="open_add_content_dialog_bottom"):
+        st.session_state['show_add_content_dialog'] = True
+    if st.session_state.get('show_add_content_dialog', False):
+        st.markdown("### Add Content")
+        with st.form("quick_add_form_dialog_bottom"):
+            title = st.text_input("Title")
+            platform = st.selectbox("Platform", ["Blog", "Instagram", "Twitter", "LinkedIn", "Facebook"])
+            content_type = st.selectbox("Content Type", ["Article", "Social Post", "Video", "Newsletter"])
+            publish_date = st.date_input("Publish Date", selected_date)
+            col_add, col_ai = st.columns([0.6, 0.4])
+            with col_add:
+                if st.form_submit_button("Add Content"):
+                    on_add_content(title, platform, content_type, publish_date)
+            with col_ai:
+                if st.form_submit_button("Generate with AI"):
+                    on_generate_with_ai(title, platform, content_type)
+        if st.button("Close", key="close_add_content_dialog_bottom"):
+            st.session_state['show_add_content_dialog'] = False 

lib/ai_seo_tools/content_calendar/ui/ai_suggestions_modal.py

@@ -0,0 +1,137 @@
+import streamlit as st
+
+def render_ai_suggestions_modal(generate_ai_suggestions, on_create_brief, on_schedule, on_refine, on_customize):
+    st.subheader("AI Content Suggestions")
+    default_type = st.session_state.get('ai_modal_type', "Blog Post")
+    default_topic = st.session_state.get('ai_modal_topic', "")
+    default_platform = st.session_state.get('ai_modal_platform', "Blog")
+    content_types = {
+        "Blog Post": "Long-form content for in-depth topics",
+        "Social Media Post": "Short, engaging content for social platforms",
+        "Video": "Visual content with script and storyboard",
+        "Newsletter": "Email content for subscriber engagement"
+    }
+    content_type = st.selectbox(
+        "Content Type",
+        list(content_types.keys()),
+        format_func=lambda x: f"{x} - {content_types[x]}",
+        key="modal_suggestion_type",
+        index=list(content_types.keys()).index(default_type) if default_type in content_types else 0
+    )
+    topic = st.text_input("Enter topic or keyword", value=default_topic, key="modal_suggestion_topic")
+    with st.expander("Advanced Options"):
+        audience = st.multiselect(
+            "Target Audience",
+            ["Professionals", "Students", "Entrepreneurs", "General Public", "Industry Experts"],
+            default=["Professionals"]
+        )
+        goals = st.multiselect(
+            "Content Goals",
+            ["Increase Engagement", "Generate Leads", "Build Authority", "Drive Traffic", "Educate"],
+            default=["Increase Engagement"]
+        )
+        tone = st.select_slider(
+            "Content Tone",
+            options=["Professional", "Casual", "Educational", "Entertaining", "Persuasive"],
+            value="Professional"
+        )
+        length = st.radio(
+            "Content Length",
+            ["Short", "Medium", "Long"],
+            horizontal=True
+        )
+        st.subheader("AI Model Settings")
+        model_settings = {
+            "Creativity Level": st.slider("Creativity Level", 0.0, 1.0, 0.7, 0.1),
+            "Formality Level": st.slider("Formality Level", 0.0, 1.0, 0.5, 0.1),
+            "Technical Depth": st.slider("Technical Depth", 0.0, 1.0, 0.5, 0.1)
+        }
+        st.subheader("Content Style Preferences")
+        style_preferences = {
+            "Use Examples": st.checkbox("Include Real-world Examples", True),
+            "Use Statistics": st.checkbox("Include Statistics and Data", True),
+            "Use Quotes": st.checkbox("Include Expert Quotes", False),
+            "Use Case Studies": st.checkbox("Include Case Studies", False)
+        }
+        st.subheader("SEO Preferences")
+        seo_preferences = {
+            "Keyword Density": st.slider("Keyword Density (%)", 1, 5, 2),
+            "Internal Linking": st.checkbox("Suggest Internal Links", True),
+            "External Linking": st.checkbox("Suggest External Links", True),
+            "Meta Description": st.checkbox("Generate Meta Description", True)
+        }
+        st.subheader("Platform-specific Settings")
+        platform_settings = {
+            "Hashtag Usage": st.checkbox("Suggest Hashtags", True),
+            "Image Suggestions": st.checkbox("Suggest Images", True),
+            "Video Suggestions": st.checkbox("Suggest Videos", False),
+            "Interactive Elements": st.checkbox("Suggest Interactive Elements", False)
+        }
+    if st.button("Generate Suggestions", type="primary", key="modal_generate_btn"):
+        with st.spinner("Generating suggestions..."):
+            suggestions = generate_ai_suggestions(
+                content_type,
+                topic,
+                audience,
+                goals,
+                tone,
+                length,
+                model_settings,
+                style_preferences,
+                seo_preferences,
+                platform_settings
+            )
+            if suggestions:
+                suggestion_tabs = st.tabs([f"Suggestion {i+1}" for i in range(len(suggestions))])
+                for i, (tab, suggestion) in enumerate(zip(suggestion_tabs, suggestions)):
+                    with tab:
+                        col1, col2 = st.columns([2, 1])
+                        with col1:
+                            st.subheader(suggestion['title'])
+                            st.write(f"**Type:** {suggestion['type']}")
+                            st.write(f"**Platform:** {suggestion['platform']}")
+                            st.write(f"**Target Audience:** {', '.join(suggestion['audience'])}")
+                            st.write(f"**Estimated Impact:** {suggestion['impact']}")
+                            with st.expander("Content Preview"):
+                                st.write(suggestion.get('preview', 'Preview not available'))
+                                if suggestion.get('style_elements'):
+                                    st.write("**Style Elements:**")
+                                    for element in suggestion['style_elements']:
+                                        st.write(f"- {element}")
+                                if suggestion.get('seo_elements'):
+                                    st.write("**SEO Elements:**")
+                                    for element in suggestion['seo_elements']:
+                                        st.write(f"- {element}")
+                        with col2:
+                            st.subheader("Performance Metrics")
+                            metrics = {
+                                "Engagement Score": suggestion.get('engagement_score', '85%'),
+                                "Reach Potential": suggestion.get('reach', 'High'),
+                                "Conversion Rate": suggestion.get('conversion', '3.5%'),
+                                "SEO Impact": suggestion.get('seo_impact', 'Strong')
+                            }
+                            for metric, value in metrics.items():
+                                st.metric(metric, value)
+                            st.subheader("Actions")
+                            if st.button("Create Brief", key=f"modal_brief_{i}"):
+                                on_create_brief(suggestion)
+                            if st.button("Schedule", key=f"modal_schedule_{i}"):
+                                on_schedule(suggestion)
+                            if st.button("Refine", key=f"modal_refine_{i}"):
+                                on_refine(suggestion)
+                            if st.button("Customize", key=f"modal_customize_{i}"):
+                                on_customize(suggestion)
+                        with st.expander("Additional Options"):
+                            st.write("**Platform Optimizations**")
+                            for platform in suggestion.get('platform_optimizations', []):
+                                st.write(f"- {platform}")
+                            st.write("**Content Variations**")
+                            for variation in suggestion.get('variations', []):
+                                st.write(f"- {variation}")
+                            st.write("**SEO Recommendations**")
+                            for seo in suggestion.get('seo_recommendations', []):
+                                st.write(f"- {seo}")
+                            if suggestion.get('media_suggestions'):
+                                st.write("**Media Suggestions**")
+                                for media in suggestion['media_suggestions']:
+                                    st.write(f"- {media}") 

lib/ai_seo_tools/content_calendar/ui/calendar_view.py

@@ -0,0 +1,51 @@
+import streamlit as st
+from .components.content_card import render_content_card
+from .components.badge import render_badge
+
+def render_calendar_view(calendar_data, icon_map, status_color, on_edit, on_delete, on_generate, get_item_key):
+    if calendar_data is not None and not calendar_data.empty:
+        st.markdown("### All Scheduled Content")
+        calendar_data = calendar_data.sort_values(by="date")
+        grouped = list(calendar_data.groupby(calendar_data['date'].dt.date))
+        for i, (date, group) in enumerate(grouped):
+            exp_open = (i == 0)
+            with st.expander(f"{date.strftime('%B %d, %Y')}", expanded=exp_open):
+                for idx, row in group.iterrows():
+                    item_key = get_item_key(row)
+                    is_editing = st.session_state.get("editing_item_key") == item_key
+                    platform = str(row['platform'])
+                    if hasattr(platform, 'value'):
+                        platform = platform.value
+                    platform_map = {
+                        'blog': 'Blog',
+                        'website': 'Blog',
+                        'instagram': 'Instagram',
+                        'twitter': 'Twitter',
+                        'linkedin': 'LinkedIn',
+                        'facebook': 'Facebook',
+                    }
+                    platform_disp = platform_map.get(platform.lower(), 'Blog')
+                    type_disp = str(row['type'])
+                    if hasattr(type_disp, 'value'):
+                        type_disp = type_disp.value
+                    type_disp = type_disp.replace('_', ' ').title()
+                    status_disp = row['status'].capitalize()
+                    platform_icon = icon_map.get(platform_disp, '🌐')
+                    type_icon = icon_map.get(type_disp, '📄')
+                    render_content_card(
+                        row=row,
+                        is_editing=is_editing,
+                        on_edit=lambda r=row: on_edit(r),
+                        on_delete=lambda r=row: on_delete(r),
+                        on_generate=lambda r=row: on_generate(r),
+                        icon_map=icon_map,
+                        status_color=status_color,
+                        platform_disp=platform_disp,
+                        type_disp=type_disp,
+                        status_disp=status_disp,
+                        platform_icon=platform_icon,
+                        type_icon=type_icon,
+                        item_key=item_key
+                    )
+    else:
+        st.info("No content scheduled yet. Add content to see it here.") 

lib/ai_seo_tools/content_calendar/ui/components/ab_testing.py

@@ -0,0 +1,231 @@
+import streamlit as st
+from typing import Dict, Any, List
+from lib.ai_seo_tools.content_calendar.models.calendar import ContentItem
+import logging
+
+logger = logging.getLogger(__name__)
+
+def render_ab_testing(
+    content_generator,
+    calendar_manager
+) -> None:
+    """Render the A/B testing interface."""
+    try:
+        st.header("A/B Testing")
+        
+        # Test Configuration
+        st.markdown("### Create A/B Test")
+        col1, col2 = st.columns([2, 1])
+        
+        with col1:
+            test_content = st.selectbox(
+                "Select content for A/B testing",
+                options=[item.title for item in calendar_manager.get_calendar().get_all_content()],
+                key="ab_test_content_select"
+            )
+        
+        with col2:
+            num_variants = st.slider(
+                "Number of variants",
+                min_value=2,
+                max_value=5,
+                value=2,
+                help="Number of different versions to test"
+            )
+        
+        if test_content:
+            content_item = next(
+                item for item in calendar_manager.get_calendar().get_all_content()
+                if item.title == test_content
+            )
+            
+            # Test Settings
+            with st.expander("Test Settings"):
+                col1, col2 = st.columns(2)
+                with col1:
+                    test_duration = st.number_input(
+                        "Test Duration (days)",
+                        min_value=1,
+                        max_value=30,
+                        value=7
+                    )
+                    target_metric = st.selectbox(
+                        "Primary Metric",
+                        options=['Engagement', 'Conversion', 'Reach', 'Click-through'],
+                        value='Engagement'
+                    )
+                with col2:
+                    audience_size = st.select_slider(
+                        "Audience Size",
+                        options=['Small', 'Medium', 'Large'],
+                        value='Medium'
+                    )
+                    confidence_level = st.slider(
+                        "Confidence Level",
+                        min_value=90,
+                        max_value=99,
+                        value=95,
+                        help="Statistical confidence level for test results"
+                    )
+            
+            # Generate Variants
+            if st.button("Generate Variants"):
+                with st.spinner("Generating variants..."):
+                    variants = _generate_ab_test_variants(content_generator, content_item, num_variants)
+                    if variants:
+                        st.success(f"Generated {len(variants)} variants!")
+                        
+                        # Display variants in tabs
+                        variant_tabs = st.tabs([f"Variant {i+1}" for i in range(len(variants))])
+                        for i, tab in enumerate(variant_tabs):
+                            with tab:
+                                st.markdown(f"### Variant {i+1}")
+                                st.json(variants[i]['content'])
+                                
+                                # Variant metrics
+                                col1, col2, col3 = st.columns(3)
+                                with col1:
+                                    st.metric(
+                                        "Engagement Score",
+                                        f"{variants[i]['metrics']['engagement_score']:.1f}%"
+                                    )
+                                with col2:
+                                    st.metric(
+                                        "Conversion Rate",
+                                        f"{variants[i]['metrics']['conversion_rate']:.1f}%"
+                                    )
+                                with col3:
+                                    st.metric(
+                                        "Reach",
+                                        f"{variants[i]['metrics']['reach']:,}"
+                                    )
+        
+        # Results Analysis
+        st.markdown("### Analyze Results")
+        if test_content in st.session_state.ab_test_results:
+            test_data = st.session_state.ab_test_results[test_content]
+            
+            # Test Status
+            st.info(f"Test Status: {test_data['status']}")
+            st.write(f"Started: {test_data['start_time']}")
+            
+            if test_data['status'] == 'running':
+                if st.button("End Test and Analyze"):
+                    with st.spinner("Analyzing results..."):
+                        results = _analyze_ab_test_results(content_item)
+                        if results:
+                            st.success("Analysis complete!")
+                            _display_test_results(results)
+    
+    except Exception as e:
+        logger.error(f"Error in A/B testing interface: {str(e)}", exc_info=True)
+        st.error(f"Error in A/B testing: {str(e)}")
+
+def _generate_ab_test_variants(
+    content_generator,
+    content: ContentItem,
+    num_variants: int
+) -> List[Dict[str, Any]]:
+    """Generate A/B test variants for content."""
+    try:
+        logger.info(f"Generating {num_variants} variants for content: {content.title}")
+        
+        # Convert content to dictionary format
+        content_dict = {
+            'title': content.title,
+            'content': content.description,
+            'metadata': {
+                'platform': content.platforms[0].name if content.platforms else 'Unknown',
+                'content_type': content.content_type.name
+            }
+        }
+        
+        variants = []
+        for i in range(num_variants):
+            # Generate different variations
+            variant = content_generator.generate_variation(
+                content=content_dict,
+                variation_type=f"variant_{i+1}"
+            )
+            if variant:
+                variants.append(variant)
+        
+        return variants
+        
+    except Exception as e:
+        logger.error(f"Error generating variants: {str(e)}")
+        return []
+
+def _analyze_ab_test_results(content_item: ContentItem) -> Dict[str, Any]:
+    """Analyze results of A/B testing for content optimization."""
+    try:
+        logger.info(f"Analyzing A/B test results for: {content_item.title}")
+        
+        if content_item.title not in st.session_state.ab_test_results:
+            raise ValueError("No A/B test results found for this content")
+        
+        test_data = st.session_state.ab_test_results[content_item.title]
+        variants = test_data['variants']
+        
+        # Calculate performance metrics
+        results = {
+            'total_engagement': sum(v['metrics']['engagement_score'] for v in variants),
+            'total_conversions': sum(v['metrics']['conversion_rate'] for v in variants),
+            'total_reach': sum(v['metrics']['reach'] for v in variants),
+            'best_performing_variant': max(variants, key=lambda x: x['metrics']['engagement_score']),
+            'recommendations': []
+        }
+        
+        # Generate recommendations
+        for variant in variants:
+            if variant['metrics']['engagement_score'] > 0.7:  # High engagement threshold
+                results['recommendations'].append({
+                    'variant_id': variant['variant_id'],
+                    'reason': 'High engagement score',
+                    'suggested_actions': ['Scale this variant', 'Apply learnings to other content']
+                })
+        
+        # Update test status
+        test_data['status'] = 'completed'
+        test_data['results'] = results
+        
+        logger.info("A/B test results analyzed successfully")
+        return results
+        
+    except Exception as e:
+        logger.error(f"Error analyzing A/B test results: {str(e)}", exc_info=True)
+        st.error(f"Error analyzing A/B test results: {str(e)}")
+        return {}
+
+def _display_test_results(results: Dict[str, Any]) -> None:
+    """Display A/B test results in the UI."""
+    with st.expander("Overall Performance", expanded=True):
+        col1, col2, col3 = st.columns(3)
+        with col1:
+            st.metric(
+                "Total Engagement",
+                f"{results['total_engagement']:.1f}%"
+            )
+        with col2:
+            st.metric(
+                "Total Conversions",
+                f"{results['total_conversions']:.1f}%"
+            )
+        with col3:
+            st.metric(
+                "Total Reach",
+                f"{results['total_reach']:,}"
+            )
+    
+    with st.expander("Best Performing Variant", expanded=True):
+        best_variant = results['best_performing_variant']
+        st.markdown(f"### {best_variant['variant_id']}")
+        st.json(best_variant['content'])
+    
+    with st.expander("Recommendations", expanded=True):
+        for rec in results['recommendations']:
+            st.markdown(f"#### {rec['variant_id']}")
+            st.write(f"Reason: {rec['reason']}")
+            st.write("Suggested Actions:")
+            for action in rec['suggested_actions']:
+                st.write(f"- {action}") 

lib/ai_seo_tools/content_calendar/ui/components/badge.py

@@ -0,0 +1,2 @@
+def render_badge(platform_disp, platform_icon, type_disp, status_disp):
+    return f"<span class='badge-content-calendar badge-platform-{platform_disp.lower()}'>{platform_icon} {platform_disp} &nbsp;|&nbsp; {type_disp} &nbsp;|&nbsp; <span class='chip-status chip-status-{status_disp.lower()}'>{status_disp}</span></span>" 

lib/ai_seo_tools/content_calendar/ui/components/content_card.py

@@ -0,0 +1,22 @@
+import streamlit as st
+
+def render_content_card(row, is_editing, on_edit, on_delete, on_generate, icon_map, status_color, platform_disp, type_disp, status_disp, platform_icon, type_icon, item_key):
+    st.markdown(f"<div class='card-content-calendar'>", unsafe_allow_html=True)
+    st.markdown(f"<div style='display:flex;align-items:center;justify-content:space-between;gap:8px;'>", unsafe_allow_html=True)
+    st.markdown(f"<div style='display:flex;align-items:center;gap:8px;min-width:0;flex:1;'>"
+                f"{type_icon}<span class='content-title'>{row['title']}</span></div>", unsafe_allow_html=True)
+    st.markdown("<div style='display:flex;align-items:center;gap:4px;'>", unsafe_allow_html=True)
+    col1, col2, col3 = st.columns([1, 1, 1])
+    with col1:
+        if st.button("⚡", key=f"generate_{item_key}", help="Generate with AI Blog Writer", use_container_width=True):
+            on_generate()
+    with col2:
+        if st.button("✏️", key=f"edit_{item_key}", help="Edit Content", use_container_width=True):
+            on_edit()
+    with col3:
+        if st.button("🗑️", key=f"delete_{item_key}", help="Delete Content", use_container_width=True):
+            on_delete()
+    st.markdown("</div>", unsafe_allow_html=True)
+    st.markdown("</div>", unsafe_allow_html=True)
+    st.markdown(f"<div class='content-meta'><span class='badge-content-calendar badge-platform-{platform_disp.lower()}'>{platform_icon} {platform_disp} &nbsp;|&nbsp; {type_disp} &nbsp;|&nbsp; <span class='chip-status chip-status-{status_disp.lower()}'>{status_disp}</span></span></div>", unsafe_allow_html=True)
+    st.markdown("</div>", unsafe_allow_html=True) 

lib/ai_seo_tools/content_calendar/ui/components/content_optimization.py

@@ -0,0 +1,467 @@
+import streamlit as st
+from typing import Dict, Any, List
+from datetime import datetime
+import pandas as pd
+from ...core.content_generator import ContentGenerator
+from ...core.ai_generator import AIGenerator
+from ...integrations.seo_optimizer import SEOOptimizer
+from ...models.calendar import ContentItem, ContentType, Platform, SEOData
+import logging
+
+logger = logging.getLogger('content_calendar.optimization')
+
+class OptimizationManager:
+    def __init__(self):
+        if 'optimization_history' not in st.session_state:
+            st.session_state.optimization_history = {}
+        if 'optimization_previews' not in st.session_state:
+            st.session_state.optimization_previews = {}
+        if 'optimization_metrics' not in st.session_state:
+            st.session_state.optimization_metrics = {}
+
+    def track_optimization(self, content_id: str, optimization_data: Dict[str, Any]) -> bool:
+        """Track optimization changes for content with detailed metrics."""
+        try:
+            if content_id not in st.session_state.optimization_history:
+                st.session_state.optimization_history[content_id] = []
+            
+            optimization_data['timestamp'] = datetime.now()
+            optimization_data['metrics'] = self._calculate_optimization_metrics(optimization_data)
+            st.session_state.optimization_history[content_id].append(optimization_data)
+            
+            # Update metrics
+            if content_id not in st.session_state.optimization_metrics:
+                st.session_state.optimization_metrics[content_id] = []
+            st.session_state.optimization_metrics[content_id].append(optimization_data['metrics'])
+            
+            return True
+        except Exception as e:
+            logger.error(f"Error tracking optimization: {str(e)}")
+            return False
+
+    def _calculate_optimization_metrics(self, optimization_data: Dict[str, Any]) -> Dict[str, Any]:
+        """Calculate detailed optimization metrics."""
+        try:
+            metrics = {
+                'readability_score': 0,
+                'seo_score': 0,
+                'engagement_potential': 0,
+                'keyword_density': 0,
+                'content_quality': 0
+            }
+            
+            # Calculate readability score
+            if 'content' in optimization_data:
+                content = optimization_data['content']
+                metrics['readability_score'] = self._calculate_readability(content)
+            
+            # Calculate SEO score
+            if 'seo_data' in optimization_data:
+                seo_data = optimization_data['seo_data']
+                metrics['seo_score'] = self._calculate_seo_score(seo_data)
+                metrics['keyword_density'] = self._calculate_keyword_density(seo_data)
+            
+            # Calculate engagement potential
+            if 'engagement_metrics' in optimization_data:
+                engagement = optimization_data['engagement_metrics']
+                metrics['engagement_potential'] = self._calculate_engagement_potential(engagement)
+            
+            # Calculate overall content quality
+            metrics['content_quality'] = (
+                metrics['readability_score'] * 0.3 +
+                metrics['seo_score'] * 0.3 +
+                metrics['engagement_potential'] * 0.4
+            )
+            
+            return metrics
+        except Exception as e:
+            logger.error(f"Error calculating optimization metrics: {str(e)}")
+            return {}
+
+    def _calculate_readability(self, content: str) -> float:
+        """Calculate content readability score."""
+        try:
+            # Implement readability calculation logic
+            # This is a placeholder implementation
+            return 0.8
+        except Exception as e:
+            logger.error(f"Error calculating readability: {str(e)}")
+            return 0.0
+
+    def _calculate_seo_score(self, seo_data: SEOData) -> float:
+        """Calculate SEO optimization score."""
+        try:
+            # Implement SEO score calculation logic
+            # This is a placeholder implementation
+            return 0.85
+        except Exception as e:
+            logger.error(f"Error calculating SEO score: {str(e)}")
+            return 0.0
+
+    def _calculate_keyword_density(self, seo_data: SEOData) -> float:
+        """Calculate keyword density."""
+        try:
+            # Implement keyword density calculation logic
+            # This is a placeholder implementation
+            return 2.5
+        except Exception as e:
+            logger.error(f"Error calculating keyword density: {str(e)}")
+            return 0.0
+
+    def _calculate_engagement_potential(self, engagement: Dict[str, Any]) -> float:
+        """Calculate content engagement potential."""
+        try:
+            # Implement engagement potential calculation logic
+            # This is a placeholder implementation
+            return 0.75
+        except Exception as e:
+            logger.error(f"Error calculating engagement potential: {str(e)}")
+            return 0.0
+
+    def get_optimization_history(self, content_id: str) -> List[Dict[str, Any]]:
+        """Get detailed optimization history for content."""
+        return st.session_state.optimization_history.get(content_id, [])
+
+    def get_optimization_metrics(self, content_id: str) -> List[Dict[str, Any]]:
+        """Get optimization metrics history."""
+        return st.session_state.optimization_metrics.get(content_id, [])
+
+    def save_preview(self, content_id: str, preview_data: Dict[str, Any]) -> bool:
+        """Save optimization preview with versioning."""
+        try:
+            if content_id not in st.session_state.optimization_previews:
+                st.session_state.optimization_previews[content_id] = []
+            
+            preview_data['version'] = len(st.session_state.optimization_previews[content_id]) + 1
+            preview_data['timestamp'] = datetime.now()
+            st.session_state.optimization_previews[content_id].append(preview_data)
+            return True
+        except Exception as e:
+            logger.error(f"Error saving preview: {str(e)}")
+            return False
+
+    def get_preview(self, content_id: str, version: int = None) -> Dict[str, Any]:
+        """Get optimization preview with optional versioning."""
+        try:
+            previews = st.session_state.optimization_previews.get(content_id, [])
+            if not previews:
+                return {}
+            
+            if version is None:
+                return previews[-1]
+            
+            for preview in previews:
+                if preview['version'] == version:
+                    return preview
+            
+            return {}
+        except Exception as e:
+            logger.error(f"Error getting preview: {str(e)}")
+            return {}
+
+def render_content_optimization(
+    content_generator: ContentGenerator,
+    ai_generator: AIGenerator,
+    seo_optimizer: SEOOptimizer
+):
+    """Render the content optimization interface with advanced features."""
+    st.header("Content Optimization")
+    
+    # Initialize optimization manager
+    optimization_manager = OptimizationManager()
+    
+    # Check if calendar manager is available
+    if 'calendar_manager' not in st.session_state:
+        st.error("Calendar manager not initialized. Please refresh the page.")
+        return
+    
+    # Get available content
+    try:
+        available_content = st.session_state.calendar_manager.get_calendar().get_all_content()
+        content_options = [item.title for item in available_content]
+    except Exception as e:
+        logger.error(f"Error getting content options: {str(e)}")
+        st.error("Error loading content. Please try again.")
+        return
+    
+    if not content_options:
+        st.info("No content available for optimization. Please add some content first.")
+        return
+    
+    # Content Selection
+    selected_content = st.selectbox(
+        "Select content to optimize",
+        options=content_options,
+        key="optimize_content_select"
+    )
+    
+    if selected_content:
+        try:
+            content_item = next(
+                item for item in available_content
+                if item.title == selected_content
+            )
+            
+            # Create tabs for different optimization aspects
+            opt_tabs = st.tabs(["Content Optimization", "SEO Optimization", "Preview", "History", "Analytics"])
+            
+            with opt_tabs[0]:
+                st.subheader("Content Optimization")
+                
+                # Advanced Optimization Settings
+                with st.expander("Advanced Settings", expanded=True):
+                    col1, col2 = st.columns(2)
+                    
+                    with col1:
+                        tone = st.select_slider(
+                            "Content Tone",
+                            options=['Professional', 'Casual', 'Friendly', 'Authoritative', 'Conversational'],
+                            value='Professional'
+                        )
+                        length = st.select_slider(
+                            "Content Length",
+                            options=['Short', 'Medium', 'Long', 'Comprehensive'],
+                            value='Medium'
+                        )
+                    
+                    with col2:
+                        engagement_goal = st.select_slider(
+                            "Engagement Goal",
+                            options=['Awareness', 'Consideration', 'Conversion', 'Retention'],
+                            value='Consideration'
+                        )
+                        creativity_level = st.slider(
+                            "Creativity Level",
+                            min_value=1,
+                            max_value=10,
+                            value=5
+                        )
+                
+                # Platform-Specific Optimization
+                st.subheader("Platform-Specific Optimization")
+                platforms = st.multiselect(
+                    "Target Platforms",
+                    options=[p.name for p in content_item.platforms],
+                    default=[p.name for p in content_item.platforms]
+                )
+                
+                # Generate Optimization
+                if st.button("Generate Optimization"):
+                    with st.spinner("Generating optimization..."):
+                        try:
+                            # Generate optimized content
+                            optimized_content = content_generator.optimize_for_platform(
+                                content=content_item,
+                                platform=Platform[platforms[0]] if platforms else content_item.platforms[0],
+                                requirements={
+                                    'tone': tone,
+                                    'length': length,
+                                    'engagement_goal': engagement_goal,
+                                    'creativity_level': creativity_level
+                                }
+                            )
+                            
+                            if optimized_content:
+                                # Track optimization
+                                optimization_manager.track_optimization(
+                                    content_item.title,
+                                    {
+                                        'type': 'content',
+                                        'changes': optimized_content.get('changes', []),
+                                        'metrics': optimized_content.get('metrics', {}),
+                                        'content': optimized_content.get('content', ''),
+                                        'engagement_metrics': optimized_content.get('engagement_metrics', {})
+                                    }
+                                )
+                                
+                                # Save preview
+                                optimization_manager.save_preview(
+                                    content_item.title,
+                                    {
+                                        'original': content_item.description,
+                                        'optimized': optimized_content.get('content', ''),
+                                        'changes': optimized_content.get('changes', []),
+                                        'metrics': optimized_content.get('metrics', {})
+                                    }
+                                )
+                                
+                                st.success("Content optimized successfully!")
+                        except Exception as e:
+                            logger.error(f"Error optimizing content: {str(e)}")
+                            st.error(f"Error optimizing content: {str(e)}")
+            
+            with opt_tabs[1]:
+                st.subheader("SEO Optimization")
+                
+                # SEO Settings
+                with st.expander("SEO Settings", expanded=True):
+                    col1, col2 = st.columns(2)
+                    
+                    with col1:
+                        keyword_density = st.slider(
+                            "Target Keyword Density",
+                            min_value=1,
+                            max_value=5,
+                            value=2,
+                            help="Target percentage of keywords in content"
+                        )
+                        internal_linking = st.checkbox(
+                            "Enable Internal Linking",
+                            value=True,
+                            help="Automatically add internal links to related content"
+                        )
+                    
+                    with col2:
+                        external_linking = st.checkbox(
+                            "Enable External Linking",
+                            value=True,
+                            help="Add relevant external links for credibility"
+                        )
+                        structured_data = st.checkbox(
+                            "Add Structured Data",
+                            value=True,
+                            help="Include schema.org structured data"
+                        )
+                
+                # Generate SEO Optimization
+                if st.button("Generate SEO Optimization"):
+                    with st.spinner("Generating SEO optimization..."):
+                        try:
+                            # Generate SEO-optimized content
+                            seo_optimized = seo_optimizer.optimize_content(
+                                content=content_item,
+                                content_type=content_item.content_type.name,
+                                language='English',
+                                search_intent='Informational Intent',
+                                settings={
+                                    'keyword_density': keyword_density,
+                                    'internal_linking': internal_linking,
+                                    'external_linking': external_linking,
+                                    'structured_data': structured_data
+                                }
+                            )
+                            
+                            if seo_optimized:
+                                # Track optimization
+                                optimization_manager.track_optimization(
+                                    content_item.title,
+                                    {
+                                        'type': 'seo',
+                                        'changes': seo_optimized.get('changes', []),
+                                        'metrics': seo_optimized.get('metrics', {}),
+                                        'seo_data': seo_optimized
+                                    }
+                                )
+                                
+                                # Save preview
+                                optimization_manager.save_preview(
+                                    content_item.title,
+                                    {
+                                        'meta_description': seo_optimized.get('meta_description', ''),
+                                        'keywords': seo_optimized.get('keywords', []),
+                                        'structured_data': seo_optimized.get('structured_data', {}),
+                                        'changes': seo_optimized.get('changes', [])
+                                    }
+                                )
+                                
+                                st.success("SEO optimization completed!")
+                        except Exception as e:
+                            logger.error(f"Error optimizing SEO: {str(e)}")
+                            st.error(f"Error optimizing SEO: {str(e)}")
+            
+            with opt_tabs[2]:
+                st.subheader("Optimization Preview")
+                
+                preview_data = optimization_manager.get_preview(content_item.title)
+                if preview_data:
+                    # Content Preview
+                    if 'original' in preview_data:
+                        st.markdown("### Content Changes")
+                        col1, col2 = st.columns(2)
+                        
+                        with col1:
+                            st.markdown("#### Original Content")
+                            st.write(preview_data['original'])
+                        
+                        with col2:
+                            st.markdown("#### Optimized Content")
+                            st.write(preview_data['optimized'])
+                        
+                        st.markdown("#### Key Changes")
+                        for change in preview_data.get('changes', []):
+                            st.write(f"- {change}")
+                    
+                    # SEO Preview
+                    if 'meta_description' in preview_data:
+                        st.markdown("### SEO Changes")
+                        st.markdown("#### Meta Description")
+                        st.write(preview_data['meta_description'])
+                        
+                        st.markdown("#### Keywords")
+                        st.write(", ".join(preview_data['keywords']))
+                        
+                        st.markdown("#### Structured Data")
+                        st.json(preview_data['structured_data'])
+                    
+                    # Metrics Preview
+                    if 'metrics' in preview_data:
+                        st.markdown("### Optimization Metrics")
+                        metrics = preview_data['metrics']
+                        col1, col2, col3 = st.columns(3)
+                        
+                        with col1:
+                            st.metric("Readability Score", f"{metrics.get('readability_score', 0):.1%}")
+                        with col2:
+                            st.metric("SEO Score", f"{metrics.get('seo_score', 0):.1%}")
+                        with col3:
+                            st.metric("Engagement Potential", f"{metrics.get('engagement_potential', 0):.1%}")
+                else:
+                    st.info("No optimization preview available. Generate optimization first.")
+            
+            with opt_tabs[3]:
+                st.subheader("Optimization History")
+                
+                history = optimization_manager.get_optimization_history(content_item.title)
+                if history:
+                    for entry in history:
+                        with st.expander(f"Optimization at {entry['timestamp']}"):
+                            st.write(f"Type: {entry['type']}")
+                            st.write("Changes:")
+                            for change in entry.get('changes', []):
+                                st.write(f"- {change}")
+                            
+                            if 'metrics' in entry:
+                                st.write("Metrics:")
+                                st.json(entry['metrics'])
+                else:
+                    st.info("No optimization history available.")
+            
+            with opt_tabs[4]:
+                st.subheader("Optimization Analytics")
+                
+                metrics_history = optimization_manager.get_optimization_metrics(content_item.title)
+                if metrics_history:
+                    # Convert metrics history to DataFrame
+                    df = pd.DataFrame(metrics_history)
+                    
+                    # Plot metrics over time
+                    st.line_chart(df[['readability_score', 'seo_score', 'engagement_potential', 'content_quality']])
+                    
+                    # Display current metrics
+                    current_metrics = metrics_history[-1]
+                    col1, col2, col3, col4 = st.columns(4)
+                    
+                    with col1:
+                        st.metric("Readability", f"{current_metrics.get('readability_score', 0):.1%}")
+                    with col2:
+                        st.metric("SEO Score", f"{current_metrics.get('seo_score', 0):.1%}")
+                    with col3:
+                        st.metric("Engagement", f"{current_metrics.get('engagement_potential', 0):.1%}")
+                    with col4:
+                        st.metric("Overall Quality", f"{current_metrics.get('content_quality', 0):.1%}")
+                    
+                    # Display keyword density trend
+                    st.subheader("Keyword Density Trend")
+                    st.line_chart(df['keyword_density'])
+                else:
+                    st.info("No optimization metrics available. Generate optimization first.") 

lib/ai_seo_tools/content_calendar/ui/components/content_series.py

@@ -0,0 +1,392 @@
+import streamlit as st
+from typing import Dict, Any, List
+from datetime import datetime, timedelta
+import pandas as pd
+from ...core.content_generator import ContentGenerator
+from ...core.ai_generator import AIGenerator
+from ...integrations.seo_optimizer import SEOOptimizer
+from ...models.calendar import ContentItem, ContentType, Platform, SEOData
+import logging
+
+logger = logging.getLogger('content_calendar.series')
+
+class SeriesManager:
+    def __init__(self):
+        self.series_data = {}
+        if 'content_series' not in st.session_state:
+            st.session_state.content_series = {}
+        if 'series_relationships' not in st.session_state:
+            st.session_state.series_relationships = {}
+        if 'series_performance' not in st.session_state:
+            st.session_state.series_performance = {}
+
+    def create_series(self, series_id: str, topic: str, num_pieces: int, content_type: ContentType, 
+                     platforms: List[Platform], schedule_strategy: str = 'linear') -> Dict[str, Any]:
+        """Create a new content series with tracking and scheduling."""
+        try:
+            series = {
+                'id': series_id,
+                'topic': topic,
+                'num_pieces': num_pieces,
+                'content_type': content_type,
+                'platforms': platforms,
+                'schedule_strategy': schedule_strategy,
+                'pieces': [],
+                'performance': {},
+                'created_at': datetime.now(),
+                'status': 'draft',
+                'relationships': {},
+                'platform_distribution': {p.name: [] for p in platforms}
+            }
+            st.session_state.content_series[series_id] = series
+            return series
+        except Exception as e:
+            logger.error(f"Error creating series: {str(e)}")
+            return None
+
+    def add_piece(self, series_id: str, piece: Dict[str, Any]) -> bool:
+        """Add a content piece to the series with relationship tracking."""
+        try:
+            if series_id in st.session_state.content_series:
+                series = st.session_state.content_series[series_id]
+                piece_id = f"piece_{len(series['pieces'])}"
+                piece['id'] = piece_id
+                
+                # Track relationships
+                if series['pieces']:
+                    previous_piece = series['pieces'][-1]
+                    piece['relationships'] = {
+                        'previous': previous_piece['id'],
+                        'next': None
+                    }
+                    previous_piece['relationships']['next'] = piece_id
+                
+                # Add to platform distribution
+                for platform in piece.get('platforms', []):
+                    if platform.name in series['platform_distribution']:
+                        series['platform_distribution'][platform.name].append(piece_id)
+                
+                series['pieces'].append(piece)
+                return True
+            return False
+        except Exception as e:
+            logger.error(f"Error adding piece to series: {str(e)}")
+            return False
+
+    def get_series_performance(self, series_id: str) -> Dict[str, Any]:
+        """Get comprehensive performance analytics for a series."""
+        try:
+            if series_id in st.session_state.content_series:
+                series = st.session_state.content_series[series_id]
+                performance = {
+                    'overall': {
+                        'total_engagement': 0,
+                        'total_reach': 0,
+                        'conversion_rate': 0,
+                        'average_engagement': 0
+                    },
+                    'platforms': {},
+                    'pieces': {},
+                    'trends': {
+                        'engagement': [],
+                        'reach': [],
+                        'conversions': []
+                    }
+                }
+                
+                # Calculate overall metrics
+                for piece in series['pieces']:
+                    piece_performance = piece.get('performance', {})
+                    performance['overall']['total_engagement'] += piece_performance.get('engagement', 0)
+                    performance['overall']['total_reach'] += piece_performance.get('reach', 0)
+                    performance['overall']['conversion_rate'] += piece_performance.get('conversion_rate', 0)
+                    
+                    # Track piece-specific performance
+                    performance['pieces'][piece['id']] = piece_performance
+                    
+                    # Track trends
+                    performance['trends']['engagement'].append(piece_performance.get('engagement', 0))
+                    performance['trends']['reach'].append(piece_performance.get('reach', 0))
+                    performance['trends']['conversions'].append(piece_performance.get('conversion_rate', 0))
+                
+                # Calculate averages
+                num_pieces = len(series['pieces'])
+                if num_pieces > 0:
+                    performance['overall']['average_engagement'] = performance['overall']['total_engagement'] / num_pieces
+                    performance['overall']['conversion_rate'] = performance['overall']['conversion_rate'] / num_pieces
+                
+                # Calculate platform-specific performance
+                for platform in series['platforms']:
+                    platform_pieces = series['platform_distribution'].get(platform.name, [])
+                    platform_performance = {
+                        'engagement': 0,
+                        'reach': 0,
+                        'conversion_rate': 0
+                    }
+                    
+                    for piece_id in platform_pieces:
+                        piece_performance = performance['pieces'].get(piece_id, {})
+                        platform_performance['engagement'] += piece_performance.get('engagement', 0)
+                        platform_performance['reach'] += piece_performance.get('reach', 0)
+                        platform_performance['conversion_rate'] += piece_performance.get('conversion_rate', 0)
+                    
+                    if platform_pieces:
+                        platform_performance['engagement'] /= len(platform_pieces)
+                        platform_performance['conversion_rate'] /= len(platform_pieces)
+                    
+                    performance['platforms'][platform.name] = platform_performance
+                
+                return performance
+            return {}
+        except Exception as e:
+            logger.error(f"Error getting series performance: {str(e)}")
+            return {}
+
+    def update_series_status(self, series_id: str, status: str) -> bool:
+        """Update the status of a series."""
+        try:
+            if series_id in st.session_state.content_series:
+                st.session_state.content_series[series_id]['status'] = status
+                return True
+            return False
+        except Exception as e:
+            logger.error(f"Error updating series status: {str(e)}")
+            return False
+
+    def schedule_series(self, series_id: str, start_date: datetime, interval: int = 7) -> bool:
+        """Schedule the series content with flexible scheduling strategies."""
+        try:
+            if series_id in st.session_state.content_series:
+                series = st.session_state.content_series[series_id]
+                current_date = start_date
+                
+                for piece in series['pieces']:
+                    piece['scheduled_date'] = current_date
+                    if series['schedule_strategy'] == 'linear':
+                        current_date += timedelta(days=interval)
+                    elif series['schedule_strategy'] == 'burst':
+                        current_date += timedelta(days=1)
+                    elif series['schedule_strategy'] == 'custom':
+                        # Custom scheduling is handled by the UI
+                        pass
+                
+                return True
+            return False
+        except Exception as e:
+            logger.error(f"Error scheduling series: {str(e)}")
+            return False
+
+def render_content_series_generator(ai_generator: AIGenerator, content_generator: ContentGenerator, 
+                                  seo_optimizer: SEOOptimizer):
+    """Render the content series generator interface with enhanced features."""
+    st.header("Content Series Generator")
+    
+    # Initialize series manager
+    series_manager = SeriesManager()
+    
+    # Series Creation Form
+    with st.form("series_creation_form"):
+        st.subheader("Create New Series")
+        series_topic = st.text_input("Series Topic")
+        num_pieces = st.slider("Number of pieces", 2, 10, 3)
+        content_type = st.selectbox(
+            "Content Type",
+            options=[ct.name for ct in ContentType],
+            key="series_content_type"
+        )
+        
+        # Multi-platform selection
+        platforms = st.multiselect(
+            "Target Platforms",
+            options=[p.name for p in Platform],
+            default=['WEBSITE'],
+            key="series_platforms"
+        )
+        
+        # Schedule strategy
+        schedule_strategy = st.selectbox(
+            "Schedule Strategy",
+            options=['linear', 'burst', 'custom'],
+            help="Linear: Evenly spaced, Burst: Grouped together, Custom: Manual scheduling"
+        )
+        
+        # Series metadata
+        with st.expander("Series Metadata"):
+            target_audience = st.text_area("Target Audience")
+            series_goals = st.multiselect(
+                "Series Goals",
+                options=['Awareness', 'Engagement', 'Conversion', 'Education'],
+                default=['Awareness']
+            )
+            series_tone = st.select_slider(
+                "Series Tone",
+                options=['Professional', 'Casual', 'Friendly', 'Authoritative', 'Conversational'],
+                value='Professional'
+            )
+        
+        submitted = st.form_submit_button("Generate Series")
+        
+        if submitted and series_topic:
+            with st.spinner("Generating content series..."):
+                try:
+                    # Create series
+                    series_id = f"series_{datetime.now().strftime('%Y%m%d_%H%M%S')}"
+                    series = series_manager.create_series(
+                        series_id=series_id,
+                        topic=series_topic,
+                        num_pieces=num_pieces,
+                        content_type=ContentType[content_type],
+                        platforms=[Platform[p] for p in platforms],
+                        schedule_strategy=schedule_strategy
+                    )
+                    
+                    if series:
+                        # Generate series content
+                        for i in range(num_pieces):
+                            content_item = ContentItem(
+                                title=f"{series_topic} - Part {i+1}",
+                                description="",
+                                content_type=ContentType[content_type],
+                                platforms=[Platform[p] for p in platforms],
+                                publish_date=datetime.now() + timedelta(days=i*7),
+                                seo_data=SEOData(
+                                    title=f"{series_topic} - Part {i+1}",
+                                    meta_description="",
+                                    keywords=[],
+                                    structured_data={}
+                                ),
+                                status='Draft'
+                            )
+                            
+                            # Generate content using AI
+                            base_content = ai_generator.generate_series_content(
+                                content_item=content_item,
+                                series_info={
+                                    'topic': series_topic,
+                                    'part_number': i+1,
+                                    'total_parts': num_pieces,
+                                    'content_type': content_type,
+                                    'platforms': platforms,
+                                    'audience': target_audience,
+                                    'goals': series_goals,
+                                    'tone': series_tone
+                                }
+                            )
+                            
+                            if base_content:
+                                # Enhance with Content Generator
+                                enhanced_content = content_generator.enhance_series_content(
+                                    content=base_content,
+                                    series_info={
+                                        'topic': series_topic,
+                                        'part_number': i+1,
+                                        'total_parts': num_pieces
+                                    }
+                                )
+                                
+                                if enhanced_content:
+                                    base_content.update(enhanced_content)
+                                
+                                # Add to series
+                                series_manager.add_piece(series_id, {
+                                    'part_number': i+1,
+                                    'content': base_content,
+                                    'seo_data': seo_optimizer.optimize_content(
+                                        content=base_content,
+                                        content_type=content_type,
+                                        language='English',
+                                        search_intent='Informational Intent'
+                                    )
+                                })
+                        
+                        st.success(f"Generated {num_pieces} content pieces for series!")
+                        
+                        # Display series preview
+                        with st.expander("Series Preview", expanded=True):
+                            for piece in series_manager.series_data[series_id]['pieces']:
+                                st.markdown(f"### Part {piece['part_number']}")
+                                st.json(piece['content'])
+                                
+                                # Platform-specific previews
+                                st.markdown("#### Platform Previews")
+                                for platform in platforms:
+                                    with st.expander(f"{platform} Preview"):
+                                        st.write(piece['content'].get('platform_previews', {}).get(platform, 'No preview available'))
+                        
+                        # Series scheduling
+                        st.subheader("Series Scheduling")
+                        if schedule_strategy == 'linear':
+                            start_date = st.date_input("Start Date", datetime.now())
+                            interval = st.number_input("Days between pieces", min_value=1, value=7)
+                            
+                            if st.button("Schedule Series"):
+                                series_manager.schedule_series(series_id, start_date, interval)
+                                st.success("Series scheduled successfully!")
+                        
+                        elif schedule_strategy == 'burst':
+                            start_date = st.date_input("Start Date", datetime.now())
+                            if st.button("Schedule Series"):
+                                series_manager.schedule_series(series_id, start_date, interval=1)
+                                st.success("Series scheduled successfully!")
+                        
+                        else:  # custom
+                            for i, piece in enumerate(series_manager.series_data[series_id]['pieces']):
+                                piece['scheduled_date'] = st.date_input(
+                                    f"Publish Date for Part {i+1}",
+                                    datetime.now() + timedelta(days=i*7)
+                                )
+                            
+                            if st.button("Save Schedule"):
+                                st.success("Series schedule saved!")
+                        
+                        # Series performance tracking
+                        st.subheader("Series Performance")
+                        performance_data = series_manager.get_series_performance(series_id)
+                        if performance_data:
+                            st.write("### Overall Performance")
+                            col1, col2, col3 = st.columns(3)
+                            with col1:
+                                st.metric("Total Engagement", f"{performance_data['overall']['total_engagement']:.1f}%")
+                            with col2:
+                                st.metric("Total Reach", f"{performance_data['overall']['total_reach']:,}")
+                            with col3:
+                                st.metric("Conversion Rate", f"{performance_data['overall']['conversion_rate']:.1f}%")
+                            
+                            # Platform-specific performance
+                            st.write("### Platform Performance")
+                            for platform in platforms:
+                                with st.expander(f"{platform} Performance"):
+                                    platform_data = performance_data['platforms'].get(platform, {})
+                                    st.write(f"Engagement: {platform_data.get('engagement', 0):.1f}%")
+                                    st.write(f"Reach: {platform_data.get('reach', 0):,}")
+                                    st.write(f"Conversions: {platform_data.get('conversion_rate', 0):.1f}%")
+                            
+                            # Performance trends
+                            st.write("### Performance Trends")
+                            trend_data = performance_data['trends']
+                            st.line_chart(pd.DataFrame({
+                                'Engagement': trend_data['engagement'],
+                                'Reach': trend_data['reach'],
+                                'Conversions': trend_data['conversions']
+                            }))
+                        
+                except Exception as e:
+                    logger.error(f"Error generating series: {str(e)}", exc_info=True)
+                    st.error(f"Error generating series: {str(e)}")
+
+    # Display existing series
+    if st.session_state.content_series:
+        st.subheader("Existing Series")
+        for series_id, series in st.session_state.content_series.items():
+            with st.expander(f"Series: {series['topic']}"):
+                st.write(f"Status: {series['status']}")
+                st.write(f"Pieces: {len(series['pieces'])}")
+                st.write(f"Created: {series['created_at']}")
+                
+                # Series actions
+                if st.button(f"View Details", key=f"view_{series_id}"):
+                    st.session_state.selected_series = series_id
+                
+                if st.button(f"Delete Series", key=f"delete_{series_id}"):
+                    del st.session_state.content_series[series_id]
+                    st.experimental_rerun() 

lib/ai_seo_tools/content_calendar/ui/components/performance_insights.py

@@ -0,0 +1,81 @@
+import streamlit as st
+from typing import Dict, Any
+from lib.ai_seo_tools.content_calendar.models.calendar import ContentItem
+import logging
+
+logger = logging.getLogger(__name__)
+
+def render_performance_insights(content_item: ContentItem, platform_adapter) -> None:
+    """Render performance insights for a content item."""
+    try:
+        logger.info(f"Rendering performance insights for: {content_item.title}")
+        
+        # Get performance data from platform adapter
+        performance_data = platform_adapter.get_content_performance(content_item)
+        
+        if not performance_data:
+            st.warning("No performance data available for this content")
+            return
+        
+        # Create metrics section
+        st.subheader("Performance Metrics")
+        col1, col2, col3 = st.columns(3)
+        
+        with col1:
+            st.metric(
+                "Engagement Rate",
+                f"{performance_data.get('engagement_rate', 0):.1f}%",
+                f"{performance_data.get('engagement_rate_change', 0):+.1f}%"
+            )
+        
+        with col2:
+            st.metric(
+                "Reach",
+                f"{performance_data.get('reach', 0):,}",
+                f"{performance_data.get('reach_change', 0):+,}"
+            )
+        
+        with col3:
+            st.metric(
+                "Conversion Rate",
+                f"{performance_data.get('conversion_rate', 0):.1f}%",
+                f"{performance_data.get('conversion_rate_change', 0):+.1f}%"
+            )
+        
+        # Create audience insights section
+        st.subheader("Audience Insights")
+        audience_data = performance_data.get('audience_insights', {})
+        
+        if audience_data:
+            col1, col2 = st.columns(2)
+            
+            with col1:
+                st.write("Demographics")
+                st.write(f"- Age: {audience_data.get('age_range', 'N/A')}")
+                st.write(f"- Gender: {audience_data.get('gender', 'N/A')}")
+                st.write(f"- Location: {audience_data.get('location', 'N/A')}")
+            
+            with col2:
+                st.write("Behavior")
+                st.write(f"- Peak Time: {audience_data.get('peak_time', 'N/A')}")
+                st.write(f"- Device: {audience_data.get('device', 'N/A')}")
+                st.write(f"- Platform: {audience_data.get('platform', 'N/A')}")
+        
+        # Create content insights section
+        st.subheader("Content Insights")
+        content_insights = performance_data.get('content_insights', {})
+        
+        if content_insights:
+            st.write("Top Performing Elements")
+            for element, score in content_insights.get('top_elements', {}).items():
+                st.write(f"- {element}: {score}")
+            
+            st.write("Improvement Suggestions")
+            for suggestion in content_insights.get('suggestions', []):
+                st.write(f"- {suggestion}")
+        
+        logger.info(f"Performance insights rendered successfully for: {content_item.title}")
+        
+    except Exception as e:
+        logger.error(f"Error rendering performance insights: {str(e)}", exc_info=True)
+        st.error(f"Error rendering performance insights: {str(e)}") 

lib/ai_seo_tools/content_calendar/ui/dashboard.py

@@ -0,0 +1,634 @@
+import streamlit as st
+import pandas as pd
+from datetime import datetime
+import logging
+import sys
+import hashlib
+from .calendar_view import render_calendar_view
+from .filters import render_filters
+from .add_content_modal import render_add_content_modal
+from .ai_suggestions_modal import render_ai_suggestions_modal
+from .components.performance_insights import render_performance_insights
+from .components.content_series import render_content_series_generator
+from .components.ab_testing import render_ab_testing
+from .components.content_optimization import render_content_optimization
+from ..core.calendar_manager import CalendarManager
+from ..core.content_brief import ContentBriefGenerator
+from ..core.content_generator import ContentGenerator
+from ..core.ai_generator import AIGenerator
+from ..integrations.platform_adapters import UnifiedPlatformAdapter
+from ..integrations.seo_optimizer import SEOOptimizer
+from lib.ai_seo_tools.content_calendar.models.calendar import ContentItem, Platform, ContentType, SEOData, Calendar
+from lib.gpt_providers.text_generation.main_text_generation import llm_text_gen
+from typing import Dict, Any, List, Tuple
+import json
+
+class ContentCalendarDashboard:
+    """Interactive dashboard for content calendar management."""
+    def __init__(self):
+        self.logger = logging.getLogger('content_calendar.dashboard')
+        self.logger.info("Initializing ContentCalendarDashboard")
+        
+        # Initialize calendar manager and store in session state
+        if 'calendar_manager' not in st.session_state:
+            st.session_state.calendar_manager = CalendarManager()
+            st.session_state.calendar_manager.load_calendar_from_json()
+        
+        self.calendar_manager = st.session_state.calendar_manager
+        self.content_brief_generator = ContentBriefGenerator()
+        self.content_generator = ContentGenerator()
+        self.ai_generator = AIGenerator()
+        self.platform_adapter = UnifiedPlatformAdapter()
+        self.seo_optimizer = SEOOptimizer()
+        
+        # Initialize A/B testing state
+        if 'ab_test_results' not in st.session_state:
+            st.session_state.ab_test_results = {}
+        
+        # Initialize content optimization state
+        if 'optimization_history' not in st.session_state:
+            st.session_state.optimization_history = {}
+        
+        # Ensure a calendar exists
+        if not self.calendar_manager.get_calendar():
+            self.calendar_manager._calendar = Calendar(
+                start_date=datetime.now(),
+                duration='monthly',
+                platforms=[Platform.WEBSITE, Platform.INSTAGRAM, Platform.TWITTER, Platform.LINKEDIN, Platform.FACEBOOK],
+                schedule={}
+            )
+        
+        # Initialize session state
+        if 'calendar_data' not in st.session_state:
+            st.session_state.calendar_data = None
+        if 'selected_content' not in st.session_state:
+            st.session_state.selected_content = None
+        if 'view_mode' not in st.session_state:
+            st.session_state.view_mode = 'day'
+        if 'selected_date' not in st.session_state:
+            st.session_state.selected_date = datetime.now()
+        
+        self.logger.info("ContentCalendarDashboard initialized successfully")
+    
+    def render(self):
+        self.logger.info("Starting dashboard render (tabbed UI)")
+        try:
+            self._inject_custom_css()
+            st.title("AI Content Planning")
+            st.markdown("""
+            Plan, schedule, and manage your content strategy with AI-powered insights. Use the calendar to organize your content and leverage AI tools for optimization.
+            """)
+            tabs = st.tabs(["Content Planning", "Content Optimization", "A/B Testing", "Content Series", "Analytics"])
+            
+            with tabs[0]:
+                icon_map = {
+                    'Blog': '📝', 'Website': '🌐', 'Instagram': '📸', 'Twitter': '🐦', 'LinkedIn': '💼', 'Facebook': '📘',
+                    'Article': '📄', 'Social Post': '💬', 'Video': '🎬', 'Newsletter': '✉️'
+                }
+                status_color = {
+                    'Draft': '#bdbdbd', 'Scheduled': '#1976d2', 'Published': '#43a047', 'Archived': '#757575'
+                }
+                calendar_data = self._get_calendar_data()
+                def on_edit(row):
+                    st.session_state["editing_item_key"] = self._get_item_key(row)
+                    st.experimental_rerun()
+                def on_delete(row):
+                    self._delete_content(row)
+                    st.experimental_rerun()
+                def on_generate(row):
+                    st.session_state['show_ai_modal'] = True
+                    st.session_state['ai_modal_topic'] = row['title']
+                    st.session_state['ai_modal_type'] = str(row['type'])
+                    st.session_state['ai_modal_platform'] = str(row['platform'])
+                    st.experimental_rerun()
+                render_calendar_view(
+                    calendar_data=calendar_data,
+                    icon_map=icon_map,
+                    status_color=status_color,
+                    on_edit=on_edit,
+                    on_delete=on_delete,
+                    on_generate=on_generate,
+                    get_item_key=self._get_item_key
+                )
+                st.markdown("---")
+                render_filters()
+                def handle_add_content(title, platform, content_type, publish_date):
+                    self._add_content({
+                        'title': title,
+                        'platform': platform,
+                        'type': content_type,
+                        'publish_date': publish_date
+                    })
+                    st.session_state['show_add_content_dialog'] = False
+                    st.success("Content added!")
+                    st.experimental_rerun()
+                def handle_generate_with_ai(title, platform, content_type):
+                    st.session_state['show_add_content_dialog'] = False
+                    st.session_state['show_ai_modal'] = True
+                    st.session_state['ai_modal_topic'] = title
+                    st.session_state['ai_modal_type'] = content_type
+                    st.session_state['ai_modal_platform'] = platform
+                render_add_content_modal(
+                    selected_date=st.session_state.selected_date,
+                    on_add_content=handle_add_content,
+                    on_generate_with_ai=handle_generate_with_ai
+                )
+                if st.session_state.get('show_ai_modal', False):
+                    st.markdown("### AI Content Suggestions")
+                    with st.container():
+                        render_ai_suggestions_modal(
+                            generate_ai_suggestions=self._generate_ai_suggestions,
+                            on_create_brief=self._create_content_brief,
+                            on_schedule=self._schedule_content,
+                            on_refine=self._refine_suggestion,
+                            on_customize=self._customize_suggestion
+                        )
+                        if st.button("Close"):
+                            st.session_state['show_ai_modal'] = False
+
+            with tabs[1]:
+                render_content_optimization(
+                    content_generator=self.content_generator,
+                    ai_generator=self.ai_generator,
+                    seo_optimizer=self.seo_optimizer
+                )
+
+            with tabs[2]:
+                render_ab_testing(self.content_generator, self.calendar_manager)
+
+            with tabs[3]:
+                render_content_series_generator(
+                    self.ai_generator,
+                    self.content_generator,
+                    self.seo_optimizer
+                )
+
+            with tabs[4]:
+                st.header("Analytics")
+                st.markdown("### Performance Insights")
+                selected_content = st.selectbox(
+                    "Select content to analyze",
+                    options=[item.title for item in self.calendar_manager.get_calendar().get_all_content()],
+                    key="analytics_content_select"
+                )
+                if selected_content:
+                    content_item = next(
+                        item for item in self.calendar_manager.get_calendar().get_all_content()
+                        if item.title == selected_content
+                    )
+                    render_performance_insights(content_item, self.platform_adapter)
+                
+                st.markdown("### Optimization History")
+                if selected_content in st.session_state.optimization_history:
+                    st.json(st.session_state.optimization_history[selected_content])
+
+            self.logger.info("Dashboard render completed successfully (tabbed UI)")
+        except Exception as e:
+            self.logger.error(f"Error rendering dashboard: {str(e)}", exc_info=True)
+            st.error(f"An error occurred: {str(e)}")
+    
+    def _inject_custom_css(self):
+        st.markdown("""
+            <style>
+            /* Add your custom CSS here if needed */
+            </style>
+        """, unsafe_allow_html=True)
+
+    def _get_calendar_data(self):
+        self.logger.info("_get_calendar_data called")
+        try:
+            calendar_obj = self.calendar_manager.get_calendar()
+            if not calendar_obj:
+                self.logger.info("No calendar found in manager")
+                return None
+            data = []
+            for date_str, items in calendar_obj.schedule.items():
+                for item in items:
+                    data.append({
+                        'date': pd.to_datetime(date_str),
+                        'title': item.title,
+                        'platform': item.platforms[0] if item.platforms else 'Unknown',
+                        'type': item.content_type,
+                        'status': item.status
+                    })
+            df = pd.DataFrame(data) if data else None
+            return df
+        except Exception as e:
+            self.logger.error(f"Error loading calendar data: {str(e)}", exc_info=True)
+            st.error(f"Error loading calendar data: {str(e)}")
+            return None
+
+    def _add_content(self, content):
+        calendar = self.calendar_manager.get_calendar()
+        if not calendar:
+            st.error("No calendar found. Please create a calendar first.")
+            return
+        platform_map = {
+            'Blog': Platform.WEBSITE,
+            'Instagram': Platform.INSTAGRAM,
+            'Twitter': Platform.TWITTER,
+            'LinkedIn': Platform.LINKEDIN,
+            'Facebook': Platform.FACEBOOK,
+        }
+        platform_enum = platform_map.get(content['platform'], Platform.WEBSITE)
+        content_type_map = {
+            'Article': ContentType.BLOG_POST,
+            'Social Post': ContentType.SOCIAL_MEDIA,
+            'Video': ContentType.VIDEO,
+            'Newsletter': ContentType.NEWSLETTER,
+        }
+        content_type_enum = content_type_map.get(content['type'], ContentType.BLOG_POST)
+        seo_data = SEOData(
+            title=content['title'],
+            meta_description="",
+            keywords=[],
+            structured_data={},
+        )
+        new_item = ContentItem(
+            title=content['title'],
+            description="",
+            content_type=content_type_enum,
+            platforms=[platform_enum],
+            publish_date=pd.to_datetime(content['publish_date']),
+            seo_data=seo_data,
+            status=content.get('status', 'Draft')
+        )
+        calendar.add_content(new_item)
+        self.calendar_manager.save_calendar_to_json()
+
+    def _delete_content(self, row):
+        calendar = self.calendar_manager.get_calendar()
+        if not calendar:
+            return
+        for date_str, items in list(calendar.schedule.items()):
+            calendar.schedule[date_str] = [
+                item for item in items
+                if not (
+                    item.title == row['title'] and
+                    str(item.publish_date.date()) == str(row['date'].date()) and
+                    item.platforms[0].name == str(row['platform']) and
+                    item.content_type.name == str(row['type'])
+                )
+            ]
+            if not calendar.schedule[date_str]:
+                del calendar.schedule[date_str]
+        self.calendar_manager.save_calendar_to_json()
+
+    def _edit_content(self, row, new_title, new_platform, new_type, new_status):
+        self._delete_content(row)
+        self._add_content({
+            'title': new_title,
+            'platform': new_platform,
+            'type': new_type,
+            'publish_date': row['date'],
+            'status': new_status
+        })
+
+    def _get_item_key(self, row):
+        key_str = f"{row['title']}_{row['date']}_{row['platform']}_{row['type']}"
+        return hashlib.md5(key_str.encode()).hexdigest()
+
+    def _generate_ai_suggestions(self, content_type, topic, audience, goals, tone, length, model_settings, style_preferences, seo_preferences, platform_settings):
+        """Generate AI content suggestions based on input parameters."""
+        try:
+            self.logger.info(f"Generating AI suggestions for topic: {topic}")
+            
+            # Map content type string to ContentType enum
+            content_type_map = {
+                'Blog Post': ContentType.BLOG_POST,
+                'Social Media Post': ContentType.SOCIAL_MEDIA,
+                'Video': ContentType.VIDEO,
+                'Newsletter': ContentType.NEWSLETTER,
+                'Article': ContentType.BLOG_POST,
+                'Social Post': ContentType.SOCIAL_MEDIA
+            }
+            content_type_enum = content_type_map.get(content_type, ContentType.BLOG_POST)
+            
+            # Map platform string to Platform enum
+            platform_map = {
+                'Blog': Platform.WEBSITE,
+                'Instagram': Platform.INSTAGRAM,
+                'Twitter': Platform.TWITTER,
+                'LinkedIn': Platform.LINKEDIN,
+                'Facebook': Platform.FACEBOOK,
+                'Website': Platform.WEBSITE
+            }
+            platform = st.session_state.get('ai_modal_platform', 'Blog')
+            platform_enum = platform_map.get(platform, Platform.WEBSITE)
+            
+            # Create a content item for the suggestion
+            content_item = ContentItem(
+                title=topic,
+                description="",
+                content_type=content_type_enum,
+                platforms=[platform_enum],
+                publish_date=datetime.now(),
+                seo_data=SEOData(
+                    title=topic,
+                    meta_description="",
+                    keywords=[],
+                    structured_data={}
+                ),
+                status='Draft'
+            )
+            
+            # Use AIGenerator to generate suggestions
+            suggestions = self.ai_generator.generate_ai_suggestions(
+                content_type=content_type_enum,
+                topic=topic,
+                audience=audience,
+                goals=goals,
+                tone=tone,
+                length=length,
+                model_settings=model_settings,
+                style_preferences=style_preferences,
+                seo_preferences=seo_preferences,
+                platform_settings=platform_settings,
+                platform=platform_enum
+            )
+            
+            if not suggestions:
+                self.logger.warning("No suggestions generated")
+                return []
+            
+            # Format suggestions
+            formatted_suggestions = []
+            for suggestion in suggestions:
+                formatted_suggestion = {
+                    'title': suggestion.get('title', topic),
+                    'type': content_type,
+                    'platform': platform,
+                    'audience': audience,
+                    'impact': f"High impact for {', '.join(goals)}",
+                    'preview': suggestion.get('preview', ''),
+                    'style_elements': [
+                        f"Tone: {tone}",
+                        f"Length: {length}",
+                        f"Creativity: {model_settings.get('Creativity Level', 'balanced')}",
+                        f"Formality: {model_settings.get('Formality Level', 'professional')}"
+                    ],
+                    'seo_elements': [
+                        f"Keyword Density: {seo_preferences.get('Keyword Density', '2')}%",
+                        "Internal Linking: Enabled" if seo_preferences.get('Internal Linking', True) else "Internal Linking: Disabled",
+                        "External Linking: Enabled" if seo_preferences.get('External Linking', True) else "External Linking: Disabled"
+                    ],
+                    'engagement_score': f"{85 + len(formatted_suggestions)*5}%",
+                    'reach': 'High',
+                    'conversion': f"{3.5 + len(formatted_suggestions)*0.5}%",
+                    'seo_impact': 'Strong',
+                    'platform_optimizations': suggestion.get('platform_optimizations', []),
+                    'variations': suggestion.get('variations', [
+                        "Alternative headline",
+                        "Different content angle",
+                        "Alternative format"
+                    ]),
+                    'seo_recommendations': suggestion.get('seo_elements', []),
+                    'media_suggestions': suggestion.get('media_suggestions', [
+                        "Featured image",
+                        "Supporting graphics",
+                        "Social media visuals"
+                    ])
+                }
+                formatted_suggestions.append(formatted_suggestion)
+            
+            self.logger.info(f"Generated {len(formatted_suggestions)} suggestions successfully")
+            return formatted_suggestions
+            
+        except Exception as e:
+            self.logger.error(f"Error generating AI suggestions: {str(e)}", exc_info=True)
+            st.error(f"Error generating suggestions: {str(e)}")
+            return []
+
+    def _create_content_brief(self, content_item: ContentItem) -> Dict[str, Any]:
+        """Create a detailed content brief for the given content item."""
+        try:
+            self.logger.info(f"Creating content brief for: {content_item.title}")
+            
+            # Generate content brief using the content brief generator
+            brief = self.content_brief_generator.generate_brief(
+                content_item=content_item,
+                target_audience={
+                    'audience': content_item.description,
+                    'goals': ['engage', 'inform', 'convert']
+                }
+            )
+            
+            # Enhance brief with SEO data
+            if brief and 'content_flow' in brief:
+                brief['seo_optimization'] = {
+                    'meta_description': self.seo_optimizer.generate_meta_description(
+                        brief['content_flow'].get('introduction', {}).get('summary', '')
+                    ),
+                    'keywords': self.seo_optimizer.extract_keywords(
+                        brief['content_flow'].get('introduction', {}).get('summary', '')
+                    ),
+                    'structured_data': self.seo_optimizer.generate_structured_data(
+                        content_item.content_type
+                    )
+                }
+            
+            self.logger.info(f"Content brief created successfully for: {content_item.title}")
+            return brief
+            
+        except Exception as e:
+            self.logger.error(f"Error creating content brief: {str(e)}", exc_info=True)
+            st.error(f"Error creating content brief: {str(e)}")
+            return {}
+
+    def _schedule_content(self, content_item: ContentItem, publish_date: datetime) -> bool:
+        """Schedule content for publishing on the specified date."""
+        try:
+            self.logger.info(f"Scheduling content: {content_item.title} for {publish_date}")
+            
+            # Get the calendar
+            calendar = self.calendar_manager.get_calendar()
+            if not calendar:
+                raise ValueError("No calendar found")
+            
+            # Update the publish date
+            content_item.publish_date = publish_date
+            
+            # Add to calendar
+            calendar.add_content(content_item)
+            
+            # Save changes
+            self.calendar_manager.save_calendar_to_json()
+            
+            self.logger.info(f"Content scheduled successfully: {content_item.title}")
+            return True
+            
+        except Exception as e:
+            self.logger.error(f"Error scheduling content: {str(e)}", exc_info=True)
+            st.error(f"Error scheduling content: {str(e)}")
+            return False
+
+    def _refine_suggestion(self, suggestion: Dict[str, Any], feedback: Dict[str, Any]) -> Dict[str, Any]:
+        """Refine an AI-generated suggestion based on user feedback."""
+        try:
+            self.logger.info("Refining AI suggestion based on feedback")
+            
+            # Update suggestion based on feedback
+            if 'tone' in feedback:
+                suggestion['style_elements'] = [
+                    f"Tone: {feedback['tone']}",
+                    *[elem for elem in suggestion['style_elements'] if not elem.startswith('Tone:')]
+                ]
+            
+            if 'length' in feedback:
+                suggestion['style_elements'] = [
+                    f"Length: {feedback['length']}",
+                    *[elem for elem in suggestion['style_elements'] if not elem.startswith('Length:')]
+                ]
+            
+            if 'keywords' in feedback:
+                suggestion['seo_elements'] = [
+                    f"Keywords: {', '.join(feedback['keywords'])}",
+                    *[elem for elem in suggestion['seo_elements'] if not elem.startswith('Keywords:')]
+                ]
+            
+            # Regenerate content with refined parameters
+            refined_content = self.content_brief_generator.generate_brief(
+                content_item=ContentItem(
+                    title=suggestion['title'],
+                    description="",
+                    content_type=ContentType[suggestion['type'].upper().replace(' ', '_')],
+                    platforms=[Platform[suggestion['platform'].upper()]],
+                    publish_date=datetime.now(),
+                    seo_data=SEOData(
+                        title=suggestion['title'],
+                        meta_description="",
+                        keywords=feedback.get('keywords', []),
+                        structured_data={}
+                    ),
+                    status='Draft'
+                ),
+                target_audience={
+                    'audience': suggestion['audience'],
+                    'goals': feedback.get('goals', ['engage', 'inform']),
+                    'preferences': {
+                        'tone': feedback.get('tone', 'professional'),
+                        'length': feedback.get('length', 'medium')
+                    }
+                }
+            )
+            
+            if refined_content:
+                suggestion['preview'] = refined_content.get('content_flow', {}).get('introduction', {}).get('summary', '')
+            
+            self.logger.info("Suggestion refined successfully")
+            return suggestion
+            
+        except Exception as e:
+            self.logger.error(f"Error refining suggestion: {str(e)}", exc_info=True)
+            st.error(f"Error refining suggestion: {str(e)}")
+            return suggestion
+
+    def _customize_suggestion(self, suggestion: Dict[str, Any], customizations: Dict[str, Any]) -> Dict[str, Any]:
+        """Customize an AI-generated suggestion with specific requirements."""
+        try:
+            self.logger.info("Customizing AI suggestion")
+            
+            # Apply customizations
+            if 'title' in customizations:
+                suggestion['title'] = customizations['title']
+            
+            if 'platform' in customizations:
+                suggestion['platform'] = customizations['platform']
+            
+            if 'style' in customizations:
+                suggestion['style_elements'] = [
+                    f"Tone: {customizations['style'].get('tone', 'professional')}",
+                    f"Length: {customizations['style'].get('length', 'medium')}",
+                    f"Creativity: {customizations['style'].get('creativity', 'balanced')}",
+                    f"Formality: {customizations['style'].get('formality', 'professional')}"
+                ]
+            
+            if 'seo' in customizations:
+                suggestion['seo_elements'] = [
+                    f"Keyword Density: {customizations['seo'].get('keyword_density', '2')}%",
+                    "Internal Linking: Enabled" if customizations['seo'].get('internal_linking', True) else "Internal Linking: Disabled",
+                    "External Linking: Enabled" if customizations['seo'].get('external_linking', True) else "External Linking: Disabled"
+                ]
+            
+            # Regenerate content with customizations
+            customized_content = self.content_brief_generator.generate_brief(
+                content_item=ContentItem(
+                    title=suggestion['title'],
+                    description="",
+                    content_type=ContentType[suggestion['type'].upper().replace(' ', '_')],
+                    platforms=[Platform[suggestion['platform'].upper()]],
+                    publish_date=datetime.now(),
+                    seo_data=SEOData(
+                        title=suggestion['title'],
+                        meta_description="",
+                        keywords=customizations.get('seo', {}).get('keywords', []),
+                        structured_data={}
+                    ),
+                    status='Draft'
+                ),
+                target_audience={
+                    'audience': suggestion['audience'],
+                    'goals': customizations.get('goals', ['engage', 'inform']),
+                    'preferences': customizations.get('style', {})
+                }
+            )
+            
+            if customized_content:
+                suggestion['preview'] = customized_content.get('content_flow', {}).get('introduction', {}).get('summary', '')
+            
+            self.logger.info("Suggestion customized successfully")
+            return suggestion
+            
+        except Exception as e:
+            self.logger.error(f"Error customizing suggestion: {str(e)}", exc_info=True)
+            st.error(f"Error customizing suggestion: {str(e)}")
+            return suggestion
+
+    def _optimize_content_for_platform(self, content_item: ContentItem, platform: Platform) -> Dict[str, Any]:
+        """Optimize content specifically for a target platform."""
+        try:
+            self.logger.info(f"Optimizing content for {platform.name}: {content_item.title}")
+            
+            # Get platform-specific requirements
+            platform_requirements = self.platform_adapter.get_platform_requirements(platform)
+            
+            # Generate platform-optimized content
+            optimized_content = self.content_generator.optimize_for_platform(
+                content=content_item,
+                platform=platform,
+                requirements=platform_requirements
+            )
+            
+            if not optimized_content:
+                raise ValueError(f"Failed to optimize content for {platform.name}")
+            
+            # Enhance with AI
+            ai_enhanced = self.ai_generator.enhance_for_platform(
+                content=optimized_content,
+                platform=platform,
+                enhancement_type='platform_specific'
+            )
+            
+            if ai_enhanced:
+                optimized_content.update(ai_enhanced)
+            
+            # Track optimization history
+            if content_item.title not in st.session_state.optimization_history:
+                st.session_state.optimization_history[content_item.title] = []
+            st.session_state.optimization_history[content_item.title].append({
+                'platform': platform.name,
+                'timestamp': datetime.now(),
+                'changes': optimized_content.get('changes', [])
+            })
+            
+            self.logger.info(f"Content optimized successfully for {platform.name}")
+            return optimized_content
+            
+        except Exception as e:
+            self.logger.error(f"Error optimizing content: {str(e)}", exc_info=True)
+            st.error(f"Error optimizing content: {str(e)}")
+            return {}
+
+if __name__ == "__main__":
+    dashboard = ContentCalendarDashboard()
+    dashboard.render() 

lib/ai_seo_tools/content_calendar/ui/filters.py

@@ -0,0 +1,30 @@
+import streamlit as st
+from datetime import datetime, timedelta
+
+def render_filters():
+    with st.expander("Filters", expanded=False):
+        col1, col2 = st.columns(2)
+        with col1:
+            start_date = st.date_input("Start Date", st.session_state.get('filter_start_date', datetime.now()))
+            end_date = st.date_input("End Date", st.session_state.get('filter_end_date', datetime.now() + timedelta(days=30)))
+            st.session_state['filter_start_date'] = start_date
+            st.session_state['filter_end_date'] = end_date
+        with col2:
+            platforms = st.multiselect(
+                "Platforms",
+                ["Blog", "Instagram", "Twitter", "LinkedIn", "Facebook"],
+                default=st.session_state.get('filter_platforms', ["Blog"])
+            )
+            st.session_state['filter_platforms'] = platforms
+            content_types = st.multiselect(
+                "Content Types",
+                ["Article", "Social Post", "Video", "Newsletter"],
+                default=st.session_state.get('filter_content_types', ["Article"])
+            )
+            st.session_state['filter_content_types'] = content_types
+            statuses = st.multiselect(
+                "Status",
+                ["Draft", "Scheduled", "Published", "Archived"],
+                default=st.session_state.get('filter_statuses', ["Draft", "Scheduled"])
+            )
+            st.session_state['filter_statuses'] = statuses 

lib/ai_seo_tools/content_calendar/utils/date_utils.py

@@ -0,0 +1,198 @@
+from datetime import datetime, timedelta
+from typing import Dict, List, Any
+import calendar
+import random
+
+def calculate_publish_dates(
+    topics: List[Dict[str, Any]],
+    start_date: datetime,
+    duration: str
+) -> Dict[str, List[Dict[str, Any]]]:
+    """
+    Calculate optimal publish dates for content topics.
+    
+    Args:
+        topics: List of content topics to schedule
+        start_date: When to start publishing
+        duration: How long the calendar should span ('weekly', 'monthly', 'quarterly')
+        
+    Returns:
+        Dictionary mapping dates to scheduled content
+    """
+    # Calculate end date based on duration
+    end_date = _calculate_end_date(start_date, duration)
+    
+    # Get all dates in range
+    dates = _get_dates_in_range(start_date, end_date)
+    
+    # Calculate optimal posting frequency
+    frequency = _calculate_posting_frequency(len(topics), len(dates))
+    
+    # Schedule content
+    schedule = _schedule_content(topics, dates, frequency)
+    
+    return schedule
+
+def _calculate_end_date(start_date: datetime, duration: str) -> datetime:
+    """Calculate end date based on duration."""
+    if duration == 'weekly':
+        return start_date + timedelta(days=7)
+    elif duration == 'monthly':
+        # Add one month
+        if start_date.month == 12:
+            return datetime(start_date.year + 1, 1, start_date.day)
+        return datetime(start_date.year, start_date.month + 1, start_date.day)
+    elif duration == 'quarterly':
+        # Add three months
+        new_month = start_date.month + 3
+        new_year = start_date.year
+        if new_month > 12:
+            new_month -= 12
+            new_year += 1
+        return datetime(new_year, new_month, start_date.day)
+    else:
+        raise ValueError(f"Invalid duration: {duration}")
+
+def _get_dates_in_range(
+    start_date: datetime,
+    end_date: datetime
+) -> List[datetime]:
+    """Get all dates in the given range."""
+    dates = []
+    current_date = start_date
+    
+    while current_date <= end_date:
+        # Skip weekends
+        if current_date.weekday() < 5:  # 0-4 are weekdays
+            dates.append(current_date)
+        current_date += timedelta(days=1)
+    
+    return dates
+
+def _calculate_posting_frequency(
+    num_topics: int,
+    num_dates: int
+) -> Dict[str, int]:
+    """
+    Calculate optimal posting frequency based on number of topics and dates.
+    
+    Returns:
+        Dictionary with posting frequency for each content type
+    """
+    # Calculate base frequency
+    base_frequency = num_dates / num_topics
+    
+    # Adjust for content types
+    return {
+        'blog_post': max(1, int(base_frequency * 0.4)),  # 40% of content
+        'social_media': max(1, int(base_frequency * 0.3)),  # 30% of content
+        'video': max(1, int(base_frequency * 0.2)),  # 20% of content
+        'newsletter': max(1, int(base_frequency * 0.1))  # 10% of content
+    }
+
+def _schedule_content(
+    topics: List[Dict[str, Any]],
+    dates: List[datetime],
+    frequency: Dict[str, int]
+) -> Dict[str, List[Dict[str, Any]]]:
+    """
+    Schedule content topics across available dates.
+    
+    Args:
+        topics: List of content topics to schedule
+        dates: Available dates for scheduling
+        frequency: Posting frequency for each content type
+        
+    Returns:
+        Dictionary mapping dates to scheduled content
+    """
+    schedule = {}
+    current_date_index = 0
+    
+    # Group topics by content type
+    topics_by_type = _group_topics_by_type(topics)
+    
+    # Schedule each content type
+    for content_type, type_topics in topics_by_type.items():
+        type_frequency = frequency.get(content_type, 1)
+        
+        for topic in type_topics:
+            # Find next available date
+            while current_date_index < len(dates):
+                date = dates[current_date_index]
+                date_str = date.strftime('%Y-%m-%d')
+                
+                # Check if date is available
+                if date_str not in schedule:
+                    schedule[date_str] = []
+                
+                # Add topic to schedule
+                schedule[date_str].append(topic)
+                
+                # Move to next date based on frequency
+                current_date_index += type_frequency
+                break
+            
+            # If we've used all dates, wrap around
+            if current_date_index >= len(dates):
+                current_date_index = 0
+    
+    return schedule
+
+def _group_topics_by_type(
+    topics: List[Dict[str, Any]]
+) -> Dict[str, List[Dict[str, Any]]]:
+    """Group topics by their content type."""
+    grouped = {}
+    
+    for topic in topics:
+        content_type = topic.get('content_type', 'blog_post')
+        if content_type not in grouped:
+            grouped[content_type] = []
+        grouped[content_type].append(topic)
+    
+    return grouped
+
+def get_optimal_posting_time(
+    content_type: str,
+    platform: str
+) -> datetime.time:
+    """
+    Get optimal posting time for content type and platform.
+    
+    Args:
+        content_type: Type of content
+        platform: Target platform
+        
+    Returns:
+        Optimal time to post
+    """
+    # Default optimal times (can be customized based on platform analytics)
+    optimal_times = {
+        'blog_post': {
+            'website': datetime.time(9, 0),  # 9 AM
+            'medium': datetime.time(10, 0)   # 10 AM
+        },
+        'social_media': {
+            'facebook': datetime.time(15, 0),  # 3 PM
+            'twitter': datetime.time(12, 0),   # 12 PM
+            'linkedin': datetime.time(8, 0),   # 8 AM
+            'instagram': datetime.time(19, 0)  # 7 PM
+        },
+        'video': {
+            'youtube': datetime.time(14, 0)   # 2 PM
+        },
+        'newsletter': {
+            'email': datetime.time(6, 0)      # 6 AM
+        }
+    }
+    
+    # Get optimal time for content type and platform
+    content_times = optimal_times.get(content_type, {})
+    optimal_time = content_times.get(platform)
+    
+    if optimal_time is None:
+        # Default to 9 AM if no specific time is set
+        optimal_time = datetime.time(9, 0)
+    
+    return optimal_time 

lib/ai_seo_tools/content_calendar/utils/error_handling.py

@@ -0,0 +1,154 @@
+import functools
+import logging
+from typing import Any, Callable, TypeVar, cast
+from datetime import datetime
+
+logger = logging.getLogger(__name__)
+
+T = TypeVar('T')
+
+def handle_calendar_error(func: Callable[..., T]) -> Callable[..., T]:
+    """
+    Decorator to handle errors in calendar operations.
+    
+    Args:
+        func: Function to decorate
+        
+    Returns:
+        Decorated function with error handling
+    """
+    @functools.wraps(func)
+    def wrapper(*args: Any, **kwargs: Any) -> T:
+        try:
+            return func(*args, **kwargs)
+        except ValueError as e:
+            logger.error(f"Invalid input in {func.__name__}: {str(e)}")
+            raise
+        except Exception as e:
+            logger.error(f"Error in {func.__name__}: {str(e)}")
+            raise CalendarError(f"Calendar operation failed: {str(e)}")
+    return cast(Callable[..., T], wrapper)
+
+class CalendarError(Exception):
+    """Base exception for calendar-related errors."""
+    pass
+
+class ContentError(CalendarError):
+    """Exception for content-related errors."""
+    pass
+
+class SchedulingError(CalendarError):
+    """Exception for scheduling-related errors."""
+    pass
+
+class ValidationError(CalendarError):
+    """Exception for validation-related errors."""
+    pass
+
+def validate_date_range(
+    start_date: datetime,
+    end_date: datetime
+) -> None:
+    """
+    Validate date range for calendar operations.
+    
+    Args:
+        start_date: Start date
+        end_date: End date
+        
+    Raises:
+        ValidationError: If date range is invalid
+    """
+    if not isinstance(start_date, datetime):
+        raise ValidationError("Start date must be a datetime object")
+    
+    if not isinstance(end_date, datetime):
+        raise ValidationError("End date must be a datetime object")
+    
+    if start_date > end_date:
+        raise ValidationError("Start date must be before end date")
+    
+    if (end_date - start_date).days > 365:
+        raise ValidationError("Calendar duration cannot exceed one year")
+
+def validate_content_item(content: dict) -> None:
+    """
+    Validate content item structure.
+    
+    Args:
+        content: Content item to validate
+        
+    Raises:
+        ValidationError: If content item is invalid
+    """
+    required_fields = ['title', 'description', 'content_type', 'platforms']
+    
+    for field in required_fields:
+        if field not in content:
+            raise ValidationError(f"Missing required field: {field}")
+    
+    if not isinstance(content['platforms'], list):
+        raise ValidationError("Platforms must be a list")
+    
+    if not content['platforms']:
+        raise ValidationError("At least one platform must be specified")
+
+def validate_calendar_duration(duration: str) -> None:
+    """
+    Validate calendar duration.
+    
+    Args:
+        duration: Duration to validate ('weekly', 'monthly', 'quarterly')
+        
+    Raises:
+        ValidationError: If duration is invalid
+    """
+    valid_durations = ['weekly', 'monthly', 'quarterly']
+    
+    if duration not in valid_durations:
+        raise ValidationError(
+            f"Invalid duration: {duration}. "
+            f"Must be one of: {', '.join(valid_durations)}"
+        )
+
+def log_calendar_operation(
+    operation: str,
+    details: dict
+) -> None:
+    """
+    Log calendar operation details.
+    
+    Args:
+        operation: Name of the operation
+        details: Operation details to log
+    """
+    logger.info(f"Calendar operation: {operation}")
+    logger.debug(f"Operation details: {details}")
+
+def handle_api_error(
+    error: Exception,
+    operation: str
+) -> None:
+    """
+    Handle API-related errors.
+    
+    Args:
+        error: The error that occurred
+        operation: The operation that failed
+    """
+    logger.error(f"API error in {operation}: {str(error)}")
+    raise CalendarError(f"API operation failed: {str(error)}")
+
+def handle_integration_error(
+    error: Exception,
+    integration: str
+) -> None:
+    """
+    Handle integration-related errors.
+    
+    Args:
+        error: The error that occurred
+        integration: The integration that failed
+    """
+    logger.error(f"Integration error with {integration}: {str(error)}")
+    raise CalendarError(f"Integration failed: {str(error)}") 

lib/ai_seo_tools/content_gap_analysis/README.md

@@ -0,0 +1,182 @@
+# Content Gap Analysis Tool
+
+A comprehensive AI-powered tool for analyzing content gaps and generating strategic content recommendations.
+
+## Overview
+
+The Content Gap Analysis tool combines multiple SEO tools to provide a complete analysis of your content strategy, identify opportunities, and generate actionable recommendations. It leverages existing AI SEO tools and adds new capabilities for comprehensive content analysis.
+
+## Workflow Design
+
+### 1. Website Analysis
+**Input:** Website URL
+**Tools Integration:**
+- `analyze_onpage_seo()`: Analyze content quality and structure
+- `url_seo_checker()`: Check technical SEO aspects
+- `google_pagespeed_insights()`: Assess page performance
+
+**Analysis Components:**
+- Content structure mapping
+- Topic categorization
+- Content depth assessment
+- Performance metrics
+
+### 2. Competitor Analysis
+**Input:** Competitor URLs
+**Tools Integration:**
+- `url_seo_checker()`: Analyze competitor URLs
+- `analyze_onpage_seo()`: Compare content quality
+- `ai_title_generator()`: Analyze title patterns
+
+**Analysis Components:**
+- Content strategy comparison
+- Topic coverage gaps
+- Content format analysis
+- Title pattern analysis
+
+### 3. Keyword Research
+**Input:** Industry/Niche
+**Tools Integration:**
+- `ai_title_generator()`: Generate keyword-based titles
+- `metadesc_generator_main()`: Analyze meta descriptions for keyword usage
+- `ai_structured_data()`: Check structured data implementation
+
+**Analysis Components:**
+- Keyword opportunity identification
+- Search intent analysis
+- Content format suggestions
+- Topic clustering
+
+### 4. AI-Powered Recommendations
+**Tools Integration:**
+- `ai_title_generator()`: Generate content titles
+- `metadesc_generator_main()`: Create content summaries
+- `ai_structured_data()`: Suggest structured data implementation
+
+**Output Components:**
+- Content topic suggestions
+- Format recommendations
+- Priority scoring
+- Implementation timeline
+
+## Implementation Plan
+
+### Phase 1: Core Infrastructure
+1. Create base classes and interfaces
+2. Implement data collection modules
+3. Set up AI model integration
+4. Develop data storage system
+
+### Phase 2: Tool Integration
+1. Integrate existing SEO tools
+2. Create unified API for tool interaction
+3. Implement data sharing between tools
+4. Develop result aggregation system
+
+### Phase 3: Analysis Engine
+1. Implement content structure analysis
+2. Develop competitor analysis algorithms
+3. Create keyword research system
+4. Build recommendation engine
+
+### Phase 4: UI/UX Development
+1. Create step-by-step workflow interface
+2. Implement progress tracking
+3. Develop visualization components
+4. Add export functionality
+
+## Technical Requirements
+
+### Dependencies
+- Existing SEO tools from `lib/ai_seo_tools/`
+- AI models for content analysis
+- Web scraping capabilities
+- Data storage system
+
+### File Structure
+```
+content_gap_analysis/
+├── __init__.py
+├── main.py
+├── website_analyzer.py
+├── competitor_analyzer.py
+├── keyword_researcher.py
+├── recommendation_engine.py
+├── utils/
+│   ├── __init__.py
+│   ├── data_collector.py
+│   ├── content_parser.py
+│   └── ai_processor.py
+└── tests/
+    ├── __init__.py
+    ├── test_website_analyzer.py
+    ├── test_competitor_analyzer.py
+    └── test_keyword_researcher.py
+```
+
+## Integration Points
+
+### Existing Tools
+1. **On-Page SEO Analyzer**
+   - Function: `analyze_onpage_seo()`
+   - Purpose: Content quality assessment
+   - Integration: Content structure analysis
+
+2. **URL SEO Checker**
+   - Function: `url_seo_checker()`
+   - Purpose: Technical optimization
+   - Integration: URL structure analysis
+
+3. **Blog Title Generator**
+   - Function: `ai_title_generator()`
+   - Purpose: Content ideas
+   - Integration: Keyword analysis
+
+4. **Meta Description Generator**
+   - Function: `metadesc_generator_main()`
+   - Purpose: Content summaries
+   - Integration: Content optimization
+
+5. **Structured Data Generator**
+   - Function: `ai_structured_data()`
+   - Purpose: Rich snippets
+   - Integration: Content enhancement
+
+### New Components
+1. **Content Structure Analyzer**
+   - Purpose: Map website content structure
+   - Output: Content hierarchy and relationships
+
+2. **Competitor Content Analyzer**
+   - Purpose: Analyze competitor content strategy
+   - Output: Content gaps and opportunities
+
+3. **Keyword Opportunity Finder**
+   - Purpose: Identify keyword gaps
+   - Output: Keyword recommendations
+
+4. **AI Recommendation Engine**
+   - Purpose: Generate content recommendations
+   - Output: Actionable content strategy
+
+## Future Enhancements
+
+1. **Advanced Analytics**
+   - Content performance tracking
+   - ROI analysis
+   - Trend prediction
+
+2. **Automation Features**
+   - Automated content planning
+   - Schedule generation
+   - Priority scoring
+
+3. **Integration Expansion**
+   - CMS integration
+   - Analytics platform connection
+   - Social media analysis
+
+4. **AI Improvements**
+   - Advanced topic modeling
+   - Sentiment analysis
+   - Content quality scoring 

lib/ai_seo_tools/content_gap_analysis/__init__.py

@@ -0,0 +1,36 @@
+"""
+Content Gap Analysis Tool for Alwrity.
+"""
+
+from .ui import ContentGapAnalysisUI
+from .main import ContentGapAnalysis
+from .keyword_researcher import KeywordResearcher
+from .competitor_analyzer import CompetitorAnalyzer
+from .website_analyzer import WebsiteAnalyzer
+from .recommendation_engine import RecommendationEngine
+from .utils.ai_processor import AIProcessor
+
+__all__ = [
+    'ContentGapAnalysisUI',
+    'ContentGapAnalysis',
+    'KeywordResearcher',
+    'CompetitorAnalyzer',
+    'WebsiteAnalyzer',
+    'RecommendationEngine',
+    'AIProcessor'
+]
+
+def run_content_gap_analysis():
+    """Run the Content Gap Analysis tool."""
+    # Initialize the UI with proper configuration
+    ui = ContentGapAnalysisUI()
+    
+    # Set up the page configuration
+    st.set_page_config(
+        page_title="Content Gap Analysis",
+        page_icon="📊",
+        layout="wide"
+    )
+    
+    # Run the UI
+    ui.run() 

lib/ai_seo_tools/content_gap_analysis/competitor_analyzer.py

@@ -0,0 +1,711 @@
+"""
+Competitor analyzer for content gap analysis.
+"""
+
+from typing import Dict, Any, List, Optional
+import streamlit as st
+from collections import Counter, defaultdict
+from loguru import logger
+from lib.utils.website_analyzer.analyzer import WebsiteAnalyzer
+from lib.ai_seo_tools.content_gap_analysis.utils.data_collector import DataCollector
+from lib.ai_seo_tools.content_gap_analysis.utils.content_parser import ContentParser
+from lib.ai_seo_tools.content_gap_analysis.utils.ai_processor import AIProcessor, ProgressTracker
+import asyncio
+import sys
+import os
+import json
+from lib.gpt_providers.text_generation.main_text_generation import llm_text_gen
+
+# Configure logger
+logger.remove()  # Remove default handler
+logger.add(
+    "logs/competitor_analyzer.log",
+    rotation="50 MB",
+    retention="10 days",
+    level="DEBUG",
+    format="{time:YYYY-MM-DD HH:mm:ss} | {level} | {message}"
+)
+logger.add(
+    sys.stdout,
+    level="INFO",
+    format="<green>{time:YYYY-MM-DD HH:mm:ss}</green> | <level>{level: <8}</level> | <cyan>{message}</cyan>"
+)
+
+# Ensure logs directory exists
+os.makedirs("logs", exist_ok=True)
+
+class CompetitorAnalyzer:
+    """Analyzes competitor content and market position."""
+    
+    def __init__(self):
+        """Initialize the competitor analyzer."""
+        self.website_analyzer = WebsiteAnalyzer()
+        self.ai_processor = AIProcessor()
+        self.progress = ProgressTracker()
+        
+        # Define analysis stages
+        self.stages = {
+            'competitor_analysis': {
+                'name': 'Competitor Analysis',
+                'steps': [
+                    'Initializing competitor analysis',
+                    'Analyzing competitor content',
+                    'Evaluating market position',
+                    'Identifying content gaps',
+                    'Generating competitive insights'
+                ]
+            }
+        }
+        
+        logger.info("CompetitorAnalyzer initialized")
+    
+    def analyze(self, competitor_urls: List[str], industry: str) -> Dict[str, Any]:
+        """
+        Analyze competitor websites.
+        
+        Args:
+            competitor_urls: List of competitor URLs to analyze
+            industry: Industry category
+            
+        Returns:
+            Dictionary containing competitor analysis results
+        """
+        try:
+            results = {
+                'competitors': [],
+                'market_position': {},
+                'content_gaps': [],
+                'advantages': []
+            }
+            
+            # Analyze each competitor
+            for url in competitor_urls:
+                competitor_analysis = self.website_analyzer.analyze_website(url)
+                if competitor_analysis.get('success', False):
+                    results['competitors'].append({
+                        'url': url,
+                        'analysis': competitor_analysis['data']
+                    })
+            
+            # Generate market position analysis using AI
+            prompt = f"""Analyze the market position of competitors in the {industry} industry:
+            
+            Competitor Analyses:
+            {json.dumps(results['competitors'], indent=2)}
+            
+            Provide:
+            1. Market position analysis
+            2. Content gaps
+            3. Competitive advantages
+            
+            Format the response as JSON with 'market_position', 'content_gaps', and 'advantages' keys."""
+            
+            # Get AI analysis
+            analysis = llm_text_gen(
+                prompt=prompt,
+                system_prompt="You are an SEO expert specializing in competitive analysis.",
+                response_format="json_object"
+            )
+            
+            if analysis:
+                results['market_position'] = analysis.get('market_position', {})
+                results['content_gaps'] = analysis.get('content_gaps', [])
+                results['advantages'] = analysis.get('advantages', [])
+            
+            return results
+            
+        except Exception as e:
+            error_msg = f"Error analyzing competitors: {str(e)}"
+            logger.error(error_msg, exc_info=True)
+            return {
+                'error': error_msg,
+                'competitors': [],
+                'market_position': {},
+                'content_gaps': [],
+                'advantages': []
+            }
+    
+    def _analyze_competitor_content(self, competitor_urls: List[str]) -> Dict[str, Any]:
+        """Analyze competitor content."""
+        try:
+            content_analysis = {}
+            
+            for url in competitor_urls:
+                # Get AI analysis for each competitor
+                analysis = self.ai_processor.analyze_content({
+                    'url': url,
+                    'content': {}  # Content will be fetched by AI processor
+                })
+                
+                content_analysis[url] = {
+                    'content_metrics': analysis.get('content_metrics', {}),
+                    'content_evolution': analysis.get('content_evolution', {}),
+                    'topic_trends': analysis.get('topic_trends', {}),
+                    'performance_trends': analysis.get('performance_trends', {})
+                }
+            
+            return content_analysis
+        except Exception as e:
+            st.error(f"Error analyzing competitor content: {str(e)}")
+            return {}
+    
+    def _evaluate_market_position(self, content_analysis: Dict[str, Any], industry: str) -> Dict[str, Any]:
+        """Evaluate market position."""
+        try:
+            market_position = {
+                'industry_rank': 0,
+                'content_quality_rank': 0,
+                'market_share': 0,
+                'competitive_advantages': [],
+                'competitive_disadvantages': []
+            }
+            
+            # Calculate industry rank based on content quality
+            content_quality_scores = [
+                analysis.get('content_metrics', {}).get('quality_score', 0)
+                for analysis in content_analysis.values()
+            ]
+            
+            if content_quality_scores:
+                market_position['content_quality_rank'] = sum(content_quality_scores) / len(content_quality_scores)
+            
+            # Identify competitive advantages and disadvantages
+            for url, analysis in content_analysis.items():
+                quality_score = analysis.get('content_metrics', {}).get('quality_score', 0)
+                
+                if quality_score > market_position['content_quality_rank']:
+                    market_position['competitive_advantages'].append({
+                        'url': url,
+                        'advantage': 'Higher content quality',
+                        'score': quality_score
+                    })
+                elif quality_score < market_position['content_quality_rank']:
+                    market_position['competitive_disadvantages'].append({
+                        'url': url,
+                        'disadvantage': 'Lower content quality',
+                        'score': quality_score
+                    })
+            
+            return market_position
+        except Exception as e:
+            st.error(f"Error evaluating market position: {str(e)}")
+            return {}
+    
+    def _identify_content_gaps(self, content_analysis: Dict[str, Any]) -> List[Dict[str, Any]]:
+        """Identify content gaps."""
+        try:
+            content_gaps = []
+            
+            # Analyze content coverage
+            all_topics = set()
+            for analysis in content_analysis.values():
+                topics = analysis.get('topic_trends', {}).get('topics', [])
+                all_topics.update(topics)
+            
+            # Identify missing topics for each competitor
+            for url, analysis in content_analysis.items():
+                covered_topics = set(analysis.get('topic_trends', {}).get('topics', []))
+                missing_topics = all_topics - covered_topics
+                
+                if missing_topics:
+                    content_gaps.append({
+                        'url': url,
+                        'missing_topics': list(missing_topics),
+                        'gap_type': 'topic_coverage'
+                    })
+            
+            return content_gaps
+        except Exception as e:
+            st.error(f"Error identifying content gaps: {str(e)}")
+            return []
+    
+    def _generate_competitive_insights(self, content_analysis: Dict[str, Any], market_position: Dict[str, Any], content_gaps: List[Dict[str, Any]]) -> List[str]:
+        """Generate competitive insights."""
+        try:
+            insights = []
+            
+            # Market position insights
+            if market_position.get('content_quality_rank', 0) > 80:
+                insights.append("Strong market position with high content quality")
+            elif market_position.get('content_quality_rank', 0) > 60:
+                insights.append("Moderate market position with room for improvement")
+            else:
+                insights.append("Weak market position requiring significant improvement")
+            
+            # Content gap insights
+            if content_gaps:
+                insights.append(f"Identified {len(content_gaps)} content gaps across competitors")
+            
+            # Competitive advantage insights
+            if market_position.get('competitive_advantages'):
+                insights.append(f"Found {len(market_position['competitive_advantages'])} competitive advantages")
+            
+            return insights
+        except Exception as e:
+            st.error(f"Error generating competitive insights: {str(e)}")
+            return []
+    
+    def _run_seo_analysis(self, url: str) -> dict:
+        """
+        Run SEO analysis on competitor website.
+        
+        Args:
+            url (str): The URL to analyze
+            
+        Returns:
+            dict: SEO analysis results
+        """
+        # Run website analysis using the new analyzer
+        analysis = self.website_analyzer.analyze_website(url)
+        
+        if not analysis.get('success', False):
+            return {
+                'error': analysis.get('error', 'Unknown error in SEO analysis'),
+                'onpage_seo': {},
+                'url_seo': {}
+            }
+        
+        # Extract SEO information from the analysis
+        seo_info = analysis['data']['analysis']['seo_info']
+        basic_info = analysis['data']['analysis']['basic_info']
+        
+        return {
+            'onpage_seo': {
+                'meta_tags': seo_info.get('meta_tags', {}),
+                'content': seo_info.get('content', {}),
+                'recommendations': seo_info.get('recommendations', [])
+            },
+            'url_seo': {
+                'title': basic_info.get('title', ''),
+                'meta_description': basic_info.get('meta_description', ''),
+                'has_robots_txt': bool(basic_info.get('robots_txt')),
+                'has_sitemap': bool(basic_info.get('sitemap'))
+            }
+        }
+    
+    def _analyze_title_patterns(self, url: str) -> dict:
+        """
+        Analyze title patterns using the title generator.
+        
+        Args:
+            url (str): The URL to analyze
+            
+        Returns:
+            dict: Title pattern analysis results
+        """
+        # Use title generator to analyze patterns
+        title_analysis = ai_title_generator(url)
+        
+        return {
+            'patterns': title_analysis.get('patterns', {}),
+            'suggestions': title_analysis.get('suggestions', [])
+        }
+    
+    def _compare_competitors(self, results: dict) -> dict:
+        """
+        Compare results across all competitors.
+        
+        Args:
+            results (dict): Analysis results for all competitors
+            
+        Returns:
+            dict: Comparative analysis results
+        """
+        comparison = {
+            'content_comparison': self._compare_content(results),
+            'seo_comparison': self._compare_seo(results),
+            'title_comparison': self._compare_titles(results),
+            'performance_metrics': self._compare_performance(results),
+            'content_gaps': self._identify_content_gaps(results)
+        }
+        
+        # Add AI-enhanced insights
+        comparison['ai_insights'] = self.ai_processor.analyze_competitor_comparison(comparison)
+        
+        return comparison
+    
+    def _compare_content(self, results: dict) -> dict:
+        """Compare content structure across competitors."""
+        content_comparison = {
+            'topic_distribution': self._analyze_topic_distribution(results),
+            'content_depth': self._analyze_content_depth(results),
+            'content_formats': self._analyze_content_formats(results),
+            'content_quality': self._analyze_content_quality(results)
+        }
+        
+        return content_comparison
+    
+    def _analyze_topic_distribution(self, results: dict) -> dict:
+        """Analyze topic distribution across competitors."""
+        all_topics = []
+        topic_frequency = Counter()
+        
+        for url, data in results.items():
+            topics = data['content_structure'].get('topics', [])
+            all_topics.extend([t['topic'] for t in topics])
+            topic_frequency.update([t['topic'] for t in topics])
+        
+        return {
+            'common_topics': [topic for topic, count in topic_frequency.most_common(10)],
+            'unique_topics': list(set(all_topics)),
+            'topic_frequency': dict(topic_frequency.most_common()),
+            'topic_coverage': len(set(all_topics)) / len(all_topics) if all_topics else 0
+        }
+    
+    def _analyze_content_depth(self, results: dict) -> dict:
+        """Analyze content depth across competitors."""
+        depth_metrics = {
+            'word_counts': {},
+            'section_counts': {},
+            'heading_distribution': defaultdict(list),
+            'content_hierarchy': {}
+        }
+        
+        for url, data in results.items():
+            content_structure = data['content_structure']
+            
+            # Word count analysis
+            depth_metrics['word_counts'][url] = content_structure.get('text_statistics', {}).get('word_count', 0)
+            
+            # Section analysis
+            depth_metrics['section_counts'][url] = len(content_structure.get('sections', []))
+            
+            # Heading distribution
+            for level, count in content_structure.get('hierarchy', {}).get('heading_distribution', {}).items():
+                depth_metrics['heading_distribution'][level].append(count)
+            
+            # Content hierarchy
+            depth_metrics['content_hierarchy'][url] = content_structure.get('hierarchy', {})
+        
+        return depth_metrics
+    
+    def _analyze_content_formats(self, results: dict) -> dict:
+        """Analyze content formats across competitors."""
+        format_analysis = {
+            'format_types': defaultdict(int),
+            'format_distribution': defaultdict(list),
+            'format_effectiveness': {}
+        }
+        
+        for url, data in results.items():
+            sections = data['content_structure'].get('sections', [])
+            
+            for section in sections:
+                format_type = section.get('type', 'unknown')
+                format_analysis['format_types'][format_type] += 1
+                format_analysis['format_distribution'][format_type].append({
+                    'url': url,
+                    'heading': section.get('heading', ''),
+                    'word_count': section.get('word_count', 0)
+                })
+        
+        return format_analysis
+    
+    def _analyze_content_quality(self, results: dict) -> dict:
+        """Analyze content quality across competitors."""
+        quality_metrics = {
+            'readability_scores': {},
+            'content_structure_scores': {},
+            'engagement_metrics': {},
+            'overall_quality': {}
+        }
+        
+        for url, data in results.items():
+            content_structure = data['content_structure']
+            
+            # Readability analysis
+            readability = content_structure.get('readability', {})
+            quality_metrics['readability_scores'][url] = {
+                'flesch_score': readability.get('flesch_score', 0),
+                'avg_sentence_length': readability.get('avg_sentence_length', 0),
+                'avg_word_length': readability.get('avg_word_length', 0)
+            }
+            
+            # Structure analysis
+            hierarchy = content_structure.get('hierarchy', {})
+            quality_metrics['content_structure_scores'][url] = {
+                'has_proper_hierarchy': hierarchy.get('has_proper_hierarchy', False),
+                'heading_distribution': hierarchy.get('heading_distribution', {}),
+                'max_depth': hierarchy.get('max_depth', 0)
+            }
+        
+        return quality_metrics
+    
+    def _compare_seo(self, results: dict) -> dict:
+        """Compare SEO metrics across competitors."""
+        seo_comparison = {
+            'onpage_metrics': defaultdict(list),
+            'technical_metrics': defaultdict(list),
+            'content_metrics': defaultdict(list),
+            'overall_seo_score': {}
+        }
+        
+        for url, data in results.items():
+            seo_info = data.get('website_analysis', {}).get('analysis', {}).get('seo_info', {})
+            
+            # On-page SEO metrics
+            meta_tags = seo_info.get('meta_tags', {})
+            seo_comparison['onpage_metrics']['title_score'].append(
+                100 if meta_tags.get('title', {}).get('status') == 'good' else 50
+            )
+            seo_comparison['onpage_metrics']['description_score'].append(
+                100 if meta_tags.get('description', {}).get('status') == 'good' else 50
+            )
+            seo_comparison['onpage_metrics']['keywords_score'].append(
+                100 if meta_tags.get('keywords', {}).get('status') == 'good' else 50
+            )
+            
+            # Technical SEO metrics
+            technical = data.get('website_analysis', {}).get('analysis', {}).get('basic_info', {})
+            seo_comparison['technical_metrics']['has_robots_txt'].append(
+                100 if technical.get('robots_txt') else 0
+            )
+            seo_comparison['technical_metrics']['has_sitemap'].append(
+                100 if technical.get('sitemap') else 0
+            )
+            
+            # Content SEO metrics
+            content = seo_info.get('content', {})
+            seo_comparison['content_metrics']['readability_score'].append(
+                content.get('readability_score', 0)
+            )
+            seo_comparison['content_metrics']['content_quality_score'].append(
+                content.get('content_quality_score', 0)
+            )
+            
+            # Overall SEO score
+            seo_comparison['overall_seo_score'][url] = seo_info.get('overall_score', 0)
+        
+        return seo_comparison
+    
+    def _compare_titles(self, results: dict) -> dict:
+        """Compare title patterns across competitors."""
+        title_comparison = {
+            'pattern_distribution': defaultdict(int),
+            'length_distribution': defaultdict(list),
+            'keyword_usage': defaultdict(int),
+            'format_preferences': defaultdict(int)
+        }
+        
+        for url, data in results.items():
+            title_patterns = data['title_patterns']
+            
+            # Pattern analysis
+            for pattern in title_patterns.get('patterns', {}):
+                title_comparison['pattern_distribution'][pattern] += 1
+            
+            # Length analysis
+            for suggestion in title_patterns.get('suggestions', []):
+                title_comparison['length_distribution'][len(suggestion)].append(suggestion)
+            
+            # Keyword analysis
+            for suggestion in title_patterns.get('suggestions', []):
+                words = suggestion.lower().split()
+                for word in words:
+                    if len(word) > 3:  # Filter out short words
+                        title_comparison['keyword_usage'][word] += 1
+        
+        return title_comparison
+    
+    def _compare_performance(self, results: dict) -> dict:
+        """Compare performance metrics across competitors."""
+        performance_metrics = {
+            'content_effectiveness': {},
+            'engagement_metrics': {},
+            'technical_performance': {},
+            'overall_performance': {}
+        }
+        
+        for url, data in results.items():
+            # Content effectiveness
+            content_structure = data['content_structure']
+            performance_metrics['content_effectiveness'][url] = {
+                'content_depth': content_structure.get('text_statistics', {}).get('word_count', 0),
+                'content_quality': content_structure.get('readability', {}).get('flesch_score', 0),
+                'content_structure': content_structure.get('hierarchy', {}).get('has_proper_hierarchy', False)
+            }
+            
+            # Technical performance
+            seo_analysis = data['seo_analysis']
+            performance_metrics['technical_performance'][url] = {
+                'onpage_score': sum(1 for v in seo_analysis.get('onpage_seo', {}).values() if v),
+                'technical_score': sum(1 for v in seo_analysis.get('url_seo', {}).values() if v)
+            }
+        
+        return performance_metrics
+    
+    def _find_missing_topics(self, results: dict) -> List[Dict[str, Any]]:
+        """Find topics that are missing or underrepresented."""
+        all_topics = set()
+        topic_coverage = defaultdict(int)
+        
+        # Collect all topics and their coverage
+        for url, data in results.items():
+            topics = data['content_structure'].get('topics', [])
+            for topic in topics:
+                all_topics.add(topic['topic'])
+                topic_coverage[topic['topic']] += 1
+        
+        # Identify missing or underrepresented topics
+        missing_topics = []
+        total_competitors = len(results)
+        
+        for topic in all_topics:
+            coverage = topic_coverage[topic] / total_competitors
+            if coverage < 0.5:  # Topic covered by less than 50% of competitors
+                missing_topics.append({
+                    'topic': topic,
+                    'coverage': coverage,
+                    'opportunity_score': 1 - coverage
+                })
+        
+        return sorted(missing_topics, key=lambda x: x['opportunity_score'], reverse=True)
+    
+    def _identify_opportunities(self, results: dict) -> List[Dict[str, Any]]:
+        """Identify content opportunities based on analysis."""
+        opportunities = []
+        
+        # Analyze content depth opportunities
+        depth_metrics = self._analyze_content_depth(results)
+        avg_word_count = sum(depth_metrics['word_counts'].values()) / len(depth_metrics['word_counts'])
+        
+        for url, word_count in depth_metrics['word_counts'].items():
+            if word_count < avg_word_count * 0.7:  # Content depth significantly below average
+                opportunities.append({
+                    'type': 'content_depth',
+                    'url': url,
+                    'current_value': word_count,
+                    'target_value': avg_word_count,
+                    'opportunity_score': (avg_word_count - word_count) / avg_word_count
+                })
+        
+        # Analyze format opportunities
+        format_analysis = self._analyze_content_formats(results)
+        for format_type, distribution in format_analysis['format_distribution'].items():
+            if len(distribution) < len(results) * 0.3:  # Format used by less than 30% of competitors
+                opportunities.append({
+                    'type': 'content_format',
+                    'format': format_type,
+                    'current_coverage': len(distribution) / len(results),
+                    'opportunity_score': 1 - (len(distribution) / len(results))
+                })
+        
+        return sorted(opportunities, key=lambda x: x['opportunity_score'], reverse=True)
+    
+    def _analyze_format_gaps(self, results: dict) -> List[Dict[str, Any]]:
+        """Analyze gaps in content formats."""
+        format_gaps = []
+        format_analysis = self._analyze_content_formats(results)
+        
+        # Identify underutilized formats
+        for format_type, count in format_analysis['format_types'].items():
+            if count < len(results) * 0.3:  # Format used by less than 30% of competitors
+                format_gaps.append({
+                    'format': format_type,
+                    'current_usage': count,
+                    'potential_impact': 'high' if count < len(results) * 0.2 else 'medium',
+                    'suggested_implementation': self._generate_format_suggestions(format_type)
+                })
+        
+        return format_gaps
+    
+    def _analyze_quality_gaps(self, results: dict) -> List[Dict[str, Any]]:
+        """Analyze gaps in content quality."""
+        quality_gaps = []
+        quality_metrics = self._analyze_content_quality(results)
+        
+        # Analyze readability gaps
+        readability_scores = quality_metrics['readability_scores']
+        avg_flesch = sum(score['flesch_score'] for score in readability_scores.values()) / len(readability_scores)
+        
+        for url, scores in readability_scores.items():
+            if scores['flesch_score'] < avg_flesch * 0.8:  # Readability significantly below average
+                quality_gaps.append({
+                    'type': 'readability',
+                    'url': url,
+                    'current_score': scores['flesch_score'],
+                    'target_score': avg_flesch,
+                    'improvement_needed': avg_flesch - scores['flesch_score']
+                })
+        
+        return quality_gaps
+    
+    def _analyze_seo_gaps(self, results: dict) -> List[Dict[str, Any]]:
+        """Analyze gaps in SEO implementation."""
+        seo_gaps = []
+        seo_comparison = self._compare_seo(results)
+        
+        # Analyze on-page SEO gaps
+        for metric, values in seo_comparison['onpage_metrics'].items():
+            avg_value = sum(values) / len(values)
+            for url, value in zip(results.keys(), values):
+                if value < avg_value * 0.7:  # Significantly below average
+                    seo_gaps.append({
+                        'type': 'onpage_seo',
+                        'metric': metric,
+                        'url': url,
+                        'current_value': value,
+                        'target_value': avg_value,
+                        'improvement_needed': avg_value - value
+                    })
+        
+        # Analyze technical SEO gaps
+        for metric, values in seo_comparison['technical_metrics'].items():
+            avg_value = sum(values) / len(values)
+            for url, value in zip(results.keys(), values):
+                if value < avg_value * 0.7:  # Significantly below average
+                    seo_gaps.append({
+                        'type': 'technical_seo',
+                        'metric': metric,
+                        'url': url,
+                        'current_value': value,
+                        'target_value': avg_value,
+                        'improvement_needed': avg_value - value
+                    })
+        
+        # Analyze content SEO gaps
+        for metric, values in seo_comparison['content_metrics'].items():
+            avg_value = sum(values) / len(values)
+            for url, value in zip(results.keys(), values):
+                if value < avg_value * 0.7:  # Significantly below average
+                    seo_gaps.append({
+                        'type': 'content_seo',
+                        'metric': metric,
+                        'url': url,
+                        'current_value': value,
+                        'target_value': avg_value,
+                        'improvement_needed': avg_value - value
+                    })
+        
+        return seo_gaps
+    
+    def _generate_format_suggestions(self, format_type: str) -> List[str]:
+        """Generate suggestions for implementing specific content formats."""
+        format_suggestions = {
+            'article': [
+                'Create in-depth articles with comprehensive coverage',
+                'Include expert quotes and statistics',
+                'Add visual elements and infographics'
+            ],
+            'blog_post': [
+                'Write engaging blog posts with personal insights',
+                'Include call-to-actions',
+                'Add social sharing buttons'
+            ],
+            'how-to': [
+                'Create step-by-step guides',
+                'Include screenshots or videos',
+                'Add troubleshooting sections'
+            ],
+            'case_study': [
+                'Present real-world examples',
+                'Include metrics and results',
+                'Add client testimonials'
+            ]
+        }
+        
+        return format_suggestions.get(format_type, [
+            'Research successful examples',
+            'Analyze competitor implementation',
+            'Create unique value proposition'
+        ]) 

lib/ai_seo_tools/content_gap_analysis/keyword_researcher.py

@@ -0,0 +1,649 @@
+"""
+Keyword researcher for content gap analysis.
+"""
+
+from typing import Dict, Any, List, Optional
+import streamlit as st
+from loguru import logger
+from lib.utils.website_analyzer.analyzer import WebsiteAnalyzer
+from lib.ai_seo_tools.content_gap_analysis.utils.data_collector import DataCollector
+from lib.ai_seo_tools.content_gap_analysis.utils.content_parser import ContentParser
+from lib.ai_seo_tools.content_gap_analysis.utils.ai_processor import AIProcessor, ProgressTracker
+import asyncio
+import sys
+import os
+import json
+from lib.gpt_providers.text_generation.main_text_generation import llm_text_gen
+from lib.ai_seo_tools.content_title_generator import ai_title_generator
+from lib.ai_seo_tools.meta_desc_generator import metadesc_generator_main
+from lib.ai_seo_tools.seo_structured_data import ai_structured_data
+
+# Configure logger
+logger.remove()  # Remove default handler
+logger.add(
+    "logs/keyword_researcher.log",
+    rotation="50 MB",
+    retention="10 days",
+    level="DEBUG",
+    format="{time:YYYY-MM-DD HH:mm:ss} | {level} | {message}"
+)
+logger.add(
+    sys.stdout,
+    level="INFO",
+    format="<green>{time:YYYY-MM-DD HH:mm:ss}</green> | <level>{level: <8}</level> | <cyan>{message}</cyan>"
+)
+
+# Ensure logs directory exists
+os.makedirs("logs", exist_ok=True)
+
+class KeywordResearcher:
+    """Researches and analyzes keywords for content strategy."""
+    
+    def __init__(self):
+        """Initialize the keyword researcher."""
+        self.ai_processor = AIProcessor()
+        self.progress = ProgressTracker()
+        
+        # Define analysis stages
+        self.stages = {
+            'keyword_analysis': {
+                'name': 'Keyword Analysis',
+                'steps': [
+                    'Initializing keyword research',
+                    'Analyzing keyword trends',
+                    'Evaluating search intent',
+                    'Identifying opportunities',
+                    'Generating keyword insights'
+                ]
+            }
+        }
+    
+    def analyze(self, industry: str, url: str) -> Dict[str, Any]:
+        """
+        Analyze keywords for content strategy.
+        
+        Args:
+            industry: Industry category
+            url: Target website URL
+            
+        Returns:
+            Dictionary containing analysis results
+        """
+        try:
+            self.progress.start_stage('keyword_analysis')
+            self.progress.next_step()
+            
+            # Analyze keyword trends
+            trend_analysis = self._analyze_keyword_trends(industry)
+            self.progress.next_step()
+            
+            # Evaluate search intent
+            intent_analysis = self._evaluate_search_intent(trend_analysis)
+            self.progress.next_step()
+            
+            # Identify opportunities
+            opportunities = self._identify_opportunities(trend_analysis, intent_analysis)
+            self.progress.next_step()
+            
+            # Generate insights
+            insights = self._generate_keyword_insights(trend_analysis, intent_analysis, opportunities)
+            self.progress.next_step()
+            
+            self.progress.complete_stage()
+            
+            return {
+                'trend_analysis': trend_analysis,
+                'intent_analysis': intent_analysis,
+                'opportunities': opportunities,
+                'insights': insights
+            }
+            
+        except Exception as e:
+            if self.progress.current_stage:
+                self.progress.update_progress(0, f"Error in {self.progress.stages[self.progress.current_stage]['name']}: {str(e)}")
+            st.error(f"Error analyzing keywords: {str(e)}")
+            return {
+                'error': str(e),
+                'trend_analysis': {},
+                'intent_analysis': {},
+                'opportunities': [],
+                'insights': []
+            }
+    
+    def _analyze_keyword_trends(self, industry: str) -> Dict[str, Any]:
+        """Analyze keyword trends."""
+        try:
+            # Get AI analysis for keyword trends
+            analysis = self.ai_processor.analyze_keywords({
+                'industry': industry,
+                'keywords': {}  # Keywords will be fetched by AI processor
+            })
+            
+            return {
+                'trends': analysis.get('keyword_trends', {}),
+                'search_intent': analysis.get('search_intent', {}),
+                'keyword_insights': analysis.get('keyword_insights', {})
+            }
+        except Exception as e:
+            st.error(f"Error analyzing keyword trends: {str(e)}")
+            return {}
+    
+    def _evaluate_search_intent(self, trend_analysis: Dict[str, Any]) -> Dict[str, Any]:
+        """Evaluate search intent."""
+        try:
+            intent_analysis = {
+                'informational': [],
+                'transactional': [],
+                'navigational': [],
+                'commercial': []
+            }
+            
+            # Categorize keywords by intent
+            for keyword, data in trend_analysis.get('trends', {}).items():
+                intent = data.get('intent', 'informational')
+                if intent in intent_analysis:
+                    intent_analysis[intent].append({
+                        'keyword': keyword,
+                        'volume': data.get('volume', 0),
+                        'difficulty': data.get('difficulty', 0)
+                    })
+            
+            return intent_analysis
+        except Exception as e:
+            st.error(f"Error evaluating search intent: {str(e)}")
+            return {}
+    
+    def _identify_opportunities(self, trend_analysis: Dict[str, Any], intent_analysis: Dict[str, Any]) -> List[Dict[str, Any]]:
+        """Identify keyword opportunities."""
+        try:
+            opportunities = []
+            
+            # Analyze each intent category
+            for intent, keywords in intent_analysis.items():
+                for keyword_data in keywords:
+                    # Calculate opportunity score
+                    volume = keyword_data.get('volume', 0)
+                    difficulty = keyword_data.get('difficulty', 0)
+                    opportunity_score = volume * (1 - difficulty/100)
+                    
+                    if opportunity_score > 50:  # Threshold for good opportunities
+                        opportunities.append({
+                            'keyword': keyword_data['keyword'],
+                            'intent': intent,
+                            'volume': volume,
+                            'difficulty': difficulty,
+                            'opportunity_score': opportunity_score
+                        })
+            
+            # Sort by opportunity score
+            opportunities.sort(key=lambda x: x['opportunity_score'], reverse=True)
+            
+            return opportunities
+        except Exception as e:
+            st.error(f"Error identifying opportunities: {str(e)}")
+            return []
+    
+    def _generate_keyword_insights(self, trend_analysis: Dict[str, Any], intent_analysis: Dict[str, Any], opportunities: List[Dict[str, Any]]) -> List[str]:
+        """Generate keyword insights."""
+        try:
+            insights = []
+            
+            # Trend insights
+            if trend_analysis.get('trends'):
+                insights.append(f"Analyzed {len(trend_analysis['trends'])} keywords for trends")
+            
+            # Intent insights
+            for intent, keywords in intent_analysis.items():
+                if keywords:
+                    insights.append(f"Found {len(keywords)} {intent} keywords")
+            
+            # Opportunity insights
+            if opportunities:
+                insights.append(f"Identified {len(opportunities)} high-potential keyword opportunities")
+            
+            return insights
+        except Exception as e:
+            st.error(f"Error generating keyword insights: {str(e)}")
+            return []
+    
+    def _generate_titles(self, industry: str) -> dict:
+        """
+        Generate keyword-based titles using the title generator.
+        
+        Args:
+            industry (str): The industry to generate titles for
+            
+        Returns:
+            dict: Generated titles and patterns
+        """
+        return ai_title_generator(industry)
+    
+    def _analyze_meta_descriptions(self, industry: str) -> dict:
+        """
+        Analyze meta descriptions for keyword usage.
+        
+        Args:
+            industry (str): The industry to analyze
+            
+        Returns:
+            dict: Meta description analysis results
+        """
+        return metadesc_generator_main(industry)
+    
+    def _analyze_structured_data(self, industry: str) -> dict:
+        """
+        Analyze structured data implementation.
+        
+        Args:
+            industry (str): The industry to analyze
+            
+        Returns:
+            dict: Structured data analysis results
+        """
+        return ai_structured_data(industry)
+    
+    def _extract_keywords(self, titles: dict, meta_analysis: dict) -> list:
+        """
+        Extract keywords from titles and meta descriptions.
+        
+        Args:
+            titles (dict): Generated titles
+            meta_analysis (dict): Meta description analysis
+            
+        Returns:
+            list: Extracted keywords with metrics
+        """
+        prompt = f"""
+        As an SEO expert, analyze the following content and extract relevant keywords with their metrics:
+
+        Titles: {titles}
+        Meta Descriptions: {meta_analysis}
+
+        Please provide a JSON response with the following structure:
+        {{
+            "keywords": [
+                {{
+                    "keyword": "string",
+                    "search_volume": "number",
+                    "difficulty": "number",
+                    "relevance_score": "number",
+                    "content_type": "string"
+                }}
+            ],
+            "summary": {{
+                "total_keywords": "number",
+                "high_opportunity_keywords": "number",
+                "recommended_focus_areas": ["string"]
+            }}
+        }}
+
+        Focus on:
+        1. Primary keywords and their variations
+        2. Long-tail keywords
+        3. Industry-specific terminology
+        4. Search volume and difficulty metrics
+        5. Content type recommendations
+        """
+
+        try:
+            response = llm_text_gen(prompt, json_struct={
+                "type": "object",
+                "properties": {
+                    "keywords": {
+                        "type": "array",
+                        "items": {
+                            "type": "object",
+                            "properties": {
+                                "keyword": {"type": "string"},
+                                "search_volume": {"type": "number"},
+                                "difficulty": {"type": "number"},
+                                "relevance_score": {"type": "number"},
+                                "content_type": {"type": "string"}
+                            }
+                        }
+                    },
+                    "summary": {
+                        "type": "object",
+                        "properties": {
+                            "total_keywords": {"type": "number"},
+                            "high_opportunity_keywords": {"type": "number"},
+                            "recommended_focus_areas": {
+                                "type": "array",
+                                "items": {"type": "string"}
+                            }
+                        }
+                    }
+                }
+            })
+            return response
+        except Exception as e:
+            st.error(f"Error extracting keywords: {e}")
+            return []
+    
+    def _analyze_search_intent(self, ai_insights: dict) -> dict:
+        """
+        Analyze search intent from AI insights.
+        
+        Args:
+            ai_insights (dict): AI-processed insights
+            
+        Returns:
+            dict: Search intent analysis
+        """
+        prompt = f"""
+        As an SEO expert, analyze the following content insights and determine the search intent:
+
+        Content Insights: {ai_insights}
+
+        Please provide a JSON response with the following structure:
+        {{
+            "informational": [
+                {{
+                    "keyword": "string",
+                    "intent_type": "string",
+                    "content_suggestions": ["string"]
+                }}
+            ],
+            "transactional": [
+                {{
+                    "keyword": "string",
+                    "intent_type": "string",
+                    "content_suggestions": ["string"]
+                }}
+            ],
+            "navigational": [
+                {{
+                    "keyword": "string",
+                    "intent_type": "string",
+                    "content_suggestions": ["string"]
+                }}
+            ],
+            "summary": {{
+                "dominant_intent": "string",
+                "content_strategy_recommendations": ["string"]
+            }}
+        }}
+
+        Focus on:
+        1. Identifying primary search intent for each keyword
+        2. Suggesting appropriate content types
+        3. Providing content strategy recommendations
+        4. Analyzing user behavior patterns
+        """
+
+        try:
+            response = llm_text_gen(prompt, json_struct={
+                "type": "object",
+                "properties": {
+                    "informational": {
+                        "type": "array",
+                        "items": {
+                            "type": "object",
+                            "properties": {
+                                "keyword": {"type": "string"},
+                                "intent_type": {"type": "string"},
+                                "content_suggestions": {
+                                    "type": "array",
+                                    "items": {"type": "string"}
+                                }
+                            }
+                        }
+                    },
+                    "transactional": {
+                        "type": "array",
+                        "items": {
+                            "type": "object",
+                            "properties": {
+                                "keyword": {"type": "string"},
+                                "intent_type": {"type": "string"},
+                                "content_suggestions": {
+                                    "type": "array",
+                                    "items": {"type": "string"}
+                                }
+                            }
+                        }
+                    },
+                    "navigational": {
+                        "type": "array",
+                        "items": {
+                            "type": "object",
+                            "properties": {
+                                "keyword": {"type": "string"},
+                                "intent_type": {"type": "string"},
+                                "content_suggestions": {
+                                    "type": "array",
+                                    "items": {"type": "string"}
+                                }
+                            }
+                        }
+                    },
+                    "summary": {
+                        "type": "object",
+                        "properties": {
+                            "dominant_intent": {"type": "string"},
+                            "content_strategy_recommendations": {
+                                "type": "array",
+                                "items": {"type": "string"}
+                            }
+                        }
+                    }
+                }
+            })
+            return response
+        except Exception as e:
+            st.error(f"Error analyzing search intent: {e}")
+            return {
+                'informational': [],
+                'transactional': [],
+                'navigational': []
+            }
+    
+    def _suggest_content_formats(self, ai_insights: dict) -> list:
+        """
+        Suggest content formats based on AI insights.
+        
+        Args:
+            ai_insights (dict): AI-processed insights
+            
+        Returns:
+            list: Suggested content formats
+        """
+        prompt = f"""
+        As a content strategy expert, analyze the following insights and suggest appropriate content formats:
+
+        AI Insights: {ai_insights}
+
+        Please provide a JSON response with the following structure:
+        {{
+            "content_formats": [
+                {{
+                    "format": "string",
+                    "description": "string",
+                    "use_cases": ["string"],
+                    "recommended_topics": ["string"],
+                    "estimated_impact": "string"
+                }}
+            ],
+            "format_strategy": {{
+                "primary_formats": ["string"],
+                "secondary_formats": ["string"],
+                "implementation_priority": ["string"]
+            }}
+        }}
+
+        Focus on:
+        1. Identifying the most effective content formats
+        2. Matching formats to user intent
+        3. Suggesting specific use cases
+        4. Providing implementation guidance
+        """
+
+        try:
+            response = llm_text_gen(prompt, json_struct={
+                "type": "object",
+                "properties": {
+                    "content_formats": {
+                        "type": "array",
+                        "items": {
+                            "type": "object",
+                            "properties": {
+                                "format": {"type": "string"},
+                                "description": {"type": "string"},
+                                "use_cases": {
+                                    "type": "array",
+                                    "items": {"type": "string"}
+                                },
+                                "recommended_topics": {
+                                    "type": "array",
+                                    "items": {"type": "string"}
+                                },
+                                "estimated_impact": {"type": "string"}
+                            }
+                        }
+                    },
+                    "format_strategy": {
+                        "type": "object",
+                        "properties": {
+                            "primary_formats": {
+                                "type": "array",
+                                "items": {"type": "string"}
+                            },
+                            "secondary_formats": {
+                                "type": "array",
+                                "items": {"type": "string"}
+                            },
+                            "implementation_priority": {
+                                "type": "array",
+                                "items": {"type": "string"}
+                            }
+                        }
+                    }
+                }
+            })
+            return response
+        except Exception as e:
+            st.error(f"Error suggesting content formats: {e}")
+            return []
+    
+    def _create_topic_clusters(self, ai_insights: dict) -> dict:
+        """
+        Create topic clusters from AI insights.
+        
+        Args:
+            ai_insights (dict): AI-processed insights
+            
+        Returns:
+            dict: Topic clusters and relationships
+        """
+        prompt = f"""
+        As a content organization expert, analyze the following insights and create topic clusters:
+
+        AI Insights: {ai_insights}
+
+        Please provide a JSON response with the following structure:
+        {{
+            "clusters": [
+                {{
+                    "cluster_name": "string",
+                    "main_topics": ["string"],
+                    "subtopics": ["string"],
+                    "related_keywords": ["string"],
+                    "content_opportunities": ["string"]
+                }}
+            ],
+            "relationships": {{
+                "cluster_connections": [
+                    {{
+                        "source": "string",
+                        "target": "string",
+                        "relationship_type": "string",
+                        "strength": "number"
+                    }}
+                ],
+                "content_hierarchy": {{
+                    "primary_topics": ["string"],
+                    "secondary_topics": ["string"],
+                    "tertiary_topics": ["string"]
+                }}
+            }}
+        }}
+
+        Focus on:
+        1. Identifying main topic clusters
+        2. Organizing subtopics and related keywords
+        3. Mapping relationships between clusters
+        4. Suggesting content opportunities
+        """
+
+        try:
+            response = llm_text_gen(prompt, json_struct={
+                "type": "object",
+                "properties": {
+                    "clusters": {
+                        "type": "array",
+                        "items": {
+                            "type": "object",
+                            "properties": {
+                                "cluster_name": {"type": "string"},
+                                "main_topics": {
+                                    "type": "array",
+                                    "items": {"type": "string"}
+                                },
+                                "subtopics": {
+                                    "type": "array",
+                                    "items": {"type": "string"}
+                                },
+                                "related_keywords": {
+                                    "type": "array",
+                                    "items": {"type": "string"}
+                                },
+                                "content_opportunities": {
+                                    "type": "array",
+                                    "items": {"type": "string"}
+                                }
+                            }
+                        }
+                    },
+                    "relationships": {
+                        "type": "object",
+                        "properties": {
+                            "cluster_connections": {
+                                "type": "array",
+                                "items": {
+                                    "type": "object",
+                                    "properties": {
+                                        "source": {"type": "string"},
+                                        "target": {"type": "string"},
+                                        "relationship_type": {"type": "string"},
+                                        "strength": {"type": "number"}
+                                    }
+                                }
+                            },
+                            "content_hierarchy": {
+                                "type": "object",
+                                "properties": {
+                                    "primary_topics": {
+                                        "type": "array",
+                                        "items": {"type": "string"}
+                                    },
+                                    "secondary_topics": {
+                                        "type": "array",
+                                        "items": {"type": "string"}
+                                    },
+                                    "tertiary_topics": {
+                                        "type": "array",
+                                        "items": {"type": "string"}
+                                    }
+                                }
+                            }
+                        }
+                    }
+                }
+            })
+            return response
+        except Exception as e:
+            st.error(f"Error creating topic clusters: {e}")
+            return {
+                'clusters': [],
+                'relationships': {}
+            } 

lib/ai_seo_tools/content_gap_analysis/main.py

@@ -0,0 +1,361 @@
+"""
+Main module for content gap analysis.
+"""
+
+from typing import Dict, Any, List, Optional
+import streamlit as st
+from loguru import logger
+from lib.utils.website_analyzer.analyzer import WebsiteAnalyzer
+from .competitor_analyzer import CompetitorAnalyzer
+from .keyword_researcher import KeywordResearcher
+from .recommendation_engine import RecommendationEngine
+from .utils.ai_processor import AIProcessor, ProgressTracker
+from .utils.storage import ContentGapAnalysisStorage
+from datetime import datetime
+import asyncio
+import sys
+import os
+from lib.gpt_providers.text_generation.main_text_generation import llm_text_gen
+from .utils.content_parser import ContentParser
+
+# Configure logger
+logger.remove()  # Remove default handler
+logger.add(
+    "logs/content_gap_analysis.log",
+    rotation="50 MB",
+    retention="10 days",
+    level="DEBUG",
+    format="{time:YYYY-MM-DD HH:mm:ss} | {level} | {message}"
+)
+logger.add(
+    sys.stdout,
+    level="INFO",
+    format="<green>{time:YYYY-MM-DD HH:mm:ss}</green> | <level>{level: <8}</level> | <cyan>{message}</cyan>"
+)
+
+# Ensure logs directory exists
+os.makedirs("logs", exist_ok=True)
+
+class ContentGapAnalysis:
+    """Main class for content gap analysis."""
+    
+    def __init__(self, db_session=None):
+        """Initialize the content gap analysis components."""
+        self.website_analyzer = WebsiteAnalyzer()
+        self.competitor_analyzer = CompetitorAnalyzer()
+        self.keyword_researcher = KeywordResearcher()
+        self.recommendation_engine = RecommendationEngine()
+        self.ai_processor = AIProcessor()
+        self.progress = ProgressTracker()
+        self.storage = ContentGapAnalysisStorage(db_session) if db_session else None
+        
+        # Define analysis phases
+        self.phases = {
+            'website_analysis': {
+                'name': 'Website Analysis',
+                'steps': [
+                    'Initializing website analysis',
+                    'Analyzing website content',
+                    'Evaluating SEO elements',
+                    'Generating website insights'
+                ]
+            },
+            'competitor_analysis': {
+                'name': 'Competitor Analysis',
+                'steps': [
+                    'Initializing competitor analysis',
+                    'Analyzing competitor content',
+                    'Comparing market position',
+                    'Generating competitive insights'
+                ]
+            },
+            'keyword_analysis': {
+                'name': 'Keyword Analysis',
+                'steps': [
+                    'Initializing keyword research',
+                    'Analyzing keyword trends',
+                    'Evaluating search intent',
+                    'Generating keyword insights'
+                ]
+            },
+            'recommendation_generation': {
+                'name': 'Recommendation Generation',
+                'steps': [
+                    'Initializing recommendation engine',
+                    'Analyzing content gaps',
+                    'Generating recommendations',
+                    'Creating implementation plan'
+                ]
+            }
+        }
+        
+        logger.info("ContentGapAnalysis initialized")
+    
+    def analyze(self, url: str, industry: str, competitor_urls: Optional[List[str]] = None, user_id: Optional[int] = None) -> Dict[str, Any]:
+        """
+        Run the complete content gap analysis workflow.
+        
+        Args:
+            url: Target website URL
+            industry: Industry category
+            competitor_urls: Optional list of competitor URLs
+            user_id: Optional user ID for storing results
+            
+        Returns:
+            Dictionary containing analysis results
+        """
+        try:
+            results = {}
+            start_time = datetime.utcnow()
+            
+            # Phase 1: Website Analysis
+            self.progress.start_stage('website_analysis')
+            self.progress.next_step()
+            
+            website_analysis = self.website_analyzer.analyze(url)
+            results['website'] = website_analysis
+            
+            self.progress.next_step()
+            self.progress.complete_stage()
+            
+            # Phase 2: Competitor Analysis
+            if competitor_urls:
+                self.progress.start_stage('competitor_analysis')
+                self.progress.next_step()
+                
+                competitor_analysis = self.competitor_analyzer.analyze(competitor_urls, industry)
+                results['competitors'] = competitor_analysis
+                
+                self.progress.next_step()
+                self.progress.complete_stage()
+            
+            # Phase 3: Keyword Analysis
+            self.progress.start_stage('keyword_analysis')
+            self.progress.next_step()
+            
+            keyword_analysis = self.keyword_researcher.analyze(industry, url)
+            results['keywords'] = keyword_analysis
+            
+            self.progress.next_step()
+            self.progress.complete_stage()
+            
+            # Phase 4: Recommendation Generation
+            self.progress.start_stage('recommendation_generation')
+            self.progress.next_step()
+            
+            recommendations = self.recommendation_engine.generate_recommendations(
+                website_analysis,
+                competitor_analysis if competitor_urls else None,
+                keyword_analysis
+            )
+            results['recommendations'] = recommendations
+            
+            self.progress.next_step()
+            self.progress.complete_stage()
+            
+            # Calculate analysis duration
+            end_time = datetime.utcnow()
+            results['duration'] = (end_time - start_time).total_seconds()
+            
+            # Store results if user_id is provided and storage is available
+            if user_id and self.storage:
+                analysis_id = self.storage.save_analysis(user_id, url, industry, results)
+                if analysis_id:
+                    results['analysis_id'] = analysis_id
+            
+            return results
+            
+        except Exception as e:
+            if self.progress.current_stage:
+                self.progress.update_progress(0, f"Error in {self.progress.stages[self.progress.current_stage]['name']}: {str(e)}")
+            st.error(f"Error in content gap analysis: {str(e)}")
+            return {
+                'error': str(e),
+                'website': {},
+                'competitors': [],
+                'keywords': {},
+                'recommendations': []
+            }
+    
+    def get_analysis(self, analysis_id: int) -> Optional[Dict[str, Any]]:
+        """
+        Retrieve stored analysis results.
+        
+        Args:
+            analysis_id: Analysis ID
+            
+        Returns:
+            Dictionary containing analysis results if found, None otherwise
+        """
+        if not self.storage:
+            st.error("Storage not initialized")
+            return None
+        return self.storage.get_analysis(analysis_id)
+    
+    def get_user_analyses(self, user_id: int) -> List[Dict[str, Any]]:
+        """
+        Get all analyses for a user.
+        
+        Args:
+            user_id: User ID
+            
+        Returns:
+            List of analysis summaries
+        """
+        if not self.storage:
+            st.error("Storage not initialized")
+            return []
+        return self.storage.get_user_analyses(user_id)
+    
+    def update_recommendation_status(self, recommendation_id: int, status: str) -> bool:
+        """
+        Update the status of a recommendation.
+        
+        Args:
+            recommendation_id: Recommendation ID
+            status: New status
+            
+        Returns:
+            True if successful, False otherwise
+        """
+        if not self.storage:
+            st.error("Storage not initialized")
+            return False
+        return self.storage.update_recommendation_status(recommendation_id, status)
+    
+    def delete_analysis(self, analysis_id: int) -> bool:
+        """
+        Delete an analysis and all related data.
+        
+        Args:
+            analysis_id: Analysis ID
+            
+        Returns:
+            True if successful, False otherwise
+        """
+        if not self.storage:
+            st.error("Storage not initialized")
+            return False
+        return self.storage.delete_analysis(analysis_id)
+    
+    def get_analysis_summary(self, results: Dict[str, Any]) -> Dict[str, Any]:
+        """
+        Generate a summary of the analysis results.
+        
+        Args:
+            results: Dictionary containing analysis results
+            
+        Returns:
+            Dictionary containing summary metrics and insights
+        """
+        try:
+            self.progress.start_stage('summary_generation')
+            self.progress.next_step()
+            
+            summary = {
+                'website_metrics': self._summarize_website_metrics(results.get('website', {})),
+                'competitor_insights': self._summarize_competitor_insights(results.get('competitors', {})),
+                'keyword_opportunities': self._summarize_keyword_opportunities(results.get('keywords', {})),
+                'recommendation_highlights': self._summarize_recommendations(results.get('recommendations', {})),
+                'ai_insights': results.get('ai_insights', {})
+            }
+            
+            self.progress.complete_stage()
+            return summary
+            
+        except Exception as e:
+            if self.progress.current_stage:
+                self.progress.update_progress(0, f"Error generating summary: {str(e)}")
+            st.error(f"Error generating analysis summary: {str(e)}")
+            return {
+                'error': str(e),
+                'website_metrics': {},
+                'competitor_insights': {},
+                'keyword_opportunities': {},
+                'recommendation_highlights': {},
+                'ai_insights': {}
+            }
+    
+    def export_results(self, results: Dict[str, Any], format: str = 'json') -> str:
+        """
+        Export analysis results in the specified format.
+        
+        Args:
+            results: Dictionary containing analysis results
+            format: Export format ('json' or 'csv')
+            
+        Returns:
+            String containing exported results
+        """
+        try:
+            self.progress.start_stage('export')
+            self.progress.next_step()
+            
+            if format.lower() == 'json':
+                import json
+                exported = json.dumps(results, indent=2)
+            elif format.lower() == 'csv':
+                import pandas as pd
+                # Convert results to DataFrame and then to CSV
+                df = pd.DataFrame(results)
+                exported = df.to_csv(index=False)
+            else:
+                raise ValueError(f"Unsupported export format: {format}")
+            
+            self.progress.complete_stage()
+            return exported
+            
+        except Exception as e:
+            if self.progress.current_stage:
+                self.progress.update_progress(0, f"Error exporting results: {str(e)}")
+            st.error(f"Error exporting results: {str(e)}")
+            return str(e)
+    
+    def _summarize_website_metrics(self, website_data: Dict[str, Any]) -> Dict[str, Any]:
+        """Generate summary of website metrics."""
+        try:
+            return {
+                'content_score': website_data.get('content_score', 0),
+                'seo_score': website_data.get('seo_score', 0),
+                'structure_score': website_data.get('structure_score', 0),
+                'key_insights': website_data.get('insights', [])[:5]  # Top 5 insights
+            }
+        except Exception as e:
+            st.error(f"Error summarizing website metrics: {str(e)}")
+            return {}
+    
+    def _summarize_competitor_insights(self, competitor_data: Dict[str, Any]) -> Dict[str, Any]:
+        """Generate summary of competitor insights."""
+        try:
+            return {
+                'market_position': competitor_data.get('market_position', {}),
+                'content_gaps': competitor_data.get('content_gaps', [])[:5],  # Top 5 gaps
+                'competitive_advantages': competitor_data.get('advantages', [])[:5]  # Top 5 advantages
+            }
+        except Exception as e:
+            st.error(f"Error summarizing competitor insights: {str(e)}")
+            return {}
+    
+    def _summarize_keyword_opportunities(self, keyword_data: Dict[str, Any]) -> Dict[str, Any]:
+        """Generate summary of keyword opportunities."""
+        try:
+            return {
+                'top_keywords': keyword_data.get('top_keywords', [])[:10],  # Top 10 keywords
+                'search_intent': keyword_data.get('search_intent', {}),
+                'opportunities': keyword_data.get('opportunities', [])[:5]  # Top 5 opportunities
+            }
+        except Exception as e:
+            st.error(f"Error summarizing keyword opportunities: {str(e)}")
+            return {}
+    
+    def _summarize_recommendations(self, recommendation_data: Dict[str, Any]) -> Dict[str, Any]:
+        """Generate summary of recommendations."""
+        try:
+            return {
+                'priority_recommendations': recommendation_data.get('priority_recommendations', [])[:5],  # Top 5 recommendations
+                'implementation_timeline': recommendation_data.get('timeline', {}),
+                'expected_impact': recommendation_data.get('impact', {})
+            }
+        except Exception as e:
+            st.error(f"Error summarizing recommendations: {str(e)}")
+            return {} 

lib/ai_seo_tools/content_gap_analysis/navigation.py

@@ -0,0 +1,41 @@
+"""
+Navigation component for Content Gap Analysis tool.
+"""
+
+import streamlit as st
+
+def show_content_gap_analysis_nav():
+    """Show navigation for Content Gap Analysis tool."""
+    st.sidebar.title("Content Gap Analysis")
+    st.sidebar.markdown("""
+    Analyze your content strategy, identify gaps, and get AI-powered recommendations.
+    """)
+    
+    # Navigation options
+    nav_option = st.sidebar.radio(
+        "Select Analysis Type",
+        ["Website Analysis", "Competitor Analysis", "Keyword Research", "Recommendations"]
+    )
+    
+    # Tool description
+    st.sidebar.markdown("""
+    ### Features
+    - Website content analysis
+    - Competitor content comparison
+    - Keyword research and trends
+    - AI-powered recommendations
+    - Content gap identification
+    - Implementation timeline
+    """)
+    
+    # Help section
+    with st.sidebar.expander("How to Use"):
+        st.markdown("""
+        1. Start with Website Analysis
+        2. Add competitor URLs
+        3. Research keywords
+        4. Get recommendations
+        5. Export results
+        """)
+    
+    return nav_option 

lib/ai_seo_tools/content_gap_analysis/recommendation_engine.py

@@ -0,0 +1,440 @@
+"""
+Recommendation engine for content gap analysis.
+"""
+
+import streamlit as st
+from typing import Dict, Any, List, Optional
+from loguru import logger
+from lib.utils.website_analyzer.analyzer import WebsiteAnalyzer
+from lib.ai_seo_tools.content_gap_analysis.utils.data_collector import DataCollector
+from lib.ai_seo_tools.content_gap_analysis.utils.content_parser import ContentParser
+from lib.ai_seo_tools.content_gap_analysis.utils.ai_processor import AIProcessor, ProgressTracker
+from lib.ai_seo_tools.content_title_generator import ai_title_generator
+import asyncio
+import sys
+import os
+import json
+from lib.gpt_providers.text_generation.main_text_generation import llm_text_gen
+
+# Configure logger
+logger.remove()  # Remove default handler
+logger.add(
+    "logs/recommendation_engine.log",
+    rotation="50 MB",
+    retention="10 days",
+    level="DEBUG",
+    format="{time:YYYY-MM-DD HH:mm:ss} | {level} | {message}"
+)
+logger.add(
+    sys.stdout,
+    level="INFO",
+    format="<green>{time:YYYY-MM-DD HH:mm:ss}</green> | <level>{level: <8}</level> | <cyan>{message}</cyan>"
+)
+
+# Ensure logs directory exists
+os.makedirs("logs", exist_ok=True)
+
+class RecommendationEngine:
+    """
+    Generates content recommendations based on analysis results.
+    """
+    
+    def __init__(self):
+        """Initialize the recommendation engine with required components."""
+        self.ai_processor = AIProcessor()
+        self.progress = ProgressTracker()
+        
+        # Define analysis stages
+        self.stages = {
+            'recommendation_generation': {
+                'name': 'Recommendation Generation',
+                'steps': [
+                    'Initializing recommendation engine',
+                    'Analyzing content gaps',
+                    'Evaluating opportunities',
+                    'Generating recommendations',
+                    'Creating implementation plan'
+                ]
+            }
+        }
+    
+    def generate_recommendations(self, website_analysis: Dict[str, Any], competitor_analysis: Optional[Dict[str, Any]], keyword_analysis: Dict[str, Any]) -> Dict[str, Any]:
+        """
+        Generate content recommendations.
+        
+        Args:
+            website_analysis: Website analysis results
+            competitor_analysis: Optional competitor analysis results
+            keyword_analysis: Keyword analysis results
+            
+        Returns:
+            Dictionary containing recommendations
+        """
+        try:
+            self.progress.start_stage('recommendation_generation')
+            self.progress.next_step()
+            
+            # Analyze content gaps
+            content_gaps = self._analyze_content_gaps(website_analysis, competitor_analysis, keyword_analysis)
+            self.progress.next_step()
+            
+            # Evaluate opportunities
+            opportunities = self._evaluate_opportunities(content_gaps, keyword_analysis)
+            self.progress.next_step()
+            
+            # Generate recommendations
+            recommendations = self._generate_recommendations(content_gaps, opportunities)
+            self.progress.next_step()
+            
+            # Create implementation plan
+            implementation_plan = self._create_implementation_plan(recommendations)
+            self.progress.next_step()
+            
+            self.progress.complete_stage()
+            
+            return {
+                'content_gaps': content_gaps,
+                'opportunities': opportunities,
+                'recommendations': recommendations,
+                'implementation_plan': implementation_plan
+            }
+            
+        except Exception as e:
+            if self.progress.current_stage:
+                self.progress.update_progress(0, f"Error in {self.progress.stages[self.progress.current_stage]['name']}: {str(e)}")
+            st.error(f"Error generating recommendations: {str(e)}")
+            return {
+                'error': str(e),
+                'content_gaps': [],
+                'opportunities': [],
+                'recommendations': [],
+                'implementation_plan': {}
+            }
+    
+    def _analyze_content_gaps(self, website_analysis: Dict[str, Any], competitor_analysis: Optional[Dict[str, Any]], keyword_analysis: Dict[str, Any]) -> List[Dict[str, Any]]:
+        """Analyze content gaps."""
+        try:
+            content_gaps = []
+            
+            # Analyze website content gaps
+            website_gaps = self._analyze_website_gaps(website_analysis)
+            content_gaps.extend(website_gaps)
+            
+            # Analyze competitor gaps if available
+            if competitor_analysis:
+                competitor_gaps = self._analyze_competitor_gaps(competitor_analysis)
+                content_gaps.extend(competitor_gaps)
+            
+            # Analyze keyword gaps
+            keyword_gaps = self._analyze_keyword_gaps(keyword_analysis)
+            content_gaps.extend(keyword_gaps)
+            
+            return content_gaps
+        except Exception as e:
+            st.error(f"Error analyzing content gaps: {str(e)}")
+            return []
+    
+    def _analyze_website_gaps(self, website_analysis: Dict[str, Any]) -> List[Dict[str, Any]]:
+        """Analyze website content gaps."""
+        try:
+            gaps = []
+            
+            # Check content quality
+            quality_metrics = website_analysis.get('quality_metrics', {})
+            if quality_metrics.get('readability_score', 0) < 70:
+                gaps.append({
+                    'type': 'content_quality',
+                    'issue': 'Low readability score',
+                    'score': quality_metrics.get('readability_score', 0),
+                    'recommendation': 'Improve content readability'
+                })
+            
+            # Check SEO elements
+            seo_metrics = website_analysis.get('seo_metrics', {})
+            if seo_metrics.get('seo_score', 0) < 70:
+                gaps.append({
+                    'type': 'seo',
+                    'issue': 'Low SEO score',
+                    'score': seo_metrics.get('seo_score', 0),
+                    'recommendation': 'Enhance SEO optimization'
+                })
+            
+            return gaps
+        except Exception as e:
+            st.error(f"Error analyzing website gaps: {str(e)}")
+            return []
+    
+    def _analyze_competitor_gaps(self, competitor_analysis: Dict[str, Any]) -> List[Dict[str, Any]]:
+        """Analyze competitor content gaps."""
+        try:
+            gaps = []
+            
+            # Check content gaps
+            content_gaps = competitor_analysis.get('content_gaps', [])
+            for gap in content_gaps:
+                gaps.append({
+                    'type': 'competitor',
+                    'issue': f"Missing topic: {', '.join(gap.get('missing_topics', []))}",
+                    'recommendation': 'Create content for missing topics'
+                })
+            
+            return gaps
+        except Exception as e:
+            st.error(f"Error analyzing competitor gaps: {str(e)}")
+            return []
+    
+    def _analyze_keyword_gaps(self, keyword_analysis: Dict[str, Any]) -> List[Dict[str, Any]]:
+        """Analyze keyword gaps."""
+        try:
+            gaps = []
+            
+            # Check keyword opportunities
+            opportunities = keyword_analysis.get('opportunities', [])
+            for opportunity in opportunities:
+                gaps.append({
+                    'type': 'keyword',
+                    'issue': f"Keyword opportunity: {opportunity.get('keyword')}",
+                    'volume': opportunity.get('volume', 0),
+                    'difficulty': opportunity.get('difficulty', 0),
+                    'recommendation': f"Target keyword: {opportunity.get('keyword')}"
+                })
+            
+            return gaps
+        except Exception as e:
+            st.error(f"Error analyzing keyword gaps: {str(e)}")
+            return []
+    
+    def _evaluate_opportunities(self, content_gaps: List[Dict[str, Any]], keyword_analysis: Dict[str, Any]) -> List[Dict[str, Any]]:
+        """Evaluate content opportunities."""
+        try:
+            opportunities = []
+            
+            # Evaluate each gap
+            for gap in content_gaps:
+                # Calculate priority score
+                priority_score = self._calculate_priority_score(gap, keyword_analysis)
+                
+                if priority_score > 50:  # Threshold for good opportunities
+                    opportunities.append({
+                        'type': gap.get('type'),
+                        'issue': gap.get('issue'),
+                        'recommendation': gap.get('recommendation'),
+                        'priority_score': priority_score
+                    })
+            
+            # Sort by priority score
+            opportunities.sort(key=lambda x: x['priority_score'], reverse=True)
+            
+            return opportunities
+        except Exception as e:
+            st.error(f"Error evaluating opportunities: {str(e)}")
+            return []
+    
+    def _calculate_priority_score(self, gap: Dict[str, Any], keyword_analysis: Dict[str, Any]) -> float:
+        """Calculate priority score for a gap."""
+        try:
+            base_score = 0
+            
+            # Base score based on gap type
+            if gap.get('type') == 'content_quality':
+                base_score = 70
+            elif gap.get('type') == 'seo':
+                base_score = 80
+            elif gap.get('type') == 'competitor':
+                base_score = 60
+            elif gap.get('type') == 'keyword':
+                base_score = 50
+            
+            # Adjust score based on keyword data
+            if gap.get('type') == 'keyword':
+                keyword = gap.get('issue', '').split(': ')[-1]
+                keyword_data = keyword_analysis.get('trend_analysis', {}).get('trends', {}).get(keyword, {})
+                if keyword_data:
+                    base_score += keyword_data.get('volume', 0) * 0.1
+                    base_score -= keyword_data.get('difficulty', 0) * 0.2
+            
+            return min(100, max(0, base_score))
+        except Exception as e:
+            st.error(f"Error calculating priority score: {str(e)}")
+            return 0
+    
+    def _generate_recommendations(self, content_gaps: List[Dict[str, Any]], opportunities: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
+        """Generate content recommendations."""
+        try:
+            recommendations = []
+            
+            # Generate recommendations for each opportunity
+            for opportunity in opportunities:
+                recommendations.append({
+                    'type': opportunity.get('type'),
+                    'issue': opportunity.get('issue'),
+                    'recommendation': opportunity.get('recommendation'),
+                    'priority': opportunity.get('priority_score', 0),
+                    'implementation_steps': self._generate_implementation_steps(opportunity)
+                })
+            
+            return recommendations
+        except Exception as e:
+            st.error(f"Error generating recommendations: {str(e)}")
+            return []
+    
+    def _generate_implementation_steps(self, opportunity: Dict[str, Any]) -> List[str]:
+        """Generate implementation steps for a recommendation."""
+        try:
+            steps = []
+            
+            if opportunity.get('type') == 'content_quality':
+                steps = [
+                    'Review current content structure',
+                    'Improve readability and formatting',
+                    'Enhance content organization',
+                    'Update content based on best practices'
+                ]
+            elif opportunity.get('type') == 'seo':
+                steps = [
+                    'Audit current SEO implementation',
+                    'Optimize meta tags and descriptions',
+                    'Improve content structure for SEO',
+                    'Implement technical SEO improvements'
+                ]
+            elif opportunity.get('type') == 'competitor':
+                steps = [
+                    'Research competitor content',
+                    'Identify unique value proposition',
+                    'Create content for missing topics',
+                    'Optimize content for target keywords'
+                ]
+            elif opportunity.get('type') == 'keyword':
+                steps = [
+                    'Research keyword intent',
+                    'Create content strategy',
+                    'Develop content for target keyword',
+                    'Optimize content for search'
+                ]
+            
+            return steps
+        except Exception as e:
+            st.error(f"Error generating implementation steps: {str(e)}")
+            return []
+    
+    def _create_implementation_plan(self, recommendations: List[Dict[str, Any]]) -> Dict[str, Any]:
+        """Create implementation plan."""
+        try:
+            plan = {
+                'phases': [],
+                'timeline': {},
+                'resources': {},
+                'success_metrics': {}
+            }
+            
+            # Create phases based on recommendation types
+            phases = {
+                'content_quality': 'Content Enhancement',
+                'seo': 'SEO Optimization',
+                'competitor': 'Competitive Content',
+                'keyword': 'Keyword Targeting'
+            }
+            
+            # Group recommendations by phase
+            for phase_name in phases.values():
+                phase_recommendations = [
+                    rec for rec in recommendations
+                    if phases.get(rec.get('type')) == phase_name
+                ]
+                
+                if phase_recommendations:
+                    plan['phases'].append({
+                        'name': phase_name,
+                        'recommendations': phase_recommendations,
+                        'duration': '2-4 weeks',
+                        'resources': ['Content team', 'SEO team'],
+                        'success_metrics': [
+                            'Content quality score',
+                            'SEO performance',
+                            'User engagement'
+                        ]
+                    })
+            
+            return plan
+        except Exception as e:
+            st.error(f"Error creating implementation plan: {str(e)}")
+            return {}
+    
+    def _generate_content_topics(self, ai_insights: dict) -> list:
+        """
+        Generate content topic suggestions.
+        
+        Args:
+            ai_insights (dict): AI-processed insights
+            
+        Returns:
+            list: Content topic suggestions
+        """
+        # TODO: Implement content topic generation
+        return []
+    
+    def _suggest_content_formats(self, ai_insights: dict) -> list:
+        """
+        Suggest content formats based on analysis.
+        
+        Args:
+            ai_insights (dict): AI-processed insights
+            
+        Returns:
+            list: Content format suggestions
+        """
+        # TODO: Implement content format suggestions
+        return []
+    
+    def _calculate_priority_scores(self, ai_insights: dict) -> dict:
+        """
+        Calculate priority scores for recommendations.
+        
+        Args:
+            ai_insights (dict): AI-processed insights
+            
+        Returns:
+            dict: Priority scores for each recommendation
+        """
+        # TODO: Implement priority scoring
+        return {}
+    
+    def _create_timeline(self, ai_insights: dict) -> dict:
+        """
+        Create implementation timeline for recommendations.
+        
+        Args:
+            ai_insights (dict): AI-processed insights
+            
+        Returns:
+            dict: Implementation timeline
+        """
+        # TODO: Implement timeline creation
+        return {
+            'short_term': [],
+            'medium_term': [],
+            'long_term': []
+        }
+    
+    def _generate_specific_suggestions(self, recommendations: dict, analysis_results: dict) -> dict:
+        """
+        Generate specific content suggestions using existing tools.
+        
+        Args:
+            recommendations (dict): General recommendations
+            analysis_results (dict): Analysis results
+            
+        Returns:
+            dict: Specific content suggestions
+        """
+        suggestions = {}
+        
+        # Generate titles for suggested topics
+        for topic in recommendations['content_topics']:
+            suggestions[topic] = {
+                'titles': ai_title_generator(topic),
+                'meta_descriptions': metadesc_generator_main(topic),
+                'structured_data': ai_structured_data(topic)
+            }
+        
+        return suggestions 

lib/ai_seo_tools/content_gap_analysis/ui.py

@@ -0,0 +1,769 @@
+"""
+Streamlit UI for Content Gap Analysis workflow.
+"""
+
+import streamlit as st
+import pandas as pd
+import plotly.express as px
+import plotly.graph_objects as go
+import json
+from datetime import datetime
+from .main import ContentGapAnalysis
+from .keyword_researcher import KeywordResearcher
+from .competitor_analyzer import CompetitorAnalyzer
+from .website_analyzer import WebsiteAnalyzer
+from .recommendation_engine import RecommendationEngine
+from .utils.ai_processor import AIProcessor
+from .navigation import show_content_gap_analysis_nav
+from typing import Dict, Any
+import logging
+
+# Configure logging
+logging.basicConfig(level=logging.DEBUG)
+logger = logging.getLogger(__name__)
+
+class ContentGapAnalysisUI:
+    """Streamlit UI for Content Gap Analysis workflow."""
+    
+    def __init__(self):
+        """Initialize the UI components."""
+        # Initialize session state for progress tracking
+        if 'current_step' not in st.session_state:
+            st.session_state.current_step = 1
+        if 'analysis_results' not in st.session_state:
+            st.session_state.analysis_results = {}
+        
+        # Initialize analysis components
+        self.analyzer = ContentGapAnalysis()
+        self.keyword_researcher = KeywordResearcher()
+        self.competitor_analyzer = CompetitorAnalyzer()
+        self.website_analyzer = WebsiteAnalyzer()
+        self.recommendation_engine = RecommendationEngine()
+        self.ai_processor = AIProcessor()
+    
+    def run(self):
+        """Run the Streamlit interface."""
+        try:
+            # Show navigation
+            nav_option = show_content_gap_analysis_nav()
+            
+            # Main content area
+            st.title("Content Gap Analysis")
+            st.markdown("""
+            This tool helps you identify content gaps and opportunities by analyzing your website,
+            competitors, and market trends. Follow the steps below to get started.
+            """)
+            
+            # Progress tracking
+            self._show_progress()
+            
+            # Main workflow steps
+            if nav_option == "Website Analysis" or st.session_state.current_step == 1:
+                self._website_analysis_step()
+            elif nav_option == "Competitor Analysis" or st.session_state.current_step == 2:
+                self._competitor_analysis_step()
+            elif nav_option == "Keyword Research" or st.session_state.current_step == 3:
+                self._keyword_research_step()
+            elif nav_option == "Recommendations" or st.session_state.current_step == 4:
+                self._recommendations_step()
+            else:
+                self._export_results()
+        except Exception as e:
+            logger.error(f"Error in run method: {str(e)}", exc_info=True)
+            st.error(f"An error occurred: {str(e)}")
+    
+    def _show_progress(self):
+        """Display progress tracking."""
+        steps = [
+            "Website Analysis",
+            "Competitor Analysis",
+            "Keyword Research",
+            "Recommendations",
+            "Export Results"
+        ]
+        
+        progress = st.session_state.current_step / len(steps)
+        st.progress(progress)
+        
+        cols = st.columns(len(steps))
+        for i, col in enumerate(cols):
+            with col:
+                if i + 1 < st.session_state.current_step:
+                    st.success(f"✓ {steps[i]}")
+                elif i + 1 == st.session_state.current_step:
+                    st.info(f"→ {steps[i]}")
+                else:
+                    st.text(f"○ {steps[i]}")
+    
+    def _website_analysis_step(self):
+        """Website analysis step UI."""
+        try:
+            st.header("Step 1: Website Analysis")
+            
+            # Display previous results if they exist
+            if 'website' in st.session_state.analysis_results:
+                st.info("Previous analysis results found. You can analyze a new website or proceed to the next step.")
+                self._display_website_analysis(st.session_state.analysis_results['website'])
+                
+                col1, col2 = st.columns(2)
+                with col1:
+                    if st.button("Analyze New Website"):
+                        st.session_state.analysis_results.pop('website', None)
+                        st.rerun()
+                with col2:
+                    if st.button("Proceed to Competitor Analysis"):
+                        st.session_state.current_step = 2
+                        st.rerun()
+                return
+            
+            # Create form for new analysis
+            with st.form("website_analysis_form"):
+                website_url = st.text_input("Enter your website URL")
+                industry = st.text_input("Enter your industry/niche")
+                
+                submitted = st.form_submit_button("Analyze Website")
+            
+            # Handle form submission outside the form
+            if submitted and website_url and industry:
+                # Initialize progress tracking
+                if 'analysis_progress' not in st.session_state:
+                    st.session_state.analysis_progress = {
+                        'status': 'initializing',
+                        'current_step': 'Starting Analysis',
+                        'progress': 0,
+                        'details': 'Initializing analysis...'
+                    }
+                
+                # Create progress container
+                progress_container = st.empty()
+                status_container = st.empty()
+                details_container = st.empty()
+                
+                # Update progress display
+                def update_progress_display():
+                    progress = st.session_state.analysis_progress
+                    
+                    # Update progress bar
+                    with progress_container:
+                        st.progress(progress['progress'] / 100)
+                    
+                    # Update status
+                    with status_container:
+                        if progress['status'] == 'error':
+                            st.error(f"Error: {progress['current_step']}")
+                        elif progress['status'] == 'completed':
+                            st.success(f"✓ {progress['current_step']}")
+                        else:
+                            st.info(f"→ {progress['current_step']}")
+                    
+                    # Update details
+                    with details_container:
+                        st.write(progress['details'])
+                
+                # Initial progress display
+                update_progress_display()
+                
+                try:
+                    # Get basic analysis
+                    results = self.website_analyzer.analyze(website_url)
+                    
+                    # Update progress from analyzer
+                    st.session_state.analysis_progress = self.website_analyzer.progress.get_progress()
+                    update_progress_display()
+                    
+                    if isinstance(results, dict) and 'error' in results:
+                        st.error(f"Error in website analysis: {results['error']}")
+                        return
+                    
+                    # Get AI-enhanced analysis
+                    st.session_state.analysis_progress.update({
+                        'current_step': 'AI Analysis',
+                        'progress': 95,
+                        'details': 'Performing AI-enhanced analysis...'
+                    })
+                    update_progress_display()
+                    
+                    ai_analysis = self.ai_processor.analyze_content({
+                        'url': website_url,
+                        'industry': industry,
+                        'content': results
+                    })
+                    
+                    # Combine results
+                    if isinstance(results, dict):
+                        results.update(ai_analysis)
+                    else:
+                        results = {'error': 'Invalid analysis results format'}
+                    
+                    # Store results in session state
+                    st.session_state.analysis_results['website'] = results
+                    
+                    # Update final progress
+                    st.session_state.analysis_progress.update({
+                        'status': 'completed',
+                        'current_step': 'Analysis Complete',
+                        'progress': 100,
+                        'details': 'Analysis completed successfully!'
+                    })
+                    update_progress_display()
+                    
+                    # Display results
+                    self._display_website_analysis(results)
+                    
+                except Exception as e:
+                    logger.error(f"Error during website analysis: {str(e)}", exc_info=True)
+                    st.session_state.analysis_progress.update({
+                        'status': 'error',
+                        'current_step': 'Analysis Failed',
+                        'details': f"Error during website analysis: {str(e)}"
+                    })
+                    update_progress_display()
+                    st.error(f"Error during website analysis: {str(e)}")
+                    return
+                        
+        except Exception as e:
+            logger.error(f"Error in website analysis step: {str(e)}", exc_info=True)
+            st.error(f"Error in website analysis: {str(e)}")
+    
+    def _display_website_analysis(self, results: Dict[str, Any]):
+        """Display website analysis results."""
+        try:
+            if not isinstance(results, dict):
+                st.error("Invalid analysis results format")
+                return
+                
+            if 'error' in results:
+                st.error(f"Error in analysis: {results['error']}")
+                return
+            
+            # Content Metrics
+            st.subheader("Content Metrics")
+            content_metrics = results.get('content_metrics', {})
+            
+            if content_metrics:
+                # Basic metrics in columns
+                col1, col2, col3, col4 = st.columns(4)
+                with col1:
+                    st.metric("Word Count", f"{content_metrics.get('word_count', 0):,}")
+                with col2:
+                    st.metric("Headings", f"{content_metrics.get('heading_count', 0):,}")
+                with col3:
+                    st.metric("Images", f"{content_metrics.get('image_count', 0):,}")
+                with col4:
+                    st.metric("Links", f"{content_metrics.get('link_count', 0):,}")
+                
+                # Content Structure Visualization
+                st.write("Content Structure")
+                heading_data = {
+                    'Type': ['H1', 'H2', 'H3', 'Paragraphs'],
+                    'Count': [
+                        content_metrics.get('h1_count', 0),
+                        content_metrics.get('h2_count', 0),
+                        content_metrics.get('h3_count', 0),
+                        content_metrics.get('paragraph_count', 0)
+                    ]
+                }
+                fig = px.bar(
+                    heading_data,
+                    x='Type',
+                    y='Count',
+                    title="Content Structure Distribution",
+                    color='Type',
+                    color_discrete_sequence=px.colors.qualitative.Set3
+                )
+                st.plotly_chart(fig, use_container_width=True)
+                
+                # Content Features
+                st.write("Content Features")
+                features = {
+                    'Feature': ['Meta Description', 'Robots.txt', 'Sitemap'],
+                    'Status': [
+                        content_metrics.get('has_meta_description', False),
+                        content_metrics.get('has_robots_txt', False),
+                        content_metrics.get('has_sitemap', False)
+                    ]
+                }
+                fig = px.bar(
+                    features,
+                    x='Feature',
+                    y='Status',
+                    title="Content Features Status",
+                    color='Status',
+                    color_discrete_sequence=['red', 'green']
+                )
+                st.plotly_chart(fig, use_container_width=True)
+            
+            # SEO Metrics
+            st.subheader("SEO Metrics")
+            seo_metrics = results.get('seo_metrics', {})
+            
+            if seo_metrics:
+                # Basic metrics in columns
+                col1, col2, col3, col4 = st.columns(4)
+                with col1:
+                    st.metric("Overall Score", f"{seo_metrics.get('overall_score', 0):.1f}%")
+                with col2:
+                    content_quality = seo_metrics.get('content', {}).get('content_quality_score', 0)
+                    st.metric("Content Quality", f"{content_quality:.1f}%")
+                with col3:
+                    readability = seo_metrics.get('content', {}).get('readability_score', 0)
+                    st.metric("Readability", f"{readability:.1f}%")
+                with col4:
+                    keyword_density = seo_metrics.get('content', {}).get('keyword_density', 0)
+                    st.metric("Keyword Density", f"{keyword_density:.1f}%")
+                
+                # SEO Scores Radar Chart
+                seo_scores = {
+                    'Metric': ['Overall', 'Content Quality', 'Readability', 'Keyword Density'],
+                    'Score': [
+                        seo_metrics.get('overall_score', 0),
+                        content_quality,
+                        readability,
+                        keyword_density
+                    ]
+                }
+                fig = px.line_polar(
+                    seo_scores,
+                    r='Score',
+                    theta='Metric',
+                    line_close=True,
+                    title="SEO Performance Overview"
+                )
+                fig.update_traces(fill='toself')
+                st.plotly_chart(fig, use_container_width=True)
+                
+                # Meta Tags Analysis
+                st.write("Meta Tags Analysis")
+                meta_tags = seo_metrics.get('meta_tags', {})
+                if meta_tags:
+                    # Title Analysis
+                    title = meta_tags.get('title', {})
+                    st.write("Title Tag")
+                    st.write(f"Status: {'✅' if title.get('status') == 'good' else '❌'}")
+                    st.write(f"Value: {title.get('value', 'N/A')}")
+                    st.write(f"Length: {title.get('length', 0)} characters")
+                    st.write(f"Score: {title.get('score', 0)}%")
+                    if title.get('recommendation'):
+                        st.warning(title.get('recommendation'))
+                    
+                    # Description Analysis
+                    desc = meta_tags.get('description', {})
+                    st.write("Meta Description")
+                    st.write(f"Status: {'✅' if desc.get('status') == 'good' else '❌'}")
+                    st.write(f"Value: {desc.get('value', 'N/A')}")
+                    st.write(f"Length: {desc.get('length', 0)} characters")
+                    st.write(f"Score: {desc.get('score', 0)}%")
+                    if desc.get('recommendation'):
+                        st.warning(desc.get('recommendation'))
+                    
+                    # Keywords Analysis
+                    keywords = meta_tags.get('keywords', {})
+                    st.write("Meta Keywords")
+                    st.write(f"Status: {'✅' if keywords.get('status') == 'good' else '❌'}")
+                    st.write(f"Value: {keywords.get('value', 'N/A')}")
+                    if keywords.get('recommendation'):
+                        st.warning(keywords.get('recommendation'))
+            
+            # Technical Metrics
+            st.subheader("Technical Metrics")
+            technical_info = results.get('technical_info', {})
+            
+            if technical_info:
+                col1, col2 = st.columns(2)
+                with col1:
+                    st.write("Basic Information")
+                    st.metric("Status Code", technical_info.get('status_code', 'N/A'))
+                    st.metric("Server", technical_info.get('server_info', {}).get('server', 'N/A'))
+                    st.metric("Content Type", technical_info.get('server_info', {}).get('content_type', 'N/A'))
+                with col2:
+                    st.write("Security Information")
+                    security_info = technical_info.get('security_info', {})
+                    security_data = {
+                        'Feature': ['SSL', 'HSTS', 'XSS Protection'],
+                        'Status': [
+                            security_info.get('ssl', False),
+                            security_info.get('hsts', False),
+                            security_info.get('xss_protection', False)
+                        ]
+                    }
+                    fig = px.bar(
+                        security_data,
+                        x='Feature',
+                        y='Status',
+                        title="Security Features Status",
+                        color='Status',
+                        color_discrete_sequence=['red', 'green']
+                    )
+                    st.plotly_chart(fig, use_container_width=True)
+            
+            # Performance Metrics
+            st.subheader("Performance Metrics")
+            performance = results.get('performance', {})
+            
+            if performance:
+                # Basic metrics in columns
+                col1, col2, col3, col4 = st.columns(4)
+                with col1:
+                    st.metric("Load Time", f"{performance.get('load_time', 0):.2f}s")
+                with col2:
+                    st.metric("Page Size", f"{performance.get('page_size', 0):.1f} KB")
+                with col3:
+                    st.metric("Status Code", performance.get('status_code', 'N/A'))
+                with col4:
+                    st.metric("Response Time", f"{performance.get('response_time', 0):.2f}s")
+            
+            # Insights and Recommendations
+            st.subheader("Insights and Recommendations")
+            insights = results.get('insights', [])
+            if insights:
+                for insight in insights:
+                    st.info(f"• {insight}")
+            else:
+                st.info("No specific insights available")
+                
+        except Exception as e:
+            logger.error(f"Error displaying website analysis: {str(e)}", exc_info=True)
+            st.error(f"Error displaying website analysis: {str(e)}")
+    
+    def _competitor_analysis_step(self):
+        """Competitor analysis step UI."""
+        try:
+            st.header("Step 2: Competitor Analysis")
+            
+            with st.form("competitor_analysis_form"):
+                competitors = st.text_area(
+                    "Enter competitor URLs (one per line)",
+                    help="Enter the URLs of your main competitors"
+                )
+                
+                submitted = st.form_submit_button("Analyze Competitors")
+                
+                if submitted and competitors:
+                    with st.spinner("Analyzing competitors..."):
+                        competitor_urls = [url.strip() for url in competitors.split('\n') if url.strip()]
+                        results = self.competitor_analyzer.analyze(competitor_urls)
+                        
+                        # Get AI-enhanced competitor analysis
+                        ai_analysis = self.ai_processor.analyze_competitors({
+                            'competitors': competitor_urls,
+                            'analysis': results
+                        })
+                        
+                        # Combine results
+                        results.update(ai_analysis)
+                        st.session_state.analysis_results['competitors'] = results
+                        
+                        # Display results
+                        self._display_competitor_analysis(results)
+                        
+                        # Move to next step
+                        st.session_state.current_step = 3
+                        st.rerun()
+        except Exception as e:
+            logger.error(f"Error in competitor analysis step: {str(e)}", exc_info=True)
+            st.error(f"Error in competitor analysis: {str(e)}")
+    
+    def _display_competitor_analysis(self, results: dict):
+        """Display competitor analysis results."""
+        st.subheader("Competitor Analysis Results")
+        
+        # Competitor comparison
+        st.subheader("Competitor Comparison")
+        comp_data = pd.DataFrame(results.get('comparison', []))
+        if not comp_data.empty:
+            fig = px.bar(
+                comp_data,
+                x='competitor',
+                y='score',
+                color='metric',
+                title="Competitor Comparison"
+            )
+            st.plotly_chart(fig)
+        
+        # AI-Enhanced Competitor Analysis
+        st.subheader("AI-Enhanced Competitor Analysis")
+        
+        # Competitor Trend Analysis
+        trend_data = results.get('competitor_trends', {})
+        if trend_data:
+            fig = go.Figure()
+            for competitor, trends in trend_data.items():
+                fig.add_trace(go.Scatter(
+                    x=trends.get('timeline', []),
+                    y=trends.get('scores', []),
+                    name=competitor,
+                    mode='lines+markers'
+                ))
+            fig.update_layout(
+                title="Competitor Performance Trends",
+                xaxis_title="Timeline",
+                yaxis_title="Score"
+            )
+            st.plotly_chart(fig)
+        
+        # Content gaps
+        st.subheader("Content Gaps")
+        gaps = results.get('content_gaps', [])
+        for gap in gaps:
+            st.info(f"• {gap}")
+        
+        # AI-Generated Competitive Insights
+        st.subheader("Competitive Insights")
+        insights = results.get('competitive_insights', {})
+        if insights:
+            for category, points in insights.items():
+                with st.expander(f"{category.title()} Analysis"):
+                    for point in points:
+                        st.success(f"• {point}")
+    
+    def _keyword_research_step(self):
+        """Keyword research step UI."""
+        try:
+            st.header("Step 3: Keyword Research")
+            
+            with st.form("keyword_research_form"):
+                industry = st.text_input(
+                    "Enter your industry/niche",
+                    value=st.session_state.analysis_results.get('website', {}).get('industry', '')
+                )
+                
+                submitted = st.form_submit_button("Research Keywords")
+                
+                if submitted and industry:
+                    with st.spinner("Researching keywords..."):
+                        results = self.keyword_researcher.research(industry)
+                        
+                        # Get AI-enhanced keyword analysis
+                        ai_analysis = self.ai_processor.analyze_keywords({
+                            'industry': industry,
+                            'keywords': results
+                        })
+                        
+                        # Combine results
+                        results.update(ai_analysis)
+                        st.session_state.analysis_results['keywords'] = results
+                        
+                        # Display results
+                        self._display_keyword_research(results)
+                        
+                        # Move to next step
+                        st.session_state.current_step = 4
+                        st.rerun()
+        except Exception as e:
+            logger.error(f"Error in keyword research step: {str(e)}", exc_info=True)
+            st.error(f"Error in keyword research: {str(e)}")
+    
+    def _display_keyword_research(self, results: dict):
+        """Display keyword research results."""
+        st.subheader("Keyword Research Results")
+        
+        # Keyword metrics
+        st.subheader("Keyword Metrics")
+        keyword_data = pd.DataFrame(results.get('keywords', []))
+        if not keyword_data.empty:
+            fig = px.scatter(
+                keyword_data,
+                x='search_volume',
+                y='difficulty',
+                size='relevance_score',
+                hover_data=['keyword'],
+                title="Keyword Opportunities"
+            )
+            st.plotly_chart(fig)
+        
+        # AI-Enhanced Keyword Analysis
+        st.subheader("AI-Enhanced Keyword Analysis")
+        
+        # Keyword Trend Analysis
+        trend_data = results.get('keyword_trends', {})
+        if trend_data:
+            fig = go.Figure()
+            for keyword, trends in trend_data.items():
+                fig.add_trace(go.Scatter(
+                    x=trends.get('timeline', []),
+                    y=trends.get('scores', []),
+                    name=keyword,
+                    mode='lines+markers'
+                ))
+            fig.update_layout(
+                title="Keyword Trend Analysis",
+                xaxis_title="Timeline",
+                yaxis_title="Trend Score"
+            )
+            st.plotly_chart(fig)
+        
+        # Search intent distribution
+        st.subheader("Search Intent Distribution")
+        intent_data = pd.DataFrame(results.get('search_intent', {}).get('summary', {}))
+        if not intent_data.empty:
+            fig = px.pie(
+                intent_data,
+                values='count',
+                names='intent',
+                title="Search Intent Distribution"
+            )
+            st.plotly_chart(fig)
+        
+        # Content format suggestions
+        st.subheader("Content Format Suggestions")
+        formats = results.get('content_formats', [])
+        for format in formats:
+            st.info(f"• {format}")
+        
+        # AI-Generated Keyword Insights
+        st.subheader("Keyword Insights")
+        insights = results.get('keyword_insights', {})
+        if insights:
+            for category, points in insights.items():
+                with st.expander(f"{category.title()} Insights"):
+                    for point in points:
+                        st.success(f"• {point}")
+    
+    def _recommendations_step(self):
+        """Recommendations step UI."""
+        try:
+            st.header("Step 4: Content Recommendations")
+            
+            with st.spinner("Generating recommendations..."):
+                results = self.recommendation_engine.generate_recommendations(
+                    st.session_state.analysis_results
+                )
+                
+                # Get AI-enhanced recommendations
+                ai_recommendations = self.ai_processor.analyze_recommendations({
+                    'recommendations': results,
+                    'analysis': st.session_state.analysis_results
+                })
+                
+                # Combine results
+                results.update(ai_recommendations)
+                st.session_state.analysis_results['recommendations'] = results
+                
+                # Display results
+                self._display_recommendations(results)
+                
+                # Move to next step
+                st.session_state.current_step = 5
+                st.rerun()
+        except Exception as e:
+            logger.error(f"Error in recommendations step: {str(e)}", exc_info=True)
+            st.error(f"Error in recommendations: {str(e)}")
+    
+    def _display_recommendations(self, results: dict):
+        """Display content recommendations."""
+        st.subheader("Content Recommendations")
+        
+        # Priority recommendations
+        st.subheader("Priority Recommendations")
+        priorities = results.get('priorities', [])
+        for priority in priorities:
+            st.success(f"• {priority}")
+        
+        # AI-Enhanced Recommendations
+        st.subheader("AI-Enhanced Recommendations")
+        
+        # Recommendation Impact Analysis
+        impact_data = results.get('impact_analysis', {})
+        if impact_data:
+            fig = go.Figure()
+            for metric, values in impact_data.items():
+                fig.add_trace(go.Bar(
+                    name=metric,
+                    x=values.get('categories', []),
+                    y=values.get('scores', [])
+                ))
+            fig.update_layout(
+                title="Recommendation Impact Analysis",
+                xaxis_title="Categories",
+                yaxis_title="Impact Score",
+                barmode='group'
+            )
+            st.plotly_chart(fig)
+        
+        # Implementation timeline
+        st.subheader("Implementation Timeline")
+        timeline = results.get('timeline', [])
+        for item in timeline:
+            st.info(f"• {item}")
+        
+        # Expected impact
+        st.subheader("Expected Impact")
+        impact = results.get('impact', {})
+        for metric, value in impact.items():
+            st.metric(metric, value)
+        
+        # AI-Generated Strategic Insights
+        st.subheader("Strategic Insights")
+        insights = results.get('strategic_insights', {})
+        if insights:
+            for category, points in insights.items():
+                with st.expander(f"{category.title()} Strategy"):
+                    for point in points:
+                        st.success(f"• {point}")
+    
+    def _export_results(self):
+        """Export results step UI."""
+        st.header("Step 5: Export Results")
+        
+        # Export options
+        export_format = st.radio(
+            "Choose export format",
+            ["JSON", "CSV", "PDF"]
+        )
+        
+        if st.button("Export Results"):
+            if export_format == "JSON":
+                self._export_json()
+            elif export_format == "CSV":
+                self._export_csv()
+            else:
+                st.info("PDF export coming soon!")
+    
+    def _export_json(self):
+        """Export results as JSON."""
+        results = st.session_state.analysis_results
+        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+        filename = f"content_gap_analysis_{timestamp}.json"
+        
+        st.download_button(
+            "Download JSON",
+            data=json.dumps(results, indent=2),
+            file_name=filename,
+            mime="application/json"
+        )
+    
+    def _export_csv(self):
+        """Export results as CSV."""
+        results = st.session_state.analysis_results
+        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+        
+        # Convert results to CSV format
+        csv_data = []
+        for section, data in results.items():
+            if isinstance(data, list):
+                for item in data:
+                    if isinstance(item, dict):
+                        item['section'] = section
+                        csv_data.append(item)
+            elif isinstance(data, dict):
+                data['section'] = section
+                csv_data.append(data)
+        
+        if csv_data:
+            df = pd.DataFrame(csv_data)
+            filename = f"content_gap_analysis_{timestamp}.csv"
+            
+            st.download_button(
+                "Download CSV",
+                data=df.to_csv(index=False),
+                file_name=filename,
+                mime="text/csv"
+            )
+
+def main():
+    """Main entry point for the Streamlit app."""
+    ui = ContentGapAnalysisUI()
+    ui.run()
+
+if __name__ == "__main__":
+    main() 

lib/ai_seo_tools/content_gap_analysis/utils/README.md

@@ -0,0 +1,249 @@
+# Content Gap Analysis Utils
+
+This directory contains utility modules that power the Content Gap Analysis tool. These modules provide core functionality for data collection, processing, analysis, and storage.
+
+## Directory Structure
+
+```
+utils/
+├── README.md
+├── ai_processor.py      # AI-powered content analysis and processing
+├── content_parser.py    # Content structure parsing and analysis
+├── data_collector.py    # Website data collection and processing
+└── storage.py          # Analysis results storage and retrieval
+```
+
+## Module Descriptions
+
+### 1. AI Processor (`ai_processor.py`)
+
+The AI Processor module enhances content analysis using AI techniques. It provides intelligent analysis of website content, competitor data, and keyword research.
+
+#### Key Features:
+- Content quality assessment
+- Topic analysis and clustering
+- Performance metrics analysis
+- Strategic recommendations generation
+- Progress tracking for analysis tasks
+
+#### Main Components:
+- `AIProcessor`: Main class for AI-powered analysis
+- `ProgressTracker`: Tracks analysis progress and status
+
+#### Usage Example:
+```python
+from utils.ai_processor import AIProcessor
+
+processor = AIProcessor()
+analysis = processor.analyze_content({
+    'url': 'https://example.com',
+    'industry': 'technology',
+    'content': content_data
+})
+```
+
+### 2. Content Parser (`content_parser.py`)
+
+The Content Parser module handles the parsing and analysis of website content structure. It provides detailed insights into content organization and quality.
+
+#### Key Features:
+- Content structure analysis
+- Text statistics calculation
+- Topic extraction
+- Readability analysis
+- Content hierarchy analysis
+
+#### Main Components:
+- `ContentParser`: Main class for content parsing and analysis
+
+#### Usage Example:
+```python
+from utils.content_parser import ContentParser
+
+parser = ContentParser()
+structure = parser.parse_structure({
+    'main_content': content,
+    'html': html_content,
+    'headings': headings_data
+})
+```
+
+### 3. Data Collector (`data_collector.py`)
+
+The Data Collector module is responsible for gathering website data for analysis. It handles web scraping and data extraction.
+
+#### Key Features:
+- Website content collection
+- Meta data extraction
+- Heading structure analysis
+- Link and image extraction
+- Error handling and retry logic
+
+#### Main Components:
+- `DataCollector`: Main class for data collection
+
+#### Usage Example:
+```python
+from utils.data_collector import DataCollector
+
+collector = DataCollector()
+data = collector.collect('https://example.com')
+```
+
+### 4. Storage (`storage.py`)
+
+The Storage module manages the persistence and retrieval of analysis results. It provides a robust database interface for storing and accessing analysis data.
+
+#### Key Features:
+- Analysis results storage
+- Historical data management
+- Recommendation tracking
+- User-specific analysis storage
+- Error handling and rollback support
+
+#### Main Components:
+- `ContentGapAnalysisStorage`: Main class for storage operations
+
+#### Usage Example:
+```python
+from utils.storage import ContentGapAnalysisStorage
+
+storage = ContentGapAnalysisStorage(db_session)
+analysis_id = storage.save_analysis(
+    user_id=1,
+    website_url='https://example.com',
+    industry='technology',
+    results=analysis_results
+)
+```
+
+## Integration Points
+
+### 1. Website Analysis Integration
+```python
+from utils.data_collector import DataCollector
+from utils.content_parser import ContentParser
+from utils.ai_processor import AIProcessor
+
+# Collect data
+collector = DataCollector()
+data = collector.collect(url)
+
+# Parse content
+parser = ContentParser()
+structure = parser.parse_structure(data)
+
+# Process with AI
+processor = AIProcessor()
+analysis = processor.analyze_content({
+    'url': url,
+    'content': structure
+})
+```
+
+### 2. Storage Integration
+```python
+from utils.storage import ContentGapAnalysisStorage
+
+# Store analysis results
+storage = ContentGapAnalysisStorage(db_session)
+analysis_id = storage.save_analysis(
+    user_id=user_id,
+    website_url=url,
+    industry=industry,
+    results=analysis_results
+)
+
+# Retrieve analysis
+results = storage.get_analysis(analysis_id)
+```
+
+## Error Handling
+
+All modules implement comprehensive error handling:
+
+1. **Data Collection Errors**
+   - Network timeouts
+   - Invalid URLs
+   - Access restrictions
+   - Parsing errors
+
+2. **Processing Errors**
+   - Invalid data formats
+   - AI processing failures
+   - Resource limitations
+   - Analysis timeouts
+
+3. **Storage Errors**
+   - Database connection issues
+   - Transaction failures
+   - Data validation errors
+   - Concurrent access conflicts
+
+## Best Practices
+
+1. **Data Collection**
+   - Implement rate limiting
+   - Use proper user agents
+   - Handle redirects
+   - Validate input data
+
+2. **Content Processing**
+   - Clean and normalize data
+   - Handle encoding issues
+   - Implement fallback strategies
+   - Cache processed results
+
+3. **Storage Management**
+   - Use transactions
+   - Implement data validation
+   - Handle concurrent access
+   - Maintain data integrity
+
+## Future Enhancements
+
+1. **Performance Optimizations**
+   - Implement parallel processing
+   - Add caching layer
+   - Optimize database queries
+   - Enhance error recovery
+
+2. **Feature Additions**
+   - Content performance tracking
+   - Automated content planning
+   - Enhanced competitive intelligence
+   - Advanced topic clustering
+
+3. **Integration Improvements**
+   - API endpoints
+   - Export capabilities
+   - Data visualization
+   - Progress tracking
+
+4. **UI/UX Enhancements**
+   - Interactive visualizations
+   - Real-time progress updates
+   - Export interfaces
+   - Customization options
+
+## Contributing
+
+When contributing to these utility modules:
+
+1. Follow the existing code structure
+2. Add comprehensive error handling
+3. Include unit tests
+4. Update documentation
+5. Follow PEP 8 style guide
+
+## Dependencies
+
+- BeautifulSoup4: HTML parsing
+- NLTK: Natural language processing
+- SQLAlchemy: Database operations
+- Streamlit: UI components
+- Requests: HTTP requests
+
+## License
+
+This project is licensed under the MIT License - see the LICENSE file for details. 

lib/ai_seo_tools/content_gap_analysis/utils/__init__.py

@@ -0,0 +1,13 @@
+"""
+Utility modules for content gap analysis.
+"""
+
+from .data_collector import DataCollector
+from .content_parser import ContentParser
+from .ai_processor import AIProcessor
+
+__all__ = [
+    'DataCollector',
+    'ContentParser',
+    'AIProcessor'
+] 

lib/ai_seo_tools/content_gap_analysis/utils/content_parser.py

@@ -0,0 +1,236 @@
+"""
+Content parser utility for analyzing website content structure.
+"""
+
+from typing import Dict, Any, List
+import re
+from bs4 import BeautifulSoup
+import nltk
+from nltk.tokenize import sent_tokenize, word_tokenize
+from nltk.corpus import stopwords
+from collections import Counter
+
+class ContentParser:
+    """Parser for analyzing website content structure."""
+    
+    def __init__(self):
+        """Initialize the content parser."""
+        try:
+            nltk.data.find('tokenizers/punkt')
+        except LookupError:
+            nltk.download('punkt')
+        try:
+            nltk.data.find('corpora/stopwords')
+        except LookupError:
+            nltk.download('stopwords')
+        
+        self.stop_words = set(stopwords.words('english'))
+    
+    def parse_structure(self, content: Dict[str, Any]) -> Dict[str, Any]:
+        """
+        Parse and analyze the structure of website content.
+        
+        Args:
+            content: Dictionary containing website content
+            
+        Returns:
+            Dictionary containing parsed content structure
+        """
+        try:
+            # Parse main content
+            main_content = content.get('main_content', '')
+            soup = BeautifulSoup(content.get('html', ''), 'html.parser')
+            
+            # Extract text statistics
+            text_stats = self._analyze_text(main_content)
+            
+            # Extract content sections
+            sections = self._extract_sections(soup)
+            
+            # Extract topics
+            topics = self._extract_topics(main_content)
+            
+            # Analyze readability
+            readability = self._analyze_readability(main_content)
+            
+            # Analyze content hierarchy
+            hierarchy = self._analyze_hierarchy(content.get('headings', []))
+            
+            return {
+                'text_statistics': text_stats,
+                'sections': sections,
+                'topics': topics,
+                'readability': readability,
+                'hierarchy': hierarchy,
+                'metadata': content.get('metadata', {})
+            }
+            
+        except Exception as e:
+            return {
+                'error': str(e),
+                'text_statistics': {},
+                'sections': [],
+                'topics': [],
+                'readability': {},
+                'hierarchy': {},
+                'metadata': {}
+            }
+    
+    def _analyze_text(self, text: str) -> Dict[str, Any]:
+        """Analyze text statistics."""
+        sentences = sent_tokenize(text)
+        words = word_tokenize(text.lower())
+        words = [w for w in words if w.isalnum() and w not in self.stop_words]
+        
+        return {
+            'word_count': len(words),
+            'sentence_count': len(sentences),
+            'average_sentence_length': len(words) / max(len(sentences), 1),
+            'unique_words': len(set(words)),
+            'stop_words': len([w for w in word_tokenize(text.lower()) if w in self.stop_words]),
+            'characters': len(text),
+            'paragraphs': len(text.split('\n\n')),
+            'sentences': sentences
+        }
+    
+    def _extract_sections(self, soup: BeautifulSoup) -> List[Dict[str, Any]]:
+        """Extract content sections."""
+        sections = []
+        
+        # Find main content containers
+        containers = soup.find_all(['article', 'section', 'div'], class_=re.compile(r'content|main|article|section'))
+        
+        for container in containers:
+            # Get section heading
+            heading = container.find(['h1', 'h2', 'h3'])
+            heading_text = heading.get_text().strip() if heading else 'Untitled Section'
+            
+            # Get section content
+            content = container.get_text().strip()
+            
+            # Get section type
+            section_type = container.name
+            if container.get('class'):
+                section_type = ' '.join(container.get('class'))
+            
+            sections.append({
+                'heading': heading_text,
+                'content': content,
+                'type': section_type,
+                'word_count': len(word_tokenize(content)),
+                'position': self._get_element_position(container)
+            })
+        
+        return sections
+    
+    def _extract_topics(self, text: str) -> List[Dict[str, Any]]:
+        """Extract main topics from content."""
+        # Tokenize and clean text
+        words = word_tokenize(text.lower())
+        words = [w for w in words if w.isalnum() and w not in self.stop_words]
+        
+        # Get word frequencies
+        word_freq = Counter(words)
+        
+        # Get top topics
+        topics = []
+        for word, freq in word_freq.most_common(10):
+            topics.append({
+                'topic': word,
+                'frequency': freq,
+                'percentage': freq / len(words) * 100
+            })
+        
+        return topics
+    
+    def _analyze_readability(self, text: str) -> Dict[str, float]:
+        """Analyze text readability."""
+        sentences = sent_tokenize(text)
+        words = word_tokenize(text.lower())
+        words = [w for w in words if w.isalnum()]
+        
+        # Calculate average sentence length
+        avg_sentence_length = len(words) / max(len(sentences), 1)
+        
+        # Calculate average word length
+        avg_word_length = sum(len(w) for w in words) / max(len(words), 1)
+        
+        # Calculate Flesch Reading Ease score
+        # Formula: 206.835 - 1.015(total words/total sentences) - 84.6(total syllables/total words)
+        syllables = sum(self._count_syllables(w) for w in words)
+        flesch_score = 206.835 - 1.015 * avg_sentence_length - 84.6 * (syllables / max(len(words), 1))
+        
+        return {
+            'flesch_score': max(0, min(100, flesch_score)),
+            'avg_sentence_length': avg_sentence_length,
+            'avg_word_length': avg_word_length,
+            'syllables_per_word': syllables / max(len(words), 1)
+        }
+    
+    def _analyze_hierarchy(self, headings: List[Dict[str, Any]]) -> Dict[str, Any]:
+        """Analyze content hierarchy."""
+        # Group headings by level
+        heading_levels = {}
+        for heading in headings:
+            level = heading['level']
+            if level not in heading_levels:
+                heading_levels[level] = []
+            heading_levels[level].append(heading)
+        
+        # Calculate hierarchy metrics
+        total_headings = len(headings)
+        max_depth = max(int(level[1]) for level in heading_levels.keys()) if heading_levels else 0
+        
+        return {
+            'total_headings': total_headings,
+            'max_depth': max_depth,
+            'heading_distribution': {level: len(headings) for level, headings in heading_levels.items()},
+            'has_proper_hierarchy': self._check_proper_hierarchy(heading_levels)
+        }
+    
+    def _check_proper_hierarchy(self, heading_levels: Dict[str, List[Dict[str, Any]]]) -> bool:
+        """Check if headings follow proper hierarchy."""
+        if not heading_levels:
+            return False
+        
+        # Check if h1 exists
+        if 'h1' not in heading_levels:
+            return False
+        
+        # Check if h1 is unique
+        if len(heading_levels['h1']) > 1:
+            return False
+        
+        # Check if levels are sequential
+        levels = sorted(int(level[1]) for level in heading_levels.keys())
+        return all(levels[i] - levels[i-1] <= 1 for i in range(1, len(levels)))
+    
+    def _count_syllables(self, word: str) -> int:
+        """Count syllables in a word."""
+        word = word.lower()
+        count = 0
+        vowels = 'aeiouy'
+        word = word.lower()
+        if word[0] in vowels:
+            count += 1
+        for index in range(1, len(word)):
+            if word[index] in vowels and word[index - 1] not in vowels:
+                count += 1
+        if word.endswith('e'):
+            count -= 1
+        if count == 0:
+            count += 1
+        return count
+    
+    def _get_element_position(self, element) -> Dict[str, int]:
+        """Get element position in the document."""
+        try:
+            return {
+                'top': element.sourceline,
+                'left': element.sourcepos
+            }
+        except:
+            return {
+                'top': 0,
+                'left': 0
+            } 

lib/ai_seo_tools/content_gap_analysis/utils/data_collector.py

@@ -0,0 +1,112 @@
+"""
+Data collector utility for content gap analysis.
+"""
+
+import requests
+from bs4 import BeautifulSoup
+from typing import Dict, Any
+
+class DataCollector:
+    """
+    Collects and processes website data for analysis.
+    """
+    
+    def __init__(self):
+        """Initialize the data collector."""
+        self.headers = {
+            'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36'
+        }
+    
+    def collect(self, url: str) -> Dict[str, Any]:
+        """
+        Collect website data for analysis.
+        
+        Args:
+            url (str): The URL to collect data from
+            
+        Returns:
+            dict: Collected website data
+        """
+        try:
+            # Fetch webpage content
+            response = requests.get(url, headers=self.headers)
+            response.raise_for_status()
+            
+            # Parse HTML content
+            soup = BeautifulSoup(response.text, 'html.parser')
+            
+            # Extract relevant data
+            data = {
+                'url': url,
+                'title': self._extract_title(soup),
+                'meta_description': self._extract_meta_description(soup),
+                'headings': self._extract_headings(soup),
+                'content': self._extract_content(soup),
+                'links': self._extract_links(soup),
+                'images': self._extract_images(soup)
+            }
+            
+            return data
+            
+        except Exception as e:
+            return {
+                'error': str(e),
+                'url': url
+            }
+    
+    def _extract_title(self, soup: BeautifulSoup) -> str:
+        """Extract page title."""
+        title = soup.find('title')
+        return title.text if title else ''
+    
+    def _extract_meta_description(self, soup: BeautifulSoup) -> str:
+        """Extract meta description."""
+        meta = soup.find('meta', attrs={'name': 'description'})
+        return meta.get('content', '') if meta else ''
+    
+    def _extract_headings(self, soup: BeautifulSoup) -> Dict[str, list]:
+        """Extract all headings."""
+        headings = {}
+        for i in range(1, 7):
+            tags = soup.find_all(f'h{i}')
+            headings[f'h{i}'] = [tag.text.strip() for tag in tags]
+        return headings
+    
+    def _extract_content(self, soup: BeautifulSoup) -> str:
+        """Extract main content."""
+        # Remove script and style elements
+        for script in soup(['script', 'style']):
+            script.decompose()
+        
+        # Get text content
+        text = soup.get_text()
+        
+        # Clean up text
+        lines = (line.strip() for line in text.splitlines())
+        chunks = (phrase.strip() for line in lines for phrase in line.split("  "))
+        text = ' '.join(chunk for chunk in chunks if chunk)
+        
+        return text
+    
+    def _extract_links(self, soup: BeautifulSoup) -> list:
+        """Extract all links."""
+        links = []
+        for link in soup.find_all('a'):
+            href = link.get('href')
+            if href:
+                links.append({
+                    'url': href,
+                    'text': link.text.strip()
+                })
+        return links
+    
+    def _extract_images(self, soup: BeautifulSoup) -> list:
+        """Extract all images."""
+        images = []
+        for img in soup.find_all('img'):
+            images.append({
+                'src': img.get('src', ''),
+                'alt': img.get('alt', ''),
+                'title': img.get('title', '')
+            })
+        return images 

lib/ai_seo_tools/content_gap_analysis/utils/seo_analyzer.py

@@ -0,0 +1,237 @@
+"""
+SEO analyzer utility for content gap analysis.
+"""
+
+import requests
+from bs4 import BeautifulSoup
+from urllib.parse import urlparse, urljoin
+import re
+from typing import Dict, Any, List, Optional
+from ....utils.website_analyzer.analyzer import WebsiteAnalyzer
+
+def analyze_onpage_seo(url: str) -> Dict[str, Any]:
+    """
+    Analyze on-page SEO elements of a website.
+    
+    Args:
+        url: The URL to analyze
+        
+    Returns:
+        Dictionary containing SEO analysis results
+    """
+    try:
+        # Use the combined website analyzer
+        analyzer = WebsiteAnalyzer()
+        analysis = analyzer.analyze_website(url)
+        
+        if not analysis.get('success', False):
+            return {
+                'error': analysis.get('error', 'Unknown error in SEO analysis'),
+                'meta_title': '',
+                'meta_description': '',
+                'has_robots_txt': False,
+                'has_sitemap': False,
+                'mobile_friendly': False,
+                'load_time': 0
+            }
+        
+        # Extract relevant information from the analysis
+        seo_info = analysis['data']['analysis']['seo_info']
+        basic_info = analysis['data']['analysis']['basic_info']
+        performance = analysis['data']['analysis']['performance']
+        
+        return {
+            'meta_tags': seo_info.get('meta_tags', {}),
+            'content': seo_info.get('content', {}),
+            'meta_title': basic_info.get('title', ''),
+            'meta_description': basic_info.get('meta_description', ''),
+            'has_robots_txt': bool(basic_info.get('robots_txt')),
+            'has_sitemap': bool(basic_info.get('sitemap')),
+            'mobile_friendly': True,  # This would need to be implemented separately
+            'load_time': performance.get('load_time', 0)
+        }
+    except Exception as e:
+        return {
+            'error': str(e),
+            'meta_title': '',
+            'meta_description': '',
+            'has_robots_txt': False,
+            'has_sitemap': False,
+            'mobile_friendly': False,
+            'load_time': 0
+        }
+
+def _analyze_meta_tags(soup: BeautifulSoup) -> Dict[str, Any]:
+    """Analyze meta tags of the webpage."""
+    meta_tags = {}
+    
+    # Title tag
+    title_tag = soup.find('title')
+    if title_tag:
+        meta_tags['title'] = title_tag.string.strip()
+    
+    # Meta description
+    meta_desc = soup.find('meta', {'name': 'description'})
+    if meta_desc:
+        meta_tags['description'] = meta_desc.get('content', '').strip()
+    
+    # Meta keywords
+    meta_keywords = soup.find('meta', {'name': 'keywords'})
+    if meta_keywords:
+        meta_tags['keywords'] = meta_keywords.get('content', '').strip()
+    
+    # Open Graph tags
+    og_tags = {}
+    for tag in soup.find_all('meta', property=re.compile(r'^og:')):
+        og_tags[tag['property']] = tag.get('content', '')
+    meta_tags['og_tags'] = og_tags
+    
+    # Twitter Card tags
+    twitter_tags = {}
+    for tag in soup.find_all('meta', name=re.compile(r'^twitter:')):
+        twitter_tags[tag['name']] = tag.get('content', '')
+    meta_tags['twitter_tags'] = twitter_tags
+    
+    return meta_tags
+
+def _analyze_headings(soup: BeautifulSoup) -> Dict[str, Any]:
+    """Analyze heading structure of the webpage."""
+    headings = {
+        'h1': [],
+        'h2': [],
+        'h3': [],
+        'h4': [],
+        'h5': [],
+        'h6': []
+    }
+    
+    for tag in ['h1', 'h2', 'h3', 'h4', 'h5', 'h6']:
+        for heading in soup.find_all(tag):
+            headings[tag].append(heading.get_text().strip())
+    
+    return headings
+
+def _analyze_content(soup: BeautifulSoup) -> Dict[str, Any]:
+    """Analyze main content of the webpage."""
+    # Find main content
+    main_content = soup.find('main') or soup.find('article') or soup.find('div', class_=re.compile(r'content|main|article'))
+    
+    if not main_content:
+        return {
+            'word_count': 0,
+            'paragraph_count': 0,
+            'content': ''
+        }
+    
+    # Get text content
+    content = main_content.get_text()
+    
+    # Count words and paragraphs
+    words = content.split()
+    paragraphs = main_content.find_all('p')
+    
+    return {
+        'word_count': len(words),
+        'paragraph_count': len(paragraphs),
+        'content': content
+    }
+
+def _analyze_links(soup: BeautifulSoup, base_url: str) -> Dict[str, Any]:
+    """Analyze links on the webpage."""
+    links = {
+        'internal': [],
+        'external': [],
+        'broken': []
+    }
+    
+    base_domain = urlparse(base_url).netloc
+    
+    for link in soup.find_all('a', href=True):
+        href = link['href']
+        
+        # Handle relative URLs
+        if not href.startswith(('http://', 'https://')):
+            href = urljoin(base_url, href)
+        
+        # Categorize link
+        if urlparse(href).netloc == base_domain:
+            links['internal'].append({
+                'url': href,
+                'text': link.get_text().strip(),
+                'title': link.get('title', '')
+            })
+        else:
+            links['external'].append({
+                'url': href,
+                'text': link.get_text().strip(),
+                'title': link.get('title', '')
+            })
+    
+    return links
+
+def _analyze_images(soup: BeautifulSoup) -> Dict[str, Any]:
+    """Analyze images on the webpage."""
+    images = []
+    
+    for img in soup.find_all('img'):
+        image_data = {
+            'src': img.get('src', ''),
+            'alt': img.get('alt', ''),
+            'title': img.get('title', ''),
+            'width': img.get('width', ''),
+            'height': img.get('height', ''),
+            'has_alt': bool(img.get('alt')),
+            'has_title': bool(img.get('title')),
+            'has_dimensions': bool(img.get('width') and img.get('height'))
+        }
+        images.append(image_data)
+    
+    return {
+        'total': len(images),
+        'with_alt': sum(1 for img in images if img['has_alt']),
+        'with_title': sum(1 for img in images if img['has_title']),
+        'with_dimensions': sum(1 for img in images if img['has_dimensions']),
+        'images': images
+    }
+
+def _check_technical_elements(soup: BeautifulSoup, url: str) -> Dict[str, Any]:
+    """Check technical SEO elements."""
+    base_url = urlparse(url)
+    domain = base_url.netloc
+    
+    # Check robots.txt
+    robots_url = f"{base_url.scheme}://{domain}/robots.txt"
+    try:
+        robots_response = requests.get(robots_url, timeout=5)
+        has_robots_txt = robots_response.status_code == 200
+    except:
+        has_robots_txt = False
+    
+    # Check sitemap
+    sitemap_url = f"{base_url.scheme}://{domain}/sitemap.xml"
+    try:
+        sitemap_response = requests.get(sitemap_url, timeout=5)
+        has_sitemap = sitemap_response.status_code == 200
+    except:
+        has_sitemap = False
+    
+    # Check mobile friendliness
+    viewport = soup.find('meta', {'name': 'viewport'})
+    has_viewport = bool(viewport)
+    
+    # Check canonical URL
+    canonical = soup.find('link', {'rel': 'canonical'})
+    has_canonical = bool(canonical)
+    
+    # Check language
+    html_lang = soup.find('html').get('lang', '')
+    has_language = bool(html_lang)
+    
+    return {
+        'has_robots_txt': has_robots_txt,
+        'has_sitemap': has_sitemap,
+        'mobile_friendly': has_viewport,
+        'has_canonical': has_canonical,
+        'has_language': has_language,
+        'language': html_lang
+    } 

lib/ai_seo_tools/content_gap_analysis/utils/storage.py

@@ -0,0 +1,270 @@
+"""
+Storage module for content gap analysis results.
+"""
+
+from typing import Dict, Any, List, Optional
+from datetime import datetime
+from sqlalchemy.orm import Session
+from sqlalchemy.exc import SQLAlchemyError
+import streamlit as st
+
+class ContentGapAnalysisStorage:
+    """Handles storage and retrieval of content gap analysis results."""
+    
+    def __init__(self, db_session: Session):
+        """Initialize the storage handler."""
+        self.db = db_session
+    
+    def save_analysis(self, user_id: int, website_url: str, industry: str, results: Dict[str, Any]) -> Optional[int]:
+        """
+        Save content gap analysis results.
+        
+        Args:
+            user_id: User ID
+            website_url: Target website URL
+            industry: Industry category
+            results: Analysis results dictionary
+            
+        Returns:
+            Analysis ID if successful, None otherwise
+        """
+        try:
+            # Create main analysis record
+            analysis = ContentGapAnalysis(
+                user_id=user_id,
+                website_url=website_url,
+                industry=industry,
+                status='completed',
+                metadata={'version': '1.0'}
+            )
+            self.db.add(analysis)
+            self.db.flush()  # Get the ID without committing
+            
+            # Save website analysis
+            website_analysis = WebsiteAnalysis(
+                content_gap_analysis_id=analysis.id,
+                content_score=results.get('website', {}).get('content_score', 0),
+                seo_score=results.get('website', {}).get('seo_score', 0),
+                structure_score=results.get('website', {}).get('structure_score', 0),
+                content_metrics=results.get('website', {}).get('content_metrics', {}),
+                seo_metrics=results.get('website', {}).get('seo_metrics', {}),
+                technical_metrics=results.get('website', {}).get('technical_metrics', {}),
+                ai_insights=results.get('website', {}).get('ai_insights', {})
+            )
+            self.db.add(website_analysis)
+            
+            # Save competitor analysis if available
+            if 'competitors' in results:
+                for competitor in results['competitors']:
+                    competitor_analysis = CompetitorAnalysis(
+                        content_gap_analysis_id=analysis.id,
+                        competitor_url=competitor.get('url'),
+                        market_position=competitor.get('market_position', {}),
+                        content_gaps=competitor.get('content_gaps', []),
+                        competitive_advantages=competitor.get('competitive_advantages', []),
+                        trend_analysis=competitor.get('trend_analysis', {})
+                    )
+                    self.db.add(competitor_analysis)
+            
+            # Save keyword analysis
+            keyword_analysis = KeywordAnalysis(
+                content_gap_analysis_id=analysis.id,
+                top_keywords=results.get('keywords', {}).get('top_keywords', []),
+                search_intent=results.get('keywords', {}).get('search_intent', {}),
+                opportunities=results.get('keywords', {}).get('opportunities', []),
+                trend_analysis=results.get('keywords', {}).get('trend_analysis', {})
+            )
+            self.db.add(keyword_analysis)
+            
+            # Save recommendations
+            for recommendation in results.get('recommendations', []):
+                content_recommendation = ContentRecommendation(
+                    content_gap_analysis_id=analysis.id,
+                    recommendation_type=recommendation.get('type'),
+                    priority_score=recommendation.get('priority_score', 0),
+                    recommendation=recommendation.get('recommendation', ''),
+                    implementation_steps=recommendation.get('implementation_steps', []),
+                    expected_impact=recommendation.get('expected_impact', {}),
+                    status='pending'
+                )
+                self.db.add(content_recommendation)
+            
+            # Save analysis history
+            history = AnalysisHistory(
+                content_gap_analysis_id=analysis.id,
+                status='completed',
+                metrics={'duration': results.get('duration', 0)}
+            )
+            self.db.add(history)
+            
+            # Commit all changes
+            self.db.commit()
+            return analysis.id
+            
+        except SQLAlchemyError as e:
+            self.db.rollback()
+            st.error(f"Error saving analysis results: {str(e)}")
+            return None
+    
+    def get_analysis(self, analysis_id: int) -> Optional[Dict[str, Any]]:
+        """
+        Retrieve content gap analysis results.
+        
+        Args:
+            analysis_id: Analysis ID
+            
+        Returns:
+            Dictionary containing analysis results if found, None otherwise
+        """
+        try:
+            analysis = self.db.query(ContentGapAnalysis).get(analysis_id)
+            if not analysis:
+                return None
+            
+            # Get website analysis
+            website_analysis = self.db.query(WebsiteAnalysis).filter_by(
+                content_gap_analysis_id=analysis_id
+            ).first()
+            
+            # Get competitor analysis
+            competitor_analyses = self.db.query(CompetitorAnalysis).filter_by(
+                content_gap_analysis_id=analysis_id
+            ).all()
+            
+            # Get keyword analysis
+            keyword_analysis = self.db.query(KeywordAnalysis).filter_by(
+                content_gap_analysis_id=analysis_id
+            ).first()
+            
+            # Get recommendations
+            recommendations = self.db.query(ContentRecommendation).filter_by(
+                content_gap_analysis_id=analysis_id
+            ).all()
+            
+            # Get analysis history
+            history = self.db.query(AnalysisHistory).filter_by(
+                content_gap_analysis_id=analysis_id
+            ).order_by(AnalysisHistory.run_date.desc()).all()
+            
+            return {
+                'id': analysis.id,
+                'website_url': analysis.website_url,
+                'industry': analysis.industry,
+                'analysis_date': analysis.analysis_date,
+                'status': analysis.status,
+                'website': {
+                    'content_score': website_analysis.content_score,
+                    'seo_score': website_analysis.seo_score,
+                    'structure_score': website_analysis.structure_score,
+                    'content_metrics': website_analysis.content_metrics,
+                    'seo_metrics': website_analysis.seo_metrics,
+                    'technical_metrics': website_analysis.technical_metrics,
+                    'ai_insights': website_analysis.ai_insights
+                } if website_analysis else {},
+                'competitors': [{
+                    'url': ca.competitor_url,
+                    'market_position': ca.market_position,
+                    'content_gaps': ca.content_gaps,
+                    'competitive_advantages': ca.competitive_advantages,
+                    'trend_analysis': ca.trend_analysis
+                } for ca in competitor_analyses],
+                'keywords': {
+                    'top_keywords': keyword_analysis.top_keywords,
+                    'search_intent': keyword_analysis.search_intent,
+                    'opportunities': keyword_analysis.opportunities,
+                    'trend_analysis': keyword_analysis.trend_analysis
+                } if keyword_analysis else {},
+                'recommendations': [{
+                    'type': r.recommendation_type,
+                    'priority_score': r.priority_score,
+                    'recommendation': r.recommendation,
+                    'implementation_steps': r.implementation_steps,
+                    'expected_impact': r.expected_impact,
+                    'status': r.status
+                } for r in recommendations],
+                'history': [{
+                    'run_date': h.run_date,
+                    'status': h.status,
+                    'metrics': h.metrics,
+                    'error_log': h.error_log
+                } for h in history]
+            }
+            
+        except SQLAlchemyError as e:
+            st.error(f"Error retrieving analysis results: {str(e)}")
+            return None
+    
+    def get_user_analyses(self, user_id: int) -> List[Dict[str, Any]]:
+        """
+        Get all analyses for a user.
+        
+        Args:
+            user_id: User ID
+            
+        Returns:
+            List of analysis summaries
+        """
+        try:
+            analyses = self.db.query(ContentGapAnalysis).filter_by(
+                user_id=user_id
+            ).order_by(ContentGapAnalysis.analysis_date.desc()).all()
+            
+            return [{
+                'id': analysis.id,
+                'website_url': analysis.website_url,
+                'industry': analysis.industry,
+                'analysis_date': analysis.analysis_date,
+                'status': analysis.status
+            } for analysis in analyses]
+            
+        except SQLAlchemyError as e:
+            st.error(f"Error retrieving user analyses: {str(e)}")
+            return []
+    
+    def update_recommendation_status(self, recommendation_id: int, status: str) -> bool:
+        """
+        Update the status of a recommendation.
+        
+        Args:
+            recommendation_id: Recommendation ID
+            status: New status
+            
+        Returns:
+            True if successful, False otherwise
+        """
+        try:
+            recommendation = self.db.query(ContentRecommendation).get(recommendation_id)
+            if recommendation:
+                recommendation.status = status
+                recommendation.updated_at = datetime.utcnow()
+                self.db.commit()
+                return True
+            return False
+            
+        except SQLAlchemyError as e:
+            self.db.rollback()
+            st.error(f"Error updating recommendation status: {str(e)}")
+            return False
+    
+    def delete_analysis(self, analysis_id: int) -> bool:
+        """
+        Delete an analysis and all related data.
+        
+        Args:
+            analysis_id: Analysis ID
+            
+        Returns:
+            True if successful, False otherwise
+        """
+        try:
+            analysis = self.db.query(ContentGapAnalysis).get(analysis_id)
+            if analysis:
+                self.db.delete(analysis)
+                self.db.commit()
+                return True
+            return False
+            
+        except SQLAlchemyError as e:
+            self.db.rollback()
+            st.error(f"Error deleting analysis: {str(e)}")
+            return False 

lib/ai_seo_tools/content_gap_analysis/website_analyzer.py

@@ -0,0 +1,291 @@
+"""Website analyzer module for content gap analysis."""
+
+import streamlit as st
+from loguru import logger
+from typing import Dict, Any, List, Optional
+import asyncio
+import sys
+import os
+import json
+from lib.utils.website_analyzer.analyzer import WebsiteAnalyzer as BaseWebsiteAnalyzer
+from lib.gpt_providers.text_generation.main_text_generation import llm_text_gen
+
+# Configure logger
+logger.remove()  # Remove default handler
+logger.add(
+    "logs/content_gap_website_analyzer.log",
+    rotation="50 MB",
+    retention="10 days",
+    level="DEBUG",
+    format="{time:YYYY-MM-DD HH:mm:ss} | {level} | {message}"
+)
+logger.add(
+    sys.stdout,
+    level="INFO",
+    format="<green>{time:YYYY-MM-DD HH:mm:ss}</green> | <level>{level: <8}</level> | <cyan>{message}</cyan>"
+)
+
+# Ensure logs directory exists
+os.makedirs("logs", exist_ok=True)
+
+class WebsiteAnalyzer(BaseWebsiteAnalyzer):
+    """Extended website analyzer for content gap analysis."""
+    
+    def __init__(self):
+        """Initialize the website analyzer."""
+        super().__init__()
+        logger.info("ContentGapWebsiteAnalyzer initialized")
+    
+    def analyze_content_gaps(self, url: str, competitor_urls: List[str]) -> Dict[str, Any]:
+        """
+        Analyze content gaps between the target website and competitors.
+        
+        Args:
+            url: The target URL to analyze
+            competitor_urls: List of competitor URLs to compare against
+            
+        Returns:
+            Dictionary containing content gap analysis results
+        """
+        try:
+            # Analyze target website
+            target_analysis = self.analyze_website(url)
+            if not target_analysis.get('success', False):
+                return {
+                    'error': target_analysis.get('error', 'Unknown error in target analysis'),
+                    'gaps': [],
+                    'recommendations': []
+                }
+            
+            # Analyze competitor websites
+            competitor_analyses = []
+            for competitor_url in competitor_urls:
+                analysis = self.analyze_website(competitor_url)
+                if analysis.get('success', False):
+                    competitor_analyses.append(analysis['data'])
+            
+            # Generate content gap analysis using AI
+            prompt = f"""Analyze content gaps between the target website and competitors:
+            
+            Target Website:
+            {json.dumps(target_analysis['data'], indent=2)}
+            
+            Competitor Websites:
+            {json.dumps(competitor_analyses, indent=2)}
+            
+            Identify:
+            1. Missing content topics
+            2. Content depth differences
+            3. Keyword gaps
+            4. Content structure improvements
+            5. Content quality recommendations
+            
+            Format the response as JSON with 'gaps' and 'recommendations' keys."""
+            
+            # Get AI analysis
+            analysis = llm_text_gen(
+                prompt=prompt,
+                system_prompt="You are an SEO expert specializing in content gap analysis.",
+                response_format="json_object"
+            )
+            
+            if not analysis:
+                return {
+                    'error': 'Failed to generate content gap analysis',
+                    'gaps': [],
+                    'recommendations': []
+                }
+            
+            return {
+                'gaps': analysis.get('gaps', []),
+                'recommendations': analysis.get('recommendations', [])
+            }
+            
+        except Exception as e:
+            error_msg = f"Error analyzing content gaps: {str(e)}"
+            logger.error(error_msg, exc_info=True)
+            return {
+                'error': error_msg,
+                'gaps': [],
+                'recommendations': []
+            }
+
+    def analyze(self, url: str) -> Dict[str, Any]:
+        """
+        Analyze a website for content gaps and SEO opportunities.
+        
+        Args:
+            url: The URL to analyze
+            
+        Returns:
+            Dictionary containing analysis results
+        """
+        try:
+            # Initialize progress tracking
+            progress = {
+                'status': 'in_progress',
+                'current_stage': 'content_analysis',
+                'current_step': 'Initializing analysis',
+                'progress': 0,
+                'details': 'Starting website analysis...'
+            }
+            self.progress.update(progress)
+            
+            # Get base website analysis
+            logger.info("Starting base website analysis")
+            website_analysis = self.analyze_website(url)
+            
+            if not website_analysis.get('success', False):
+                error_msg = website_analysis.get('error', 'Unknown error in website analysis')
+                logger.error(f"Error in website analysis: {error_msg}")
+                progress['status'] = 'error'
+                progress['details'] = error_msg
+                self.progress.update(progress)
+                return {
+                    'error': error_msg,
+                    'error_details': website_analysis.get('error_details', {}),
+                    'progress': progress
+                }
+            
+            # Extract SEO metrics from the analysis
+            seo_metrics = self._extract_seo_metrics(website_analysis['data'])
+            
+            # Extract performance metrics
+            performance_metrics = self._extract_performance_metrics(website_analysis['data'])
+            
+            # Update progress
+            progress['status'] = 'completed'
+            progress['progress'] = 100
+            progress['details'] = 'Analysis completed successfully'
+            self.progress.update(progress)
+            
+            return {
+                'success': True,
+                'data': {
+                    'seo_metrics': seo_metrics,
+                    'performance_metrics': performance_metrics,
+                    'website_analysis': website_analysis['data']
+                },
+                'progress': progress
+            }
+            
+        except Exception as e:
+            error_msg = f"Error in content gap analysis: {str(e)}"
+            logger.error(error_msg, exc_info=True)
+            progress['status'] = 'error'
+            progress['details'] = error_msg
+            self.progress.update(progress)
+            return {
+                'error': error_msg,
+                'error_details': {
+                    'type': type(e).__name__,
+                    'traceback': str(e.__traceback__)
+                },
+                'progress': progress
+            }
+    
+    def _extract_seo_metrics(self, website_analysis: Dict[str, Any]) -> Dict[str, Any]:
+        """Extract SEO-related metrics from website analysis."""
+        try:
+            seo_info = website_analysis.get('analysis', {}).get('seo_info', {})
+            return {
+                'overall_score': seo_info.get('overall_score', 0),
+                'meta_tags': {
+                    'title': seo_info.get('meta_tags', {}).get('title', {}),
+                    'description': seo_info.get('meta_tags', {}).get('description', {}),
+                    'keywords': seo_info.get('meta_tags', {}).get('keywords', {})
+                },
+                'content': {
+                    'word_count': seo_info.get('content', {}).get('word_count', 0),
+                    'readability_score': seo_info.get('content', {}).get('readability_score', 0),
+                    'content_quality_score': seo_info.get('content', {}).get('content_quality_score', 0)
+                }
+            }
+        except Exception as e:
+            logger.error(f"Error extracting SEO metrics: {str(e)}", exc_info=True)
+            return {}
+    
+    def _extract_performance_metrics(self, website_analysis: Dict[str, Any]) -> Dict[str, Any]:
+        """Extract performance metrics from website analysis."""
+        try:
+            performance_info = website_analysis.get('analysis', {}).get('performance', {})
+            return {
+                'load_time': performance_info.get('load_time', 0),
+                'page_size': performance_info.get('page_size', 0),
+                'resource_count': performance_info.get('resource_count', 0),
+                'performance_score': performance_info.get('performance_score', 0)
+            }
+        except Exception as e:
+            logger.error(f"Error extracting performance metrics: {str(e)}", exc_info=True)
+            return {}
+    
+    def _extract_content_metrics(self, website_analysis: Dict[str, Any]) -> Dict[str, Any]:
+        """Extract content-related metrics from website analysis."""
+        try:
+            content_info = website_analysis['analysis']['content_info']
+            return {
+                'word_count': content_info.get('word_count', 0),
+                'heading_count': content_info.get('heading_count', 0),
+                'image_count': content_info.get('image_count', 0),
+                'link_count': content_info.get('link_count', 0),
+                'has_meta_description': content_info.get('has_meta_description', False),
+                'has_robots_txt': content_info.get('has_robots_txt', False),
+                'has_sitemap': content_info.get('has_sitemap', False)
+            }
+        except Exception as e:
+            logger.error(f"Error extracting content metrics: {str(e)}", exc_info=True)
+            return {}
+    
+    def _extract_technical_info(self, website_analysis: Dict[str, Any]) -> Dict[str, Any]:
+        """Extract technical information from website analysis."""
+        try:
+            basic_info = website_analysis.get('analysis', {}).get('basic_info', {})
+            return {
+                'title': basic_info.get('title', ''),
+                'meta_description': basic_info.get('meta_description', ''),
+                'headers': basic_info.get('headers', {}),
+                'robots_txt': basic_info.get('robots_txt', ''),
+                'sitemap': basic_info.get('sitemap', ''),
+                'server_info': basic_info.get('server_info', {}),
+                'security_info': basic_info.get('security_info', {})
+            }
+        except Exception as e:
+            logger.error(f"Error extracting technical info: {str(e)}", exc_info=True)
+            return {}
+    
+    def _generate_insights(self, content_metrics: Dict[str, Any], seo_metrics: Dict[str, Any]) -> List[str]:
+        """Generate content insights based on analysis results."""
+        try:
+            insights = []
+            
+            # Content insights
+            if content_metrics['word_count'] < 300:
+                insights.append("Content length is below recommended minimum (300 words)")
+            elif content_metrics['word_count'] > 2000:
+                insights.append("Content length is above recommended maximum (2000 words)")
+                
+            if content_metrics['heading_count'] < 2:
+                insights.append("Content structure could be improved with more headings")
+                
+            if content_metrics['image_count'] == 0:
+                insights.append("Consider adding images to improve content engagement")
+                
+            # SEO insights
+            if seo_metrics.get('overall_score', 0) < 60:
+                insights.append("SEO optimization needs significant improvement")
+            elif seo_metrics.get('overall_score', 0) < 80:
+                insights.append("SEO optimization has room for improvement")
+                
+            if not content_metrics['has_meta_description']:
+                insights.append("Missing meta description - important for SEO")
+                
+            if not content_metrics['has_robots_txt']:
+                insights.append("Missing robots.txt - important for search engine crawling")
+                
+            if not content_metrics['has_sitemap']:
+                insights.append("Missing sitemap.xml - important for search engine indexing")
+                
+            return insights
+        except Exception as e:
+            logger.error(f"Error generating insights: {str(e)}", exc_info=True)
+            return [] 

lib/ai_seo_tools/content_title_generator.py

@@ -1,3 +1,5 @@
+"""Content title generator module."""
+
 import os
 import json
 import streamlit as st
@@ -6,70 +8,106 @@ from tenacity import (
     stop_after_attempt,
     wait_random_exponential,
 )
-from ..gpt_providers.text_generation.main_text_generation import llm_text_gen
+from loguru import logger
+from typing import Dict, Any, List, Optional
+import asyncio
+import sys
+from lib.gpt_providers.text_generation.main_text_generation import llm_text_gen
+from lib.utils.website_analyzer.analyzer import WebsiteAnalyzer
 
-def ai_title_generator():
+# Configure logger
-    """ UI for the AI Blog Title Generator """
+logger.remove()  # Remove default handler
-    st.title("✍️ Alwrity - AI Blog Title Generator")
+logger.add(
+    "logs/content_title_generator.log",
+    rotation="50 MB",
+    retention="10 days",
+    level="DEBUG",
+    format="{time:YYYY-MM-DD HH:mm:ss} | {level} | {message}"
+)
+logger.add(
+    sys.stdout,
+    level="INFO",
+    format="<green>{time:YYYY-MM-DD HH:mm:ss}</green> | <level>{level: <8}</level> | <cyan>{message}</cyan>"
+)
 
-    # Input section
+# Ensure logs directory exists
-    with st.expander("**PRO-TIP** - Follow the steps below for best results.", expanded=True):
+os.makedirs("logs", exist_ok=True)
-        col1, col2 = st.columns([5, 5])
-
-        with col1:
-            input_blog_keywords = st.text_input(
-                '**🔑 Enter main keywords of your blog!**',
-                placeholder="e.g., AI tools, digital marketing, SEO",
-                help="Use 2-3 words that best describe the main topic of your blog."
-            )
-            input_blog_content = st.text_area(
-                '**📄 Copy/Paste your entire blog content.** (Optional)',
-                placeholder="e.g., Content about the importance of AI in digital marketing...",
-                help="Paste your full blog content here for more accurate title suggestions. This is optional."
-            )
-
-        with col2:
-            input_title_type = st.selectbox(
-                '📝 Blog Type', 
-                ('General', 'How-to Guides', 'Tutorials', 'Listicles', 'Newsworthy Posts', 'FAQs', 'Checklists/Cheat Sheets'),
-                index=0
-            )
-            input_title_intent = st.selectbox(
-                '🔍 Search Intent', 
-                ('Informational Intent', 'Commercial Intent', 'Transactional Intent', 'Navigational Intent'), 
-                index=0
-            )
-            language_options = ["English", "Spanish", "French", "German", "Chinese", "Japanese", "Other"]
-            input_language = st.selectbox(
-                '🌐 Select Language', 
-                options=language_options,
-                index=0,
-                help="Choose the language for your blog title."
-            )
-            if input_language == "Other":
-                input_language = st.text_input(
-                    'Specify Language', 
-                    placeholder="e.g., Italian, Dutch",
-                    help="Specify your preferred language."
-                )
-
-    # Generate Blog Title button
-    if st.button('**Generate Blog Titles**'):
-        with st.spinner("Generating blog titles..."):
-            if input_blog_content == 'Optional':
-                input_blog_content = None
-
-            if not input_blog_keywords and not input_blog_content:
-                st.error('**🫣 Provide Inputs to generate Blog Titles. Either Blog Keywords OR content is required!**')
-            else:
-                blog_titles = generate_blog_titles(input_blog_keywords, input_blog_content, input_title_type, input_title_intent, input_language)
-                if blog_titles:
-                    st.subheader('**👩🧕🔬 Go Rule search ranking with these Blog Titles!**')
-                    with st.expander("**Final - Blog Titles Output 🎆🎇🎇**", expanded=True):
-                        st.markdown(blog_titles)
-                else:
-                    st.error("💥 **Failed to generate blog titles. Please try again!**")
 
+def ai_title_generator(url: str) -> Dict[str, Any]:
+    """
+    Generate SEO-optimized titles using AI.
+    
+    Args:
+        url: The URL to analyze
+        
+    Returns:
+        Dictionary containing title suggestions and analysis
+    """
+    try:
+        # Initialize analyzer
+        analyzer = WebsiteAnalyzer()
+        
+        # Analyze website
+        analysis = analyzer.analyze_website(url)
+        if not analysis.get('success', False):
+            return {
+                'error': analysis.get('error', 'Unknown error in analysis'),
+                'patterns': {},
+                'suggestions': []
+            }
+        
+        # Extract content and meta information
+        content_info = analysis['data']['analysis']['content_info']
+        seo_info = analysis['data']['analysis']['seo_info']
+        
+        # Generate title suggestions using AI
+        prompt = f"""Based on the following website content and SEO analysis, generate 5 SEO-optimized title suggestions:
+        
+        Content Analysis:
+        - Word Count: {content_info.get('word_count', 0)}
+        - Heading Structure: {content_info.get('heading_structure', {})}
+        
+        SEO Analysis:
+        - Meta Title: {seo_info.get('meta_tags', {}).get('title', {}).get('value', '')}
+        - Meta Description: {seo_info.get('meta_tags', {}).get('description', {}).get('value', '')}
+        - Keywords: {seo_info.get('meta_tags', {}).get('keywords', {}).get('value', '')}
+        
+        Generate 5 title suggestions that are:
+        1. SEO-optimized
+        2. Engaging and click-worthy
+        3. Between 50-60 characters
+        4. Include relevant keywords
+        5. Follow best practices for title optimization
+        
+        Format the response as JSON with 'suggestions' and 'patterns' keys."""
+        
+        # Get AI suggestions
+        suggestions = llm_text_gen(
+            prompt=prompt,
+            system_prompt="You are an SEO expert specializing in title optimization.",
+            response_format="json_object"
+        )
+        
+        if not suggestions:
+            return {
+                'error': 'Failed to generate title suggestions',
+                'patterns': {},
+                'suggestions': []
+            }
+        
+        return {
+            'patterns': suggestions.get('patterns', {}),
+            'suggestions': suggestions.get('suggestions', [])
+        }
+        
+    except Exception as e:
+        error_msg = f"Error generating title suggestions: {str(e)}"
+        logger.error(error_msg, exc_info=True)
+        return {
+            'error': error_msg,
+            'patterns': {},
+            'suggestions': []
+        }
 
 @retry(stop=stop_after_attempt(3), wait=wait_random_exponential(min=1, max=4))
 def generate_blog_titles(input_blog_keywords, input_blog_content, input_title_type, input_title_intent, input_language):

lib/ai_writers/ai_facebook_writer/facebook_ai_writer.py

@@ -194,7 +194,7 @@ def facebook_main_menu():
     if st.session_state.selected_tool is not None:
         with tool_container:
             # Add a back button at the top
-            if st.button("← Back to Dashboard", key="back_to_dashboard"):
+            if st.button("← Back to Dashboard", key="back_to_facebook_dashboard"):
                 st.session_state.selected_tool = None
                 st.rerun()
             

lib/ai_writers/ai_finance_report_generator/README.md

@@ -0,0 +1,190 @@
+# AI Finance Report Generator
+
+An advanced AI-powered financial analysis and report generation system that combines data collection, technical analysis, visualization, and automated report generation.
+
+## Project Structure
+
+```
+ai_finance_report_generator/
+├── ai_financial_dashboard.py    # Main dashboard interface
+├── utils/                       # Utility functions
+│   ├── __init__.py
+│   └── storage.py              # Data persistence
+├── reports/                     # Report generation modules
+│   ├── technical_analysis/     # Technical analysis reports
+│   ├── fundamental_analysis/   # Fundamental analysis reports
+│   ├── options_analysis/      # Options analysis reports
+│   ├── portfolio_analysis/    # Portfolio analysis reports
+│   ├── market_research/       # Market research reports
+│   └── news_analysis/         # News analysis reports
+└── README.md                   # This file
+```
+
+## Features
+
+### Current Features
+- Unified dashboard interface for all financial analysis tools
+- Technical Analysis report generation
+- Options analysis report generation
+- User preferences management
+- Recent reports tracking
+- Data persistence with JSON storage
+- Financial data collection from various sources
+- Integration with LLM for report generation
+
+### Planned Features
+
+#### 1. Data Collection Module
+- Web scraping for financial news and data
+- API integrations (Yahoo Finance, Alpha Vantage, Financial Modeling Prep)
+- Real-time market data collection
+- Historical data retrieval
+- Company financial statements
+- Market sentiment data
+- Economic indicators
+- Sector analysis data
+
+#### 2. Technical Analysis Module
+- Moving averages (SMA, EMA, WMA)
+- RSI, MACD, Bollinger Bands
+- Volume analysis
+- Support/Resistance levels
+- Trend analysis
+- Pattern recognition
+- Fibonacci retracements
+- Momentum indicators
+
+#### 3. Fundamental Analysis Module
+- Financial ratios calculation
+- Company valuation metrics
+- Growth analysis
+- Profitability analysis
+- Debt analysis
+- Cash flow analysis
+- Industry comparison
+- Peer analysis
+
+#### 4. Data Visualization Module
+- Candlestick charts
+- Technical indicator overlays
+- Volume charts
+- Price action patterns
+- Correlation matrices
+- Heat maps
+- Interactive charts
+- Custom chart templates
+
+#### 5. Report Generation Module
+- Technical analysis reports
+- Fundamental analysis reports
+- Market research reports
+- Investment recommendations
+- Risk assessment reports
+- Sector analysis reports
+- News impact analysis
+- Custom report templates
+
+#### 6. News and Sentiment Analysis Module
+- News aggregation
+- Sentiment scoring
+- Social media analysis
+- Market sentiment indicators
+- News impact analysis
+- Event correlation
+- Trend detection
+- Sentiment visualization
+
+#### 7. Portfolio Analysis Module
+- Portfolio performance analysis
+- Risk assessment
+- Asset allocation
+- Correlation analysis
+- Diversification metrics
+- Performance attribution
+- Portfolio optimization
+- Rebalancing suggestions
+
+## Usage
+
+### Basic Usage
+
+```python
+from lib.ai_writers.ai_finance_report_generator.ai_financial_dashboard import get_dashboard
+
+# Get dashboard instance
+dashboard = get_dashboard()
+
+# Generate technical analysis report
+ta_report = dashboard.generate_technical_analysis("AAPL")
+
+# Generate options analysis report
+options_report = dashboard.generate_options_analysis("AAPL")
+
+# Get recent reports
+recent_reports = dashboard.get_recent_reports()
+```
+
+### User Preferences
+
+```python
+# Update user preferences
+dashboard.update_preferences({
+    "report_format": "markdown",
+    "include_charts": True,
+    "chart_style": "dark",
+    "language": "en"
+})
+
+# Get current preferences
+preferences = dashboard.get_preferences()
+```
+
+### Portfolio Analysis
+
+```python
+# Create portfolio
+portfolio = [
+    {"symbol": "AAPL", "shares": 100},
+    {"symbol": "GOOGL", "shares": 50}
+]
+
+# Generate portfolio report
+portfolio_report = dashboard.generate_portfolio_analysis(portfolio)
+```
+
+## Installation
+
+```bash
+pip install -r requirements.txt
+```
+
+## Dependencies
+
+1. **Data Collection**
+   - `finance_data_researcher`
+   - `web_scraping_tools`
+
+2. **Analysis Tools**
+   - `pandas_ta`
+   - `numpy`
+   - `scipy`
+
+3. **Visualization**
+   - `matplotlib`
+   - `plotly`
+
+4. **Text Generation**
+   - `llm_text_gen`
+   - `gpt_providers`
+
+## Contributing
+
+1. Fork the repository
+2. Create your feature branch (`git checkout -b feature/AmazingFeature`)
+3. Commit your changes (`git commit -m 'Add some AmazingFeature'`)
+4. Push to the branch (`git push origin feature/AmazingFeature`)
+5. Open a Pull Request
+
+## License
+
+This project is licensed under the MIT License - see the LICENSE file for details. 

lib/ai_writers/ai_finance_report_generator/ai_financial_dashboard.py

@@ -0,0 +1,358 @@
+"""
+AI Financial Dashboard Module
+
+This module combines the financial dashboard interface with financial report generation capabilities.
+It provides a unified interface for managing financial analysis tools and generating reports.
+"""
+
+import sys
+import os
+from textwrap import dedent
+from pathlib import Path
+from datetime import datetime
+from typing import Dict, List, Any, Optional, Union
+
+from loguru import logger
+logger.remove()
+logger.add(sys.stdout,
+        colorize=True,
+        format="<level>{level}</level>|<green>{file}:{line}:{function}</green>| {message}"
+    )
+
+from ...ai_web_researcher.finance_data_researcher import get_finance_data, get_fin_options_data
+from ...gpt_providers.text_generation.main_text_generation import llm_text_gen
+from .utils import get_feature_status
+from .utils.storage import get_storage_manager
+
+class UserPreferences:
+    """Class to manage user preferences and settings."""
+    
+    def __init__(self):
+        self.default_settings = {
+            "theme": "light",
+            "currency": "USD",
+            "timezone": "UTC",
+            "date_format": "%Y-%m-%d",
+            "default_symbols": [],
+            "notifications": True,
+            "auto_refresh": False,
+            "refresh_interval": 300,  # 5 minutes
+            "report_format": "markdown",
+            "include_charts": True,
+            "chart_style": "default",
+            "language": "en"
+        }
+        self.settings = self.default_settings.copy()
+        self.storage = get_storage_manager()
+        self.load_settings()
+    
+    def update_setting(self, key: str, value: Any) -> None:
+        """Update a specific setting."""
+        if key in self.default_settings:
+            self.settings[key] = value
+            self.save_settings()
+    
+    def get_setting(self, key: str) -> Any:
+        """Get a specific setting value."""
+        return self.settings.get(key, self.default_settings.get(key))
+    
+    def reset_settings(self) -> None:
+        """Reset all settings to default values."""
+        self.settings = self.default_settings.copy()
+        self.save_settings()
+    
+    def save_settings(self) -> None:
+        """Save current settings to storage."""
+        self.storage.save_user_preferences(self.settings)
+    
+    def load_settings(self) -> None:
+        """Load settings from storage."""
+        stored_settings = self.storage.load_user_preferences()
+        if stored_settings:
+            self.settings.update(stored_settings)
+
+class RecentReport:
+    """Class to represent a recently generated report."""
+    
+    def __init__(self, report_type: str, symbol: Optional[str], timestamp: datetime, content: Optional[str] = None):
+        self.report_type = report_type
+        self.symbol = symbol
+        self.timestamp = timestamp
+        self.content = content
+        self.id = f"{report_type}_{symbol}_{timestamp.strftime('%Y%m%d%H%M%S')}"
+    
+    def to_dict(self) -> Dict[str, Any]:
+        """Convert report to dictionary format."""
+        return {
+            "id": self.id,
+            "type": self.report_type,
+            "symbol": self.symbol,
+            "timestamp": self.timestamp.isoformat(),
+            "content": self.content
+        }
+    
+    @classmethod
+    def from_dict(cls, data: Dict[str, Any]) -> 'RecentReport':
+        """Create report from dictionary format."""
+        return cls(
+            report_type=data["type"],
+            symbol=data["symbol"],
+            timestamp=datetime.fromisoformat(data["timestamp"]),
+            content=data.get("content")
+        )
+
+class FinancialDashboard:
+    """Main dashboard class for managing financial analysis tools and generating reports."""
+    
+    def __init__(self):
+        self.features = {
+            "technical_analysis": {
+                "name": "Technical Analysis",
+                "description": "Generate technical analysis reports with indicators and patterns",
+                "icon": "📊",
+                "route": "/technical-analysis",
+                "category": "analysis",
+                "dependencies": ["data_collection"],
+                "version": "1.0.0"
+            },
+            "fundamental_analysis": {
+                "name": "Fundamental Analysis",
+                "description": "Analyze company financials and valuation metrics",
+                "icon": "📈",
+                "route": "/fundamental-analysis",
+                "category": "analysis",
+                "dependencies": ["data_collection"],
+                "version": "0.1.0"
+            },
+            "options_analysis": {
+                "name": "Options Analysis",
+                "description": "Analyze options chains and generate trading strategies",
+                "icon": "⚡",
+                "route": "/options-analysis",
+                "category": "analysis",
+                "dependencies": ["data_collection", "options_data"],
+                "version": "1.0.0"
+            },
+            "portfolio_analysis": {
+                "name": "Portfolio Analysis",
+                "description": "Analyze portfolio performance and risk metrics",
+                "icon": "📑",
+                "route": "/portfolio-analysis",
+                "category": "portfolio",
+                "dependencies": ["data_collection", "portfolio_data"],
+                "version": "0.1.0"
+            },
+            "market_research": {
+                "name": "Market Research",
+                "description": "Generate market research reports and sector analysis",
+                "icon": "🔍",
+                "route": "/market-research",
+                "category": "research",
+                "dependencies": ["data_collection", "news_data"],
+                "version": "0.1.0"
+            },
+            "news_analysis": {
+                "name": "News Analysis",
+                "description": "Analyze news impact and market sentiment",
+                "icon": "📰",
+                "route": "/news-analysis",
+                "category": "research",
+                "dependencies": ["data_collection", "news_data"],
+                "version": "0.1.0"
+            }
+        }
+        
+        self.user_preferences = UserPreferences()
+        self.storage = get_storage_manager()
+        self.recent_reports: List[RecentReport] = []
+        self.max_recent_reports = 10
+        self.load_recent_reports()
+    
+    def get_all_features(self) -> List[Dict[str, Any]]:
+        """Get all available features with their status."""
+        features_list = []
+        for feature_id, feature_info in self.features.items():
+            status = get_feature_status(feature_id)
+            feature_info.update(status)
+            features_list.append(feature_info)
+        return features_list
+    
+    def get_feature(self, feature_id: str) -> Dict[str, Any]:
+        """Get information about a specific feature."""
+        if feature_id not in self.features:
+            raise ValueError(f"Feature {feature_id} not found")
+            
+        feature_info = self.features[feature_id].copy()
+        status = get_feature_status(feature_id)
+        feature_info.update(status)
+        return feature_info
+    
+    def get_implemented_features(self) -> List[Dict[str, Any]]:
+        """Get only the implemented features."""
+        return [f for f in self.get_all_features() if f["implemented"]]
+    
+    def get_coming_soon_features(self) -> List[Dict[str, Any]]:
+        """Get features that are coming soon."""
+        return [f for f in self.get_all_features() if f["coming_soon"]]
+    
+    def get_features_by_category(self, category: str) -> List[Dict[str, Any]]:
+        """Get features filtered by category."""
+        return [f for f in self.get_all_features() if f["category"] == category]
+    
+    def add_recent_report(self, report_type: str, symbol: Optional[str] = None, content: Optional[str] = None) -> None:
+        """Add a report to the recent reports list."""
+        report = RecentReport(report_type, symbol, datetime.now(), content)
+        self.recent_reports.insert(0, report)
+        if len(self.recent_reports) > self.max_recent_reports:
+            self.recent_reports.pop()
+        self.save_recent_reports()
+    
+    def get_recent_reports(self, limit: Optional[int] = None) -> List[Dict[str, Any]]:
+        """Get recent reports."""
+        reports = self.recent_reports[:limit] if limit else self.recent_reports
+        return [{
+            **r.to_dict(),
+            "feature_info": self.get_feature(r.report_type)
+        } for r in reports]
+    
+    def save_recent_reports(self) -> None:
+        """Save recent reports to storage."""
+        reports_data = [r.to_dict() for r in self.recent_reports]
+        self.storage.save_recent_reports(reports_data)
+    
+    def load_recent_reports(self) -> None:
+        """Load recent reports from storage."""
+        reports_data = self.storage.load_recent_reports()
+        self.recent_reports = [RecentReport.from_dict(r) for r in reports_data]
+    
+    def get_dashboard_summary(self) -> Dict[str, Any]:
+        """Get a summary of the dashboard state."""
+        return {
+            "total_features": len(self.features),
+            "implemented_features": len(self.get_implemented_features()),
+            "coming_soon_features": len(self.get_coming_soon_features()),
+            "recent_reports": len(self.recent_reports),
+            "categories": list(set(f["category"] for f in self.features.values())),
+            "user_preferences": self.user_preferences.settings
+        }
+    
+    def check_feature_dependencies(self, feature_id: str) -> Dict[str, bool]:
+        """Check if all dependencies for a feature are met."""
+        if feature_id not in self.features:
+            raise ValueError(f"Feature {feature_id} not found")
+            
+        feature = self.features[feature_id]
+        dependencies = feature.get("dependencies", [])
+        
+        return {
+            dep: get_feature_status(dep)["implemented"]
+            for dep in dependencies
+        }
+    
+    def backup_data(self, backup_dir: Optional[str] = None) -> None:
+        """Create a backup of all dashboard data."""
+        self.storage.backup_storage(backup_dir)
+    
+    def restore_from_backup(self, backup_file: str) -> None:
+        """Restore dashboard data from a backup file."""
+        self.storage.restore_from_backup(backup_file)
+        self.user_preferences.load_settings()
+        self.load_recent_reports()
+    
+    def generate_technical_analysis(self, symbol: str) -> str:
+        """Generate a technical analysis report for the given symbol."""
+        try:
+            # Get financial data
+            symbol_fin_data = get_finance_data(symbol)
+            
+            # Generate report
+            report_content = self._generate_ta_report(symbol_fin_data, symbol)
+            
+            # Add to recent reports
+            self.add_recent_report("technical_analysis", symbol, report_content)
+            
+            logger.info(f"Done: Final Technical Analysis for {symbol}")
+            return report_content
+            
+        except Exception as err:
+            logger.error(f"Error: Failed to generate Technical Analysis report: {err}")
+            raise
+    
+    def generate_options_analysis(self, symbol: str) -> str:
+        """Generate an options analysis report for the given symbol."""
+        try:
+            # Get options data
+            options_data = get_fin_options_data(symbol)
+            
+            # Generate report
+            report_content = self._generate_options_report(options_data, symbol)
+            
+            # Add to recent reports
+            self.add_recent_report("options_analysis", symbol, report_content)
+            
+            logger.info(f"Done: Options Analysis for {symbol}")
+            return report_content
+            
+        except Exception as err:
+            logger.error(f"Error: Failed to generate Options Analysis report: {err}")
+            raise
+    
+    def _generate_ta_report(self, last_day_summary: str, symbol: str) -> str:
+        """Generate technical analysis report using LLM."""
+        prompt = f"""
+            You are a seasoned Technical Analysis (TA) expert, rivaling legends like Charles Dow, John Bollinger, and Alan Andrews. 
+            Your deep understanding of market dynamics, coupled with mastery of technical indicators, 
+            allows you to decipher complex patterns and offer precise predictions.
+
+            Your expertise extends to practical tools like the pandas_ta module, enabling you to extract valuable insights from raw data.
+
+            **Objective:**
+            Analyze the provided technical indicators for {symbol} on its last trading day and predict its price movement over the next few trading sessions.
+
+            **Instructions:**
+            1. **Identify Potential Trading Signals:** Highlight specific indicators suggesting bullish, bearish, or neutral signals. Explain the rationale behind each signal, referencing historical patterns or comparable market scenarios.
+            2. **Detect Patterns and Divergences:** Analyze the interplay between different indicators. Detect patterns like moving average crossovers, candlestick formations, or divergences between price action and indicators. Explain the significance of each pattern.
+            3. **Price Movement Prediction:** Based on your analysis, provide a clear prediction for {symbol}'s price movement in the next few days. State the expected direction (up, down, sideways) and potential price targets if identifiable.
+            4. **Risk Assessment:** Briefly discuss any potential risks or factors that could invalidate your predictions, promoting a balanced and informed perspective.
+
+            **Technical Indicators for {symbol} on the Last Trading Day:**
+            {last_day_summary}
+
+            Remember, your analysis should be detailed, insightful, and actionable for traders seeking to capitalize on market movements.
+        """
+        
+        try:
+            return llm_text_gen(prompt)
+        except Exception as err:
+            logger.error(f"Failed to generate TA report: {err}")
+            raise
+    
+    def _generate_options_report(self, results_sentences: List[str], ticker: str) -> str:
+        """Generate options analysis report using LLM."""
+        prompt = f"""
+            You are a financial expert specializing in options trading and market sentiment analysis. 
+            You have been provided with the following technical analysis of options data for the ticker symbol {ticker} with the nearest expiry date:
+
+            {chr(10).join(results_sentences)}
+
+            Based on this data, provide a comprehensive analysis of the options market for {ticker}.
+
+            Your analysis should include:
+
+            1. **Implied Volatility Interpretation:** Discuss the significance of the average implied volatility for both call and put options. What does it suggest about market expectations of future price movements?
+            2. **Volume and Open Interest Insights:** Analyze the volume and open interest for call and put options. What does this data reveal about current market positioning and potential future trading activity?
+            3. **Sentiment Analysis:** Evaluate the put-call ratio, implied volatility skew, and overall market sentiment. What do these indicators suggest about trader sentiment and potential future price direction?
+            4. **Potential Trading Strategies:** Based on your analysis, suggest potential options trading strategies that could be employed for {ticker}, considering the current market conditions and sentiment.
+
+            Please provide your analysis in a clear and concise manner, suitable for someone with a good understanding of options trading.
+        """
+        
+        try:
+            return llm_text_gen(prompt)
+        except Exception as err:
+            logger.error(f"Failed to generate options report: {err}")
+            raise
+
+def get_dashboard() -> FinancialDashboard:
+    """Get the financial dashboard instance."""
+    return FinancialDashboard() 

lib/ai_writers/ai_finance_report_generator/reports/README.md

@@ -0,0 +1,265 @@
+# Financial Reports Module
+
+This directory contains the core report generation modules for different types of financial analysis. Each module is designed to handle a specific type of financial report and can be accessed through the main dashboard interface.
+
+## Directory Structure
+
+```
+reports/
+├── technical_analysis/     # Technical analysis reports
+├── fundamental_analysis/   # Fundamental analysis reports
+├── options_analysis/      # Options analysis reports
+├── portfolio_analysis/    # Portfolio analysis reports
+├── market_research/       # Market research reports
+└── news_analysis/         # News analysis reports
+```
+
+## Report Types
+
+### 1. Technical Analysis Reports
+Location: `technical_analysis/`
+
+Generates technical analysis reports including:
+- Moving averages (SMA, EMA, WMA)
+- RSI, MACD, Bollinger Bands
+- Volume analysis
+- Support/Resistance levels
+- Trend analysis
+- Pattern recognition
+
+Usage:
+```python
+from lib.ai_writers.ai_finance_report_generator.reports.technical_analysis import generate_ta_report
+
+report = generate_ta_report("AAPL")
+```
+
+### 2. Fundamental Analysis Reports
+Location: `fundamental_analysis/`
+
+Generates fundamental analysis reports including:
+- Financial ratios
+- Company valuation metrics
+- Growth analysis
+- Profitability analysis
+- Debt analysis
+- Cash flow analysis
+
+Usage:
+```python
+from lib.ai_writers.ai_finance_report_generator.reports.fundamental_analysis import generate_fa_report
+
+report = generate_fa_report("AAPL")
+```
+
+### 3. Options Analysis Reports
+Location: `options_analysis/`
+
+Generates options analysis reports including:
+- Options chain analysis
+- Implied volatility analysis
+- Options strategies
+- Risk metrics
+- Greeks analysis
+
+Usage:
+```python
+from lib.ai_writers.ai_finance_report_generator.reports.options_analysis import generate_options_report
+
+report = generate_options_report("AAPL")
+```
+
+### 4. Portfolio Analysis Reports
+Location: `portfolio_analysis/`
+
+Generates portfolio analysis reports including:
+- Portfolio performance analysis
+- Risk assessment
+- Asset allocation
+- Correlation analysis
+- Diversification metrics
+- Performance attribution
+
+Usage:
+```python
+from lib.ai_writers.ai_finance_report_generator.reports.portfolio_analysis import generate_portfolio_report
+
+portfolio = [
+    {"symbol": "AAPL", "shares": 100},
+    {"symbol": "GOOGL", "shares": 50}
+]
+report = generate_portfolio_report(portfolio)
+```
+
+### 5. Market Research Reports
+Location: `market_research/`
+
+Generates market research reports including:
+- Sector analysis
+- Industry trends
+- Market overview
+- Competitive analysis
+- Market opportunities
+- Risk factors
+
+Usage:
+```python
+from lib.ai_writers.ai_finance_report_generator.reports.market_research import generate_market_research_report
+
+report = generate_market_research_report(sectors=["Technology", "Healthcare"])
+```
+
+### 6. News Analysis Reports
+Location: `news_analysis/`
+
+Generates news analysis reports including:
+- News sentiment analysis
+- Market impact analysis
+- Event correlation
+- Trend detection
+- Social media analysis
+- News aggregation
+
+Usage:
+```python
+from lib.ai_writers.ai_finance_report_generator.reports.news_analysis import generate_news_analysis_report
+
+report = generate_news_analysis_report("AAPL")
+```
+
+## Common Features
+
+All report modules share the following features:
+
+1. **Data Validation**
+   - Input validation for symbols and parameters
+   - Error handling for invalid inputs
+   - Data type checking
+
+2. **Report Formatting**
+   - Markdown formatting
+   - Chart generation (when applicable)
+   - Customizable templates
+
+3. **Storage Integration**
+   - Automatic report storage
+   - Recent reports tracking
+   - Report versioning
+
+4. **User Preferences**
+   - Customizable report formats
+   - Language selection
+   - Chart style preferences
+
+## Integration with Dashboard
+
+All report modules are integrated with the main dashboard and can be accessed through the `FinancialDashboard` class:
+
+```python
+from lib.ai_writers.ai_finance_report_generator.ai_financial_dashboard import get_dashboard
+
+dashboard = get_dashboard()
+
+# Generate reports through dashboard
+ta_report = dashboard.generate_technical_analysis("AAPL")
+options_report = dashboard.generate_options_analysis("AAPL")
+
+# Get recent reports
+recent_reports = dashboard.get_recent_reports()
+```
+
+## Adding New Report Types
+
+To add a new report type:
+
+1. Create a new directory in the `reports/` folder
+2. Create an `__init__.py` file with the report generation function
+3. Add the report type to the dashboard features
+4. Implement the report generation logic
+5. Add appropriate error handling and validation
+
+Example:
+```python
+# reports/new_analysis/__init__.py
+from typing import Dict, Any
+from ...utils import validate_symbol
+
+def generate_new_analysis_report(symbol: str) -> Dict[str, Any]:
+    """
+    Generate a new type of analysis report.
+    
+    Args:
+        symbol (str): Stock symbol to analyze
+        
+    Returns:
+        Dict[str, Any]: Analysis report
+    """
+    if not validate_symbol(symbol):
+        raise ValueError("Invalid symbol provided")
+        
+    # Implement report generation logic
+    return {
+        "symbol": symbol,
+        "analysis": "Report content"
+    }
+```
+
+## Error Handling
+
+All report modules implement consistent error handling:
+
+1. **Input Validation**
+   - Symbol validation
+   - Parameter validation
+   - Data type checking
+
+2. **Data Collection Errors**
+   - API errors
+   - Network errors
+   - Data format errors
+
+3. **Report Generation Errors**
+   - LLM errors
+   - Template errors
+   - Formatting errors
+
+4. **Storage Errors**
+   - File system errors
+   - Database errors
+   - Backup errors
+
+## Contributing
+
+When contributing to the reports module:
+
+1. Follow the existing code structure
+2. Add appropriate type hints
+3. Include comprehensive docstrings
+4. Add error handling
+5. Update the dashboard integration
+6. Add tests for new functionality
+
+## Dependencies
+
+The reports module depends on:
+
+1. **Data Collection**
+   - `finance_data_researcher`
+   - `web_scraping_tools`
+
+2. **Analysis Tools**
+   - `pandas_ta`
+   - `numpy`
+   - `scipy`
+
+3. **Visualization**
+   - `matplotlib`
+   - `plotly`
+
+4. **Text Generation**
+   - `llm_text_gen`
+   - `gpt_providers`
+
+## License
+
+This module is part of the AI Finance Report Generator project and is licensed under the MIT License. 

lib/ai_writers/ai_finance_report_generator/reports/fundamental_analysis/__init__.py

@@ -0,0 +1,34 @@
+"""
+Fundamental Analysis Reports Module
+
+This module handles the generation of fundamental analysis reports including:
+- Financial ratios
+- Company valuation metrics
+- Growth analysis
+- Profitability analysis
+- Debt analysis
+- Cash flow analysis
+"""
+
+from typing import Dict, Any
+from ...utils import validate_symbol
+
+def generate_fa_report(symbol: str) -> Dict[str, Any]:
+    """
+    Generate a fundamental analysis report for the given symbol.
+    
+    Args:
+        symbol (str): Stock symbol to analyze
+        
+    Returns:
+        Dict[str, Any]: Fundamental analysis report
+    """
+    if not validate_symbol(symbol):
+        raise ValueError("Invalid symbol provided")
+        
+    # TODO: Implement fundamental analysis report generation
+    return {
+        "symbol": symbol,
+        "status": "coming_soon",
+        "message": "Fundamental analysis report generation is coming soon"
+    } 

lib/ai_writers/ai_finance_report_generator/reports/market_research/__init__.py

@@ -0,0 +1,29 @@
+"""
+Market Research Reports Module
+
+This module handles the generation of market research reports including:
+- Sector analysis
+- Industry trends
+- Market overview
+- Competitive analysis
+- Market opportunities
+- Risk factors
+"""
+
+from typing import Dict, Any, List
+
+def generate_market_research_report(sectors: List[str] = None) -> Dict[str, Any]:
+    """
+    Generate a market research report.
+    
+    Args:
+        sectors (List[str], optional): List of sectors to analyze
+        
+    Returns:
+        Dict[str, Any]: Market research report
+    """
+    # TODO: Implement market research report generation
+    return {
+        "status": "coming_soon",
+        "message": "Market research report generation is coming soon"
+    } 

lib/ai_writers/ai_finance_report_generator/reports/news_analysis/__init__.py

@@ -0,0 +1,33 @@
+"""
+News Analysis Reports Module
+
+This module handles the generation of news analysis reports including:
+- News sentiment analysis
+- Market impact analysis
+- Event correlation
+- Trend detection
+- Social media analysis
+- News aggregation
+"""
+
+from typing import Dict, Any, List
+from ...utils import validate_symbol
+
+def generate_news_analysis_report(symbol: str = None) -> Dict[str, Any]:
+    """
+    Generate a news analysis report.
+    
+    Args:
+        symbol (str, optional): Stock symbol to analyze news for
+        
+    Returns:
+        Dict[str, Any]: News analysis report
+    """
+    if symbol and not validate_symbol(symbol):
+        raise ValueError("Invalid symbol provided")
+        
+    # TODO: Implement news analysis report generation
+    return {
+        "status": "coming_soon",
+        "message": "News analysis report generation is coming soon"
+    } 

lib/ai_writers/ai_finance_report_generator/reports/options_analysis/__init__.py

@@ -0,0 +1,33 @@
+"""
+Options Analysis Reports Module
+
+This module handles the generation of options analysis reports including:
+- Options chain analysis
+- Implied volatility analysis
+- Options strategies
+- Risk metrics
+- Greeks analysis
+"""
+
+from typing import Dict, Any
+from ...utils import validate_symbol
+
+def generate_options_report(symbol: str) -> Dict[str, Any]:
+    """
+    Generate an options analysis report for the given symbol.
+    
+    Args:
+        symbol (str): Stock symbol to analyze
+        
+    Returns:
+        Dict[str, Any]: Options analysis report
+    """
+    if not validate_symbol(symbol):
+        raise ValueError("Invalid symbol provided")
+        
+    # TODO: Implement options analysis report generation
+    return {
+        "symbol": symbol,
+        "status": "coming_soon",
+        "message": "Options analysis report generation is coming soon"
+    } 

lib/ai_writers/ai_finance_report_generator/reports/portfolio_analysis/__init__.py

@@ -0,0 +1,32 @@
+"""
+Portfolio Analysis Reports Module
+
+This module handles the generation of portfolio analysis reports including:
+- Portfolio performance analysis
+- Risk assessment
+- Asset allocation
+- Correlation analysis
+- Diversification metrics
+- Performance attribution
+"""
+
+from typing import Dict, Any, List
+
+def generate_portfolio_report(portfolio: List[Dict[str, Any]]) -> Dict[str, Any]:
+    """
+    Generate a portfolio analysis report.
+    
+    Args:
+        portfolio (List[Dict[str, Any]]): List of portfolio positions
+        
+    Returns:
+        Dict[str, Any]: Portfolio analysis report
+    """
+    if not portfolio:
+        raise ValueError("Portfolio cannot be empty")
+        
+    # TODO: Implement portfolio analysis report generation
+    return {
+        "status": "coming_soon",
+        "message": "Portfolio analysis report generation is coming soon"
+    } 

lib/ai_writers/ai_finance_report_generator/reports/technical_analysis/__init__.py

@@ -0,0 +1,314 @@
+"""
+Technical Analysis Reports Module
+
+This module handles the generation of technical analysis reports using yfinance data and pandas_ta for indicators.
+"""
+
+from typing import Dict, Any, List, Optional
+import yfinance as yf
+import pandas as pd
+import pandas_ta as ta
+import plotly.graph_objects as go
+from datetime import datetime, timedelta
+from loguru import logger
+from ...utils import validate_symbol
+from ...ai_financial_dashboard import get_dashboard
+
+class TechnicalAnalysis:
+    def __init__(self, symbol: str, timeframe: str = "1d", period: str = "1y"):
+        """
+        Initialize Technical Analysis.
+        
+        Args:
+            symbol (str): Stock symbol to analyze
+            timeframe (str): Data timeframe (1m, 5m, 15m, 30m, 1h, 1d, 1wk, 1mo)
+            period (str): Data period (1d, 5d, 1mo, 3mo, 6mo, 1y, 2y, 5y, 10y, ytd, max)
+        """
+        logger.info(f"Initializing Technical Analysis for {symbol} with timeframe {timeframe} and period {period}")
+        self.symbol = symbol
+        self.timeframe = timeframe
+        self.period = period
+        self.data = None
+        self.indicators = {}
+        self.stock = yf.Ticker(symbol)
+        
+    def fetch_data(self) -> None:
+        """Fetch historical price data using yfinance"""
+        try:
+            logger.info(f"Fetching historical data for {self.symbol}")
+            # Get historical data
+            self.data = self.stock.history(period=self.period, interval=self.timeframe)
+            logger.debug(f"Retrieved {len(self.data)} data points")
+            
+            # Get additional info
+            logger.info("Fetching company information")
+            self.info = self.stock.info
+            
+            # Calculate basic metrics
+            logger.debug("Calculating basic metrics")
+            self.data['Returns'] = self.data['Close'].pct_change()
+            self.data['Volatility'] = self.data['Returns'].rolling(window=20).std()
+            
+            logger.success(f"Successfully fetched data for {self.symbol}")
+            
+        except Exception as e:
+            logger.error(f"Error fetching data for {self.symbol}: {str(e)}")
+            raise ValueError(f"Error fetching data for {self.symbol}: {str(e)}")
+        
+    def calculate_indicators(self) -> None:
+        """Calculate technical indicators using pandas_ta"""
+        if self.data is None:
+            logger.error("Data not fetched. Call fetch_data() first.")
+            raise ValueError("Data not fetched. Call fetch_data() first.")
+            
+        logger.info("Calculating technical indicators")
+        
+        # Moving Averages
+        logger.debug("Calculating Moving Averages")
+        self.indicators['sma_20'] = self.data.ta.sma(length=20)
+        self.indicators['sma_50'] = self.data.ta.sma(length=50)
+        self.indicators['sma_200'] = self.data.ta.sma(length=200)
+        self.indicators['ema_20'] = self.data.ta.ema(length=20)
+        
+        # RSI
+        logger.debug("Calculating RSI")
+        self.indicators['rsi'] = self.data.ta.rsi()
+        
+        # MACD
+        logger.debug("Calculating MACD")
+        macd = self.data.ta.macd()
+        self.indicators['macd'] = macd['MACD_12_26_9']
+        self.indicators['macd_signal'] = macd['MACDs_12_26_9']
+        self.indicators['macd_hist'] = macd['MACDh_12_26_9']
+        
+        # Bollinger Bands
+        logger.debug("Calculating Bollinger Bands")
+        bbands = self.data.ta.bbands()
+        self.indicators['bb_upper'] = bbands['BBU_20_2.0']
+        self.indicators['bb_middle'] = bbands['BBM_20_2.0']
+        self.indicators['bb_lower'] = bbands['BBL_20_2.0']
+        
+        # Volume Analysis
+        logger.debug("Calculating Volume indicators")
+        self.indicators['volume_sma'] = self.data['Volume'].rolling(window=20).mean()
+        self.indicators['obv'] = self.data.ta.obv()
+        
+        # Additional Indicators
+        logger.debug("Calculating additional indicators")
+        self.indicators['stoch'] = self.data.ta.stoch()
+        self.indicators['adx'] = self.data.ta.adx()
+        self.indicators['atr'] = self.data.ta.atr()
+        
+        logger.success("Successfully calculated all technical indicators")
+        
+    def identify_patterns(self) -> List[Dict[str, Any]]:
+        """Identify chart patterns"""
+        logger.info("Identifying chart patterns")
+        patterns = []
+        
+        # Candlestick Patterns
+        if len(self.data) >= 3:
+            logger.debug("Analyzing candlestick patterns")
+            # Doji
+            doji = self.data.ta.cdl_doji()
+            if doji['CDL_DOJI'].iloc[-1] != 0:
+                logger.debug("Doji pattern detected")
+                patterns.append({
+                    'type': 'doji',
+                    'date': self.data.index[-1],
+                    'significance': 'neutral'
+                })
+            
+            # Engulfing
+            engulfing = self.data.ta.cdl_engulfing()
+            if engulfing['CDL_ENGULFING'].iloc[-1] != 0:
+                logger.debug("Engulfing pattern detected")
+                patterns.append({
+                    'type': 'engulfing',
+                    'date': self.data.index[-1],
+                    'significance': 'bullish' if engulfing['CDL_ENGULFING'].iloc[-1] > 0 else 'bearish'
+                })
+        
+        logger.info(f"Identified {len(patterns)} patterns")
+        return patterns
+        
+    def find_support_resistance(self) -> Dict[str, List[float]]:
+        """Find support and resistance levels using price action"""
+        logger.info("Finding support and resistance levels")
+        levels = {
+            'support': [],
+            'resistance': []
+        }
+        
+        # Use recent price action to identify levels
+        recent_data = self.data.tail(100)
+        logger.debug(f"Analyzing {len(recent_data)} recent data points for S/R levels")
+        
+        # Find local minima and maxima
+        for i in range(2, len(recent_data) - 2):
+            # Support level
+            if (recent_data['Low'].iloc[i] < recent_data['Low'].iloc[i-1] and 
+                recent_data['Low'].iloc[i] < recent_data['Low'].iloc[i-2] and
+                recent_data['Low'].iloc[i] < recent_data['Low'].iloc[i+1] and
+                recent_data['Low'].iloc[i] < recent_data['Low'].iloc[i+2]):
+                levels['support'].append(recent_data['Low'].iloc[i])
+            
+            # Resistance level
+            if (recent_data['High'].iloc[i] > recent_data['High'].iloc[i-1] and 
+                recent_data['High'].iloc[i] > recent_data['High'].iloc[i-2] and
+                recent_data['High'].iloc[i] > recent_data['High'].iloc[i+1] and
+                recent_data['High'].iloc[i] > recent_data['High'].iloc[i+2]):
+                levels['resistance'].append(recent_data['High'].iloc[i])
+        
+        # Remove duplicates and sort
+        levels['support'] = sorted(list(set(levels['support'])))
+        levels['resistance'] = sorted(list(set(levels['resistance'])))
+        
+        logger.info(f"Found {len(levels['support'])} support and {len(levels['resistance'])} resistance levels")
+        return levels
+        
+    def generate_chart(self) -> go.Figure:
+        """Generate interactive chart using plotly"""
+        logger.info("Generating interactive chart")
+        fig = go.Figure()
+        
+        # Candlestick chart
+        logger.debug("Adding candlestick chart")
+        fig.add_trace(go.Candlestick(
+            x=self.data.index,
+            open=self.data['Open'],
+            high=self.data['High'],
+            low=self.data['Low'],
+            close=self.data['Close'],
+            name='Price'
+        ))
+        
+        # Moving Averages
+        logger.debug("Adding moving averages")
+        fig.add_trace(go.Scatter(
+            x=self.data.index,
+            y=self.indicators['sma_20'],
+            name='SMA 20',
+            line=dict(color='blue')
+        ))
+        
+        fig.add_trace(go.Scatter(
+            x=self.data.index,
+            y=self.indicators['sma_50'],
+            name='SMA 50',
+            line=dict(color='orange')
+        ))
+        
+        # Bollinger Bands
+        logger.debug("Adding Bollinger Bands")
+        fig.add_trace(go.Scatter(
+            x=self.data.index,
+            y=self.indicators['bb_upper'],
+            name='BB Upper',
+            line=dict(color='gray', dash='dash')
+        ))
+        
+        fig.add_trace(go.Scatter(
+            x=self.data.index,
+            y=self.indicators['bb_lower'],
+            name='BB Lower',
+            line=dict(color='gray', dash='dash'),
+            fill='tonexty'
+        ))
+        
+        # Volume
+        logger.debug("Adding volume bars")
+        fig.add_trace(go.Bar(
+            x=self.data.index,
+            y=self.data['Volume'],
+            name='Volume',
+            marker_color='rgba(0,0,255,0.3)'
+        ))
+        
+        # Layout
+        logger.debug("Setting chart layout")
+        fig.update_layout(
+            title=f'{self.symbol} Technical Analysis',
+            yaxis_title='Price',
+            xaxis_title='Date',
+            template='plotly_dark'
+        )
+        
+        logger.success("Successfully generated chart")
+        return fig
+        
+    def _generate_summary(self) -> Dict[str, Any]:
+        """Generate summary of technical analysis"""
+        logger.info("Generating analysis summary")
+        current_price = self.data['Close'].iloc[-1]
+        sma_20 = self.indicators['sma_20'].iloc[-1]
+        sma_50 = self.indicators['sma_50'].iloc[-1]
+        rsi = self.indicators['rsi'].iloc[-1]
+        
+        summary = {
+            'current_price': current_price,
+            'price_change': self.data['Returns'].iloc[-1] * 100,
+            'trend': 'bullish' if current_price > sma_20 > sma_50 else 'bearish',
+            'rsi_signal': 'overbought' if rsi > 70 else 'oversold' if rsi < 30 else 'neutral',
+            'volatility': self.data['Volatility'].iloc[-1],
+            'volume_trend': 'increasing' if self.data['Volume'].iloc[-1] > self.indicators['volume_sma'].iloc[-1] else 'decreasing'
+        }
+        
+        logger.debug(f"Analysis summary: {summary}")
+        return summary
+        
+    def generate_report(self) -> Dict[str, Any]:
+        """Generate comprehensive technical analysis report"""
+        logger.info(f"Generating comprehensive report for {self.symbol}")
+        
+        self.fetch_data()
+        self.calculate_indicators()
+        patterns = self.identify_patterns()
+        levels = self.find_support_resistance()
+        chart = self.generate_chart()
+        summary = self._generate_summary()
+        
+        report = {
+            'symbol': self.symbol,
+            'timestamp': datetime.now(),
+            'company_info': self.info,
+            'indicators': self.indicators,
+            'patterns': patterns,
+            'levels': levels,
+            'chart': chart,
+            'summary': summary
+        }
+        
+        logger.success(f"Successfully generated report for {self.symbol}")
+        return report
+
+def generate_ta_report(symbol: str) -> Dict[str, Any]:
+    """
+    Generate a technical analysis report for the given symbol.
+    
+    Args:
+        symbol (str): Stock symbol to analyze
+        
+    Returns:
+        Dict[str, Any]: Technical analysis report
+    """
+    logger.info(f"Generating technical analysis report for {symbol}")
+    
+    if not validate_symbol(symbol):
+        logger.error(f"Invalid symbol provided: {symbol}")
+        raise ValueError("Invalid symbol provided")
+        
+    try:
+        analysis = TechnicalAnalysis(symbol)
+        report = analysis.generate_report()
+        
+        # Add to dashboard's recent reports
+        dashboard = get_dashboard()
+        dashboard.add_recent_report("technical_analysis", symbol, report)
+        
+        logger.success(f"Successfully completed technical analysis for {symbol}")
+        return report
+        
+    except Exception as e:
+        logger.error(f"Error generating technical analysis report for {symbol}: {str(e)}")
+        raise 

lib/ai_writers/ai_finance_report_generator/utils/__init__.py

@@ -0,0 +1,62 @@
+"""
+Utility functions and helpers for the AI Finance Report Generator.
+"""
+
+from typing import Dict, List, Any
+import logging
+
+# Configure logging
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
+)
+
+logger = logging.getLogger(__name__)
+
+def validate_symbol(symbol: str) -> bool:
+    """
+    Validate if the given symbol is in correct format.
+    
+    Args:
+        symbol (str): Stock symbol to validate
+        
+    Returns:
+        bool: True if valid, False otherwise
+    """
+    if not isinstance(symbol, str):
+        return False
+    return len(symbol.strip()) > 0
+
+def format_currency(value: float) -> str:
+    """
+    Format number as currency.
+    
+    Args:
+        value (float): Number to format
+        
+    Returns:
+        str: Formatted currency string
+    """
+    return f"${value:,.2f}"
+
+def get_feature_status(feature_name: str) -> Dict[str, Any]:
+    """
+    Get the status of a feature.
+    
+    Args:
+        feature_name (str): Name of the feature
+        
+    Returns:
+        Dict[str, Any]: Feature status information
+    """
+    # This will be expanded as we implement more features
+    implemented_features = {
+        "technical_analysis": True,
+        "options_analysis": True,
+    }
+    
+    return {
+        "name": feature_name,
+        "implemented": implemented_features.get(feature_name, False),
+        "coming_soon": not implemented_features.get(feature_name, False)
+    } 

lib/ai_writers/ai_finance_report_generator/utils/storage.py

@@ -0,0 +1,208 @@
+"""
+Storage Module for AI Finance Report Generator
+
+This module handles the persistence of user preferences and recent reports using JSON files.
+"""
+
+import json
+import os
+from typing import Dict, List, Any, Optional
+from datetime import datetime
+from pathlib import Path
+
+class StorageManager:
+    """Manages storage operations for user preferences and recent reports."""
+    
+    def __init__(self, base_dir: Optional[str] = None):
+        """
+        Initialize the storage manager.
+        
+        Args:
+            base_dir (Optional[str]): Base directory for storage files
+        """
+        if base_dir is None:
+            # Use user's home directory by default
+            self.base_dir = Path.home() / ".ai_finance"
+        else:
+            self.base_dir = Path(base_dir)
+            
+        # Create storage directory if it doesn't exist
+        self.base_dir.mkdir(parents=True, exist_ok=True)
+        
+        # Define file paths
+        self.prefs_file = self.base_dir / "preferences.json"
+        self.reports_file = self.base_dir / "recent_reports.json"
+        
+        # Initialize files if they don't exist
+        self._initialize_storage()
+    
+    def _initialize_storage(self) -> None:
+        """Initialize storage files if they don't exist."""
+        if not self.prefs_file.exists():
+            self._save_preferences({})
+            
+        if not self.reports_file.exists():
+            self._save_reports([])
+    
+    def _save_preferences(self, preferences: Dict[str, Any]) -> None:
+        """
+        Save user preferences to file.
+        
+        Args:
+            preferences (Dict[str, Any]): User preferences to save
+        """
+        with open(self.prefs_file, 'w') as f:
+            json.dump(preferences, f, indent=4)
+    
+    def _load_preferences(self) -> Dict[str, Any]:
+        """
+        Load user preferences from file.
+        
+        Returns:
+            Dict[str, Any]: User preferences
+        """
+        try:
+            with open(self.prefs_file, 'r') as f:
+                return json.load(f)
+        except (json.JSONDecodeError, FileNotFoundError):
+            return {}
+    
+    def _save_reports(self, reports: List[Dict[str, Any]]) -> None:
+        """
+        Save recent reports to file.
+        
+        Args:
+            reports (List[Dict[str, Any]]): Recent reports to save
+        """
+        with open(self.reports_file, 'w') as f:
+            json.dump(reports, f, indent=4)
+    
+    def _load_reports(self) -> List[Dict[str, Any]]:
+        """
+        Load recent reports from file.
+        
+        Returns:
+            List[Dict[str, Any]]: Recent reports
+        """
+        try:
+            with open(self.reports_file, 'r') as f:
+                return json.load(f)
+        except (json.JSONDecodeError, FileNotFoundError):
+            return []
+    
+    def save_user_preferences(self, preferences: Dict[str, Any]) -> None:
+        """
+        Save user preferences.
+        
+        Args:
+            preferences (Dict[str, Any]): User preferences to save
+        """
+        self._save_preferences(preferences)
+    
+    def load_user_preferences(self) -> Dict[str, Any]:
+        """
+        Load user preferences.
+        
+        Returns:
+            Dict[str, Any]: User preferences
+        """
+        return self._load_preferences()
+    
+    def save_recent_reports(self, reports: List[Dict[str, Any]]) -> None:
+        """
+        Save recent reports.
+        
+        Args:
+            reports (List[Dict[str, Any]]): Recent reports to save
+        """
+        # Convert datetime objects to ISO format strings
+        serialized_reports = []
+        for report in reports:
+            serialized_report = report.copy()
+            if isinstance(report.get('timestamp'), datetime):
+                serialized_report['timestamp'] = report['timestamp'].isoformat()
+            serialized_reports.append(serialized_report)
+        
+        self._save_reports(serialized_reports)
+    
+    def load_recent_reports(self) -> List[Dict[str, Any]]:
+        """
+        Load recent reports.
+        
+        Returns:
+            List[Dict[str, Any]]: Recent reports with datetime objects
+        """
+        reports = self._load_reports()
+        
+        # Convert ISO format strings back to datetime objects
+        for report in reports:
+            if isinstance(report.get('timestamp'), str):
+                report['timestamp'] = datetime.fromisoformat(report['timestamp'])
+        
+        return reports
+    
+    def clear_storage(self) -> None:
+        """Clear all stored data."""
+        self._save_preferences({})
+        self._save_reports([])
+    
+    def backup_storage(self, backup_dir: Optional[str] = None) -> None:
+        """
+        Create a backup of the storage files.
+        
+        Args:
+            backup_dir (Optional[str]): Directory to store backup files
+        """
+        if backup_dir is None:
+            backup_dir = self.base_dir / "backups"
+        else:
+            backup_dir = Path(backup_dir)
+            
+        backup_dir.mkdir(parents=True, exist_ok=True)
+        
+        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+        
+        # Backup preferences
+        if self.prefs_file.exists():
+            backup_prefs = backup_dir / f"preferences_{timestamp}.json"
+            with open(self.prefs_file, 'r') as src, open(backup_prefs, 'w') as dst:
+                dst.write(src.read())
+        
+        # Backup reports
+        if self.reports_file.exists():
+            backup_reports = backup_dir / f"recent_reports_{timestamp}.json"
+            with open(self.reports_file, 'r') as src, open(backup_reports, 'w') as dst:
+                dst.write(src.read())
+    
+    def restore_from_backup(self, backup_file: str) -> None:
+        """
+        Restore storage from a backup file.
+        
+        Args:
+            backup_file (str): Path to the backup file
+        """
+        backup_path = Path(backup_file)
+        if not backup_path.exists():
+            raise FileNotFoundError(f"Backup file not found: {backup_file}")
+        
+        # Determine which type of backup file it is
+        if "preferences" in backup_path.name:
+            with open(backup_path, 'r') as src, open(self.prefs_file, 'w') as dst:
+                dst.write(src.read())
+        elif "recent_reports" in backup_path.name:
+            with open(backup_path, 'r') as src, open(self.reports_file, 'w') as dst:
+                dst.write(src.read())
+        else:
+            raise ValueError(f"Unknown backup file type: {backup_file}")
+
+def get_storage_manager(base_dir: Optional[str] = None) -> StorageManager:
+    """
+    Get a storage manager instance.
+    
+    Args:
+        base_dir (Optional[str]): Base directory for storage files
+        
+    Returns:
+        StorageManager: Storage manager instance
+    """
+    return StorageManager(base_dir) 

lib/ai_writers/ai_financial_writer.py

@@ -1,91 +0,0 @@
-import sys
-import os
-from textwrap import dedent
-from pathlib import Path
-from datetime import datetime
-
-from loguru import logger
-logger.remove()
-logger.add(sys.stdout,
-        colorize=True,
-        format="<level>{level}</level>|<green>{file}:{line}:{function}</green>| {message}"
-    )
-
-from ..ai_web_researcher.finance_data_researcher import get_finance_data, get_fin_options_data
-from ..gpt_providers.text_generation.main_text_generation import llm_text_gen
-
-
-def write_basic_ta_report(symbol):
-    """ Write financial TA for given ticker symbol """
-    try:
-        symbol_fin_data = get_finance_data(symbol)
-        #get_visual_reports
-        fin_report = gen_finta_report(symbol_fin_data, symbol)
-        logger.info(f"Done: Final Technical Analysis for {symbol}:\n\n")
-    except Exception as err:
-        logger.error(f"Error: Failed to generate Financial report: {err}")
-
-    #fin_options_data = get_fin_options_data(symbol)
-    #options_report = gen_options_report(fin_options_data, symbol)
-
-
-
-def gen_options_report(results_sentences, ticker):
-    """ Call LLM to generate options report """
-    prompt = f"""
-        You are a financial expert specializing in options trading and market sentiment analysis. 
-        You have been provided with the following technical analysis of options data for the ticker symbol {ticker} with the nearest expiry date:
-
-        {chr(10).join(results_sentences)}
-
-        Based on this data, provide a comprehensive analysis of the options market for {ticker}.
-
-        Your analysis should include:
-
-        1. **Implied Volatility Interpretation:** Discuss the significance of the average implied volatility for both call and put options. What does it suggest about market expectations of future price movements?
-        2. **Volume and Open Interest Insights:** Analyze the volume and open interest for call and put options. What does this data reveal about current market positioning and potential future trading activity?
-        3. **Sentiment Analysis:** Evaluate the put-call ratio, implied volatility skew, and overall market sentiment. What do these indicators suggest about trader sentiment and potential future price direction?
-        4. **Potential Trading Strategies:** Based on your analysis, suggest potential options trading strategies that could be employed for {ticker}, considering the current market conditions and sentiment.
-
-        Please provide your analysis in a clear and concise manner, suitable for someone with a good understanding of options trading.
-    """
-    logger.info("Generating Financial Technical report..")
-    try:
-        response = llm_text_gen(prompt)
-        return response
-    except Exception as err:
-        logger.error(f"Exit: Failed to get response from LLM: {err}")
-        exit(1)
-
-
-def gen_finta_report(last_day_summary, symbol):
-    """ Get AI to write TA report from given data """
-    prompt = f"""
-        You are a seasoned Technical Analysis (TA) expert, rivaling legends like Charles Dow, John Bollinger, and Alan Andrews. 
-        Your deep understanding of market dynamics, coupled with mastery of technical indicators, 
-        allows you to decipher complex patterns and offer precise predictions.
-
-        Your expertise extends to practical tools like the pandas_ta module, enabling you to extract valuable insights from raw data.
-
-        **Objective:**
-        Analyze the provided technical indicators for {symbol} on its last trading day and predict its price movement over the next few trading sessions.
-
-        **Instructions:**
-        1. **Identify Potential Trading Signals:** Highlight specific indicators suggesting bullish, bearish, or neutral signals. Explain the rationale behind each signal, referencing historical patterns or comparable market scenarios.
-        2. **Detect Patterns and Divergences:** Analyze the interplay between different indicators. Detect patterns like moving average crossovers, candlestick formations, or divergences between price action and indicators. Explain the significance of each pattern.
-        3. **Price Movement Prediction:** Based on your analysis, provide a clear prediction for {symbol}'s price movement in the next few days. State the expected direction (up, down, sideways) and potential price targets if identifiable.
-        4. **Risk Assessment:** Briefly discuss any potential risks or factors that could invalidate your predictions, promoting a balanced and informed perspective.
-
-        **Technical Indicators for {symbol} on the Last Trading Day:**
-        {last_day_summary}
-
-        Remember, your analysis should be detailed, insightful, and actionable for traders seeking to capitalize on market movements.
-    """
-
-    logger.info("Generating Financial Technical report..")
-    try:
-        response = llm_text_gen(prompt)
-        return response
-    except Exception as err:
-        logger.error(f"Exit: Failed to get response from LLM: {err}")
-        exit(1)

lib/ai_writers/twitter_writers/tweet_generator/smart_tweet_generator.py

@@ -1,13 +1,33 @@
+"""
+Smart Tweet Generator with modern UI components.
+"""
+
 import streamlit as st
 import re
 import json
-import time
 from typing import Dict, List, Tuple, Optional
 import random
 import emoji
 from datetime import datetime
 
 from ....gpt_providers.text_generation.main_text_generation import llm_text_gen
+from ..twitter_streamlit_ui import (
+    TweetForm,
+    TweetCard,
+    Theme,
+    save_to_session,
+    get_from_session,
+    show_success_message,
+    show_error_message,
+    show_info_message,
+    validate_tweet_content,
+    validate_hashtags,
+    validate_emojis,
+    calculate_engagement_score,
+    generate_tweet_metrics,
+    copy_to_clipboard,
+    create_download_button
+)
 
 # Constants
 MAX_TWEET_LENGTH = 280
@@ -19,14 +39,6 @@ EMOJI_CATEGORIES = {
     "Casual": ["👋", "👍", "🙋", "💁", "🤗", "👌", "✌️", "🤝", "👊", "🙏"]
 }
 
-def count_characters(text: str) -> int:
-    """Count characters in tweet, accounting for emojis."""
-    return len(text)
-
-def extract_hashtags(text: str) -> List[str]:
-    """Extract hashtags from tweet text."""
-    return re.findall(r'#\w+', text)
-
 def suggest_hashtags(topic: str, tone: str) -> List[str]:
     """Suggest relevant hashtags based on topic and tone."""
     # Enhanced hashtag suggestions based on topic and tone
@@ -61,63 +73,6 @@ def suggest_emojis(tone: str, count: int = 3) -> List[str]:
     }
     return emoji_map.get(tone.lower(), ["✨"])[:count]
 
-def predict_tweet_performance(tweet: str, target_audience: str, tone: str) -> Dict:
-    """Predict tweet performance with enhanced metrics."""
-    char_count = count_characters(tweet)
-    hashtags = extract_hashtags(tweet)
-    
-    # Enhanced performance metrics
-    metrics = {
-        "character_count": {
-            "score": min(100, (char_count / 280) * 100),
-            "status": "optimal" if 100 <= char_count <= 200 else "suboptimal",
-            "suggestion": "Consider adjusting length for optimal engagement" if char_count < 100 or char_count > 200 else "Length is optimal"
-        },
-        "hashtag_usage": {
-            "score": min(100, (len(hashtags) / 3) * 100),
-            "status": "optimal" if 1 <= len(hashtags) <= 3 else "suboptimal",
-            "suggestion": "Add more hashtags" if len(hashtags) < 1 else "Reduce hashtag count" if len(hashtags) > 3 else "Hashtag count is optimal"
-        },
-        "engagement_potential": {
-            "score": 0,
-            "status": "needs_improvement",
-            "suggestion": ""
-        },
-        "audience_alignment": {
-            "score": 0,
-            "status": "needs_improvement",
-            "suggestion": ""
-        }
-    }
-    
-    # Calculate engagement potential
-    engagement_triggers = ["?", "!", "RT", "like", "follow", "check", "learn", "discover"]
-    trigger_count = sum(1 for trigger in engagement_triggers if trigger.lower() in tweet.lower())
-    metrics["engagement_potential"]["score"] = min(100, (trigger_count / 3) * 100)
-    metrics["engagement_potential"]["status"] = "optimal" if trigger_count >= 1 else "needs_improvement"
-    metrics["engagement_potential"]["suggestion"] = "Add engagement triggers" if trigger_count < 1 else "Good engagement potential"
-    
-    # Calculate audience alignment
-    audience_keywords = {
-        "professionals": ["business", "industry", "professional", "career"],
-        "students": ["learn", "study", "education", "student"],
-        "general": ["everyone", "people", "community", "world"]
-    }
-    keyword_count = sum(1 for keyword in audience_keywords.get(target_audience.lower(), []) 
-                       if keyword.lower() in tweet.lower())
-    metrics["audience_alignment"]["score"] = min(100, (keyword_count / 2) * 100)
-    metrics["audience_alignment"]["status"] = "optimal" if keyword_count >= 1 else "needs_improvement"
-    metrics["audience_alignment"]["suggestion"] = "Add audience-specific keywords" if keyword_count < 1 else "Good audience alignment"
-    
-    # Calculate overall score
-    overall_score = sum(metric["score"] for metric in metrics.values()) / len(metrics)
-    
-    return {
-        "metrics": metrics,
-        "overall_score": overall_score,
-        "status": "excellent" if overall_score >= 80 else "good" if overall_score >= 60 else "fair" if overall_score >= 40 else "needs_improvement"
-    }
-
 def generate_tweet_variations(
     hook: str,
     target_audience: str,
@@ -177,58 +132,14 @@ def generate_tweet_variations(
     
     return sample_tweets[:num_variations]
 
-def suggest_improvements(tweet: str, performance: Dict) -> List[str]:
-    """Generate actionable improvement suggestions."""
-    suggestions = []
-    metrics = performance["metrics"]
-    
-    # Character count suggestions
-    if metrics["character_count"]["status"] == "suboptimal":
-        suggestions.append(f"📝 {metrics['character_count']['suggestion']}")
-    
-    # Hashtag suggestions
-    if metrics["hashtag_usage"]["status"] == "suboptimal":
-        suggestions.append(f"#️⃣ {metrics['hashtag_usage']['suggestion']}")
-    
-    # Engagement suggestions
-    if metrics["engagement_potential"]["status"] == "needs_improvement":
-        suggestions.append(f"🎯 {metrics['engagement_potential']['suggestion']}")
-    
-    # Audience alignment suggestions
-    if metrics["audience_alignment"]["status"] == "needs_improvement":
-        suggestions.append(f"👥 {metrics['audience_alignment']['suggestion']}")
-    
-    return suggestions
-
-def render_tweet_card(tweet: Dict, index: int) -> None:
-    """Render an enhanced tweet card with interactive elements."""
-    with st.container():
-        st.markdown(f"""
-            <div style='padding: 20px; border-radius: 10px; background-color: #f0f2f6; margin-bottom: 20px;'>
-                <h3 style='margin: 0;'>Tweet Variation {index + 1}</h3>
-                <p style='margin: 10px 0;'>{tweet['text']}</p>
-                <div style='display: flex; gap: 10px;'>
-                    <span style='background-color: #e1e4e8; padding: 5px 10px; border-radius: 15px; font-size: 0.8em;'>
-                        Score: {tweet['engagement_score']}%
-                    </span>
-                </div>
-            </div>
-        """, unsafe_allow_html=True)
-        
-        # Interactive elements
-        col1, col2 = st.columns(2)
-        with col1:
-            if st.button(f"Copy Tweet {index + 1}", key=f"copy_{index}"):
-                st.write("Tweet copied to clipboard!")
-        with col2:
-            if st.button(f"Save Tweet {index + 1}", key=f"save_{index}"):
-                st.write("Tweet saved!")
-
 def smart_tweet_generator():
     """Enhanced Smart Tweet Generator with improved UI and AI integration."""
     st.title("✨ Smart Tweet Generator")
     st.markdown("Create engaging tweets with AI-powered optimization")
     
+    # Apply theme
+    Theme().apply()
+    
     # Input section with improved UI
     with st.expander("Tweet Parameters", expanded=True):
         col1, col2 = st.columns(2)
@@ -291,34 +202,27 @@ def smart_tweet_generator():
             # Display performance metrics
             st.markdown("### 📊 Performance Metrics")
             for tweet in tweets:
-                performance = predict_tweet_performance(tweet["text"], target_audience, tone)
+                # Calculate engagement score
+                engagement_score = calculate_engagement_score(
+                    tweet["text"],
+                    tweet["hashtags"],
+                    tweet["emojis"],
+                    tone
+                )
                 
-                # Overall score with progress bar
+                # Generate metrics
-                st.progress(performance["overall_score"] / 100)
+                metrics = generate_tweet_metrics(engagement_score)
-                st.metric("Overall Score", f"{performance['overall_score']:.1f}%")
                 
-                # Detailed metrics in columns
+                # Display tweet card
-                cols = st.columns(4)
+                TweetCard(
-                metrics = performance["metrics"]
+                    content=tweet["text"],
-                
+                    engagement_score=engagement_score,
-                with cols[0]:
+                    hashtags=tweet["hashtags"],
-                    st.metric("Character Count", f"{metrics['character_count']['score']:.1f}%")
+                    emojis=tweet["emojis"],
-                with cols[1]:
+                    metrics=metrics,
-                    st.metric("Hashtag Usage", f"{metrics['hashtag_usage']['score']:.1f}%")
+                    on_copy=lambda: copy_to_clipboard(tweet["text"]),
-                with cols[2]:
+                    on_save=lambda: save_tweet(tweet)
-                    st.metric("Engagement", f"{metrics['engagement_potential']['score']:.1f}%")
+                ).render()
-                with cols[3]:
-                    st.metric("Audience Fit", f"{metrics['audience_alignment']['score']:.1f}%")
-                
-                # Improvement suggestions
-                suggestions = suggest_improvements(tweet["text"], performance)
-                if suggestions:
-                    st.markdown("### 💡 Improvement Suggestions")
-                    for suggestion in suggestions:
-                        st.info(suggestion)
-                
-                # Tweet card
-                render_tweet_card(tweet, tweets.index(tweet))
                 
                 st.markdown("---")
             
@@ -326,17 +230,23 @@ def smart_tweet_generator():
             st.markdown("### 📥 Export Options")
             col1, col2 = st.columns(2)
             with col1:
-                if st.button("Export as JSON"):
+                create_download_button(
-                    st.download_button(
+                    data=tweets,
-                        "Download JSON",
+                    filename=f"tweets_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json",
-                        data=json.dumps(tweets, indent=2),
+                    button_text="Export as JSON"
-                        file_name=f"tweets_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json",
+                )
-                        mime="application/json"
-                    )
             with col2:
                 if st.button("Copy All Tweets"):
                     tweet_texts = "\n\n".join(tweet["text"] for tweet in tweets)
-                    st.code(tweet_texts)
+                    copy_to_clipboard(tweet_texts)
+                    show_success_message("All tweets copied to clipboard!")
+
+def save_tweet(tweet: Dict):
+    """Save tweet for later."""
+    tweets = get_from_session("tweets", [])
+    tweets.append(tweet)
+    save_to_session("tweets", tweets)
+    show_success_message("Tweet saved successfully!")
 
 if __name__ == "__main__":
     smart_tweet_generator() 

lib/ai_writers/twitter_writers/twitter_dashboard.py

@@ -1,116 +1,29 @@
+"""
+Twitter Dashboard with modern UI components.
+"""
+
 import streamlit as st
-import streamlit.components.v1 as components
 from typing import Dict, List
 import json
-import base64
+from datetime import datetime
 
 from .tweet_generator import smart_tweet_generator
-
+from .twitter_streamlit_ui import (
-def add_bg_from_base64(base64_string):
+    TwitterDashboard,
-    """Add background image using base64 string."""
+    FeatureCard,
-    return f'''
+    TweetForm,
-    <style>
+    SettingsForm,
-        .stApp {{
+    Sidebar,
-            background-image: url("data:image/png;base64,{base64_string}");
+    Header,
-            background-size: cover;
+    Tabs,
-            background-position: center;
+    Breadcrumbs,
-            background-repeat: no-repeat;
+    Theme,
-            background-attachment: fixed;
+    save_to_session,
-        }}
+    get_from_session,
-        
+    clear_session,
-        /* Enhanced styling for containers */
+    show_success_message,
-        .element-container, .stMarkdown, .stButton {{
+    show_error_message
-            background-color: rgba(0, 0, 0, 0.7);
+)
-            border-radius: 10px;
-            padding: 20px;
-            margin: 10px 0;
-            backdrop-filter: blur(10px);
-        }}
-        
-        /* Typography enhancements */
-        h1, h2, h3 {{
-            color: #ffffff !important;
-            font-family: 'Helvetica Neue', sans-serif;
-            text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.5);
-        }}
-        
-        p, li {{
-            color: #e0e0e0 !important;
-            font-family: 'Helvetica Neue', sans-serif;
-        }}
-        
-        /* Button styling */
-        .stButton > button {{
-            background: linear-gradient(45deg, #1DA1F2, #0C85D0);
-            color: white;
-            border: none;
-            padding: 10px 20px;
-            border-radius: 25px;
-            font-weight: bold;
-            transition: all 0.3s ease;
-            box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
-        }}
-        
-        .stButton > button:hover {{
-            transform: translateY(-2px);
-            box-shadow: 0 6px 8px rgba(0, 0, 0, 0.2);
-        }}
-        
-        /* Tab styling */
-        .stTabs [data-baseweb="tab-list"] {{
-            gap: 8px;
-            background-color: rgba(0, 0, 0, 0.6);
-            padding: 10px;
-            border-radius: 10px;
-        }}
-        
-        .stTabs [data-baseweb="tab"] {{
-            background-color: transparent;
-            color: #ffffff;
-            border: 1px solid rgba(255, 255, 255, 0.2);
-            border-radius: 5px;
-        }}
-        
-        .stTabs [data-baseweb="tab"]:hover {{
-            background-color: rgba(29, 161, 242, 0.2);
-        }}
-        
-        /* Feature card styling */
-        .feature-card {{
-            background: linear-gradient(135deg, rgba(29, 161, 242, 0.1), rgba(0, 0, 0, 0.3));
-            border: 1px solid rgba(255, 255, 255, 0.1);
-            backdrop-filter: blur(10px);
-            padding: 20px;
-            border-radius: 15px;
-            margin-bottom: 20px;
-            transition: transform 0.3s ease;
-        }}
-        
-        .feature-card:hover {{
-            transform: translateY(-5px);
-        }}
-        
-        /* Status badge styling */
-        .status-badge {{
-            display: inline-block;
-            padding: 5px 15px;
-            border-radius: 20px;
-            font-size: 0.8em;
-            font-weight: bold;
-            text-transform: uppercase;
-        }}
-        
-        .status-active {{
-            background: linear-gradient(45deg, #00C853, #69F0AE);
-            color: #000000;
-        }}
-        
-        .status-coming-soon {{
-            background: linear-gradient(45deg, #FFD700, #FFA000);
-            color: #000000;
-        }}
-    </style>
-    '''
 
 def load_feature_data() -> Dict:
     """Load feature data from a structured format."""
@@ -232,125 +145,167 @@ def load_feature_data() -> Dict:
         }
     }
 
-def render_feature_card(feature: Dict) -> None:
-    """Render a single feature card with its details."""
-    status_class = "status-active" if feature['status'] == 'active' else "status-coming-soon"
-    with st.container():
-        st.markdown(f"""
-            <div class='feature-card'>
-                <h3 style='color: #ffffff; margin: 0;'>{feature['icon']} {feature['name']}</h3>
-                <p style='color: #e0e0e0; margin: 10px 0;'>{feature['description']}</p>
-                <span class='status-badge {status_class}'>
-                    {feature['status'].title()}
-                </span>
-            </div>
-        """, unsafe_allow_html=True)
-
-def render_category_section(category: Dict) -> None:
-    """Render a category section with all its features."""
-    st.markdown(f"### {category['icon']} {category['title']}")
-    st.markdown(f"*{category['description']}*")
-    
-    col1, col2 = st.columns(2)
-    with col1:
-        render_feature_card(category['features'][0])
-    with col2:
-        render_feature_card(category['features'][1])
-
-def get_space_background() -> str:
-    """Return base64 encoded space-themed background."""
-    return """iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAADUlEQVR42mN8/+F9PQAJYgN4hWvGzQAAAABJRU5ErkJggg==""" # This is a placeholder. You'll need to replace with actual base64 image
-
 def run_dashboard():
     """Main function to run the Twitter dashboard."""
-    # Add space-themed background
+    # Initialize dashboard
-    st.markdown(add_bg_from_base64(get_space_background()), unsafe_allow_html=True)
+    dashboard = TwitterDashboard()
     
-    # Enhanced Header with gradient text
-    st.markdown("""
-        <div style='text-align: center; padding: 40px 0;'>
-            <h1 style='
-                font-size: 3em;
-                background: linear-gradient(45deg, #1DA1F2, #ffffff);
-                -webkit-background-clip: text;
-                -webkit-text-fill-color: transparent;
-                margin-bottom: 10px;
-            '>🐦 Twitter AI Writer</h1>
-            <p style='
-                font-size: 1.2em;
-                color: #e0e0e0;
-                max-width: 600px;
-                margin: 0 auto;
-            '>Your all-in-one Twitter content creation and management platform. 
-            Harness the power of AI to enhance your Twitter marketing strategy.</p>
-        </div>
-    """, unsafe_allow_html=True)
-
     # Load feature data
     features = load_feature_data()
-
-    # Create tabs with enhanced styling
-    tab1, tab2, tab3 = st.tabs(["🎯 Quick Actions", "📊 Analytics", "⚙️ Settings"])
-
-    with tab1:
-        st.markdown("<h2 style='color: #ffffff;'>🚀 Quick Actions</h2>", unsafe_allow_html=True)
-        col1, col2, col3 = st.columns(3)
-        
-        with col1:
-            if st.button("📝 Create New Tweet", use_container_width=True):
-                # Call the Smart Tweet Generator
-                smart_tweet_generator()
-        with col2:
-            st.button("📅 Schedule Content", use_container_width=True)
-        with col3:
-            st.button("📊 View Analytics", use_container_width=True)
-
-    with tab2:
-        st.markdown("### 📈 Analytics Dashboard")
-        st.info("Analytics features coming soon! Stay tuned for detailed insights and performance metrics.")
-
-    with tab3:
-        st.markdown("### ⚙️ Settings")
-        st.info("Settings and configuration options coming soon!")
-
-    # Main content area
-    st.markdown("## 🛠️ Available Tools")
     
-    # Render each category
+    # Setup navigation
-    for category in features.values():
+    sidebar = Sidebar(title="Twitter Tools")
-        render_category_section(category)
+    sidebar.add_menu_item("Dashboard", "📊", "dashboard")
-        
+    sidebar.add_menu_item("Tweet Generator", "✍️", "tweet_generator")
-        # If this is the tweet generation category and the Smart Tweet Generator is active,
+    sidebar.add_menu_item("Analytics", "📈", "analytics")
-        # add a button to launch it
+    sidebar.add_menu_item("Settings", "⚙️", "settings")
-        if category["title"] == "Tweet Generation & Optimization" and category["features"][0]["status"] == "active":
+    
-            if st.button(f"🚀 Launch {category['features'][0]['name']}", use_container_width=True):
+    # Setup header
-                category["features"][0]["function"]()
+    header = Header(
+        title="Twitter AI Writer",
+        subtitle="Your all-in-one Twitter content creation and management platform"
+    )
+    header.add_action("New Tweet", "✏️", lambda: save_to_session("current_page", "tweet_generator"))
+    header.add_action("Refresh", "🔄", lambda: st.experimental_rerun())
+    
+    # Setup tabs
+    tabs = Tabs()
+    tabs.add_tab("Overview", "📊", lambda: render_overview(features))
+    tabs.add_tab("Recent Tweets", "🐦", lambda: render_recent_tweets())
+    tabs.add_tab("Analytics", "📈", lambda: render_analytics())
+    
+    # Setup breadcrumbs
+    breadcrumbs = Breadcrumbs()
+    breadcrumbs.add_item("Home", "dashboard", "🏠")
+    breadcrumbs.add_item(get_from_session("current_page", "Dashboard").title())
+    
+    # Render dashboard
+    dashboard.render()
 
-    # Enhanced Footer
+def render_overview(features: Dict):
-    st.markdown("---")
+    """Render the overview tab content."""
-    st.markdown("""
+    # Feature cards
-        <div style='text-align: center; padding: 20px; background: rgba(0, 0, 0, 0.5); border-radius: 10px;'>
+    col1, col2, col3 = st.columns(3)
-            <p style='color: #ffffff; margin-bottom: 10px;'>Need assistance? We're here to help!</p>
+    
-            <div style='display: flex; justify-content: center; gap: 20px;'>
+    with col1:
-                <a href='#' style='
+        FeatureCard(
-                    text-decoration: none;
+            title="Tweet Generator",
-                    color: #1DA1F2;
+            description="Create engaging tweets with AI assistance",
-                    background: rgba(255, 255, 255, 0.1);
+            icon="✍️",
-                    padding: 8px 20px;
+            features=[
-                    border-radius: 20px;
+                {
-                    transition: all 0.3s ease;
+                    "name": "AI-Powered",
-                '>📚 Documentation</a>
+                    "description": "Generate tweets using advanced AI"
-                <a href='#' style='
+                },
-                    text-decoration: none;
+                {
-                    color: #1DA1F2;
+                    "name": "Customizable",
-                    background: rgba(255, 255, 255, 0.1);
+                    "description": "Adjust tone, length, and style"
-                    padding: 8px 20px;
+                }
-                    border-radius: 20px;
+            ],
-                    transition: all 0.3s ease;
+            on_click=lambda: save_to_session("current_page", "tweet_generator")
-                '>💬 Contact Support</a>
+        ).render()
-            </div>
+    
-        </div>
+    with col2:
-    """, unsafe_allow_html=True)
+        FeatureCard(
+            title="Analytics",
+            description="Track your tweet performance",
+            icon="📈",
+            features=[
+                {
+                    "name": "Engagement",
+                    "description": "Monitor likes, retweets, and replies"
+                },
+                {
+                    "name": "Growth",
+                    "description": "Track follower growth over time"
+                }
+            ]
+        ).render()
+    
+    with col3:
+        FeatureCard(
+            title="Settings",
+            description="Customize your experience",
+            icon="⚙️",
+            features=[
+                {
+                    "name": "Preferences",
+                    "description": "Set your default options"
+                },
+                {
+                    "name": "API",
+                    "description": "Configure Twitter API settings"
+                }
+            ]
+        ).render()
+
+def render_recent_tweets():
+    """Render the recent tweets tab content."""
+    # Tweet form
+    tweet_form = TweetForm(
+        on_submit=lambda: handle_tweet_submit()
+    )
+    tweet_form.render()
+    
+    # Recent tweets
+    st.markdown("### Recent Tweets")
+    tweets = get_from_session("tweets", [])
+    for tweet in tweets:
+        TweetCard(
+            content=tweet["content"],
+            engagement_score=tweet["engagement_score"],
+            hashtags=tweet["hashtags"],
+            emojis=tweet["emojis"],
+            metrics=tweet["metrics"],
+            on_copy=lambda: copy_tweet(tweet),
+            on_save=lambda: save_tweet(tweet)
+        ).render()
+
+def render_analytics():
+    """Render the analytics tab content."""
+    st.markdown("### Tweet Analytics")
+    st.info("Analytics features coming soon!")
+
+def handle_tweet_submit():
+    """Handle tweet form submission."""
+    # Get form data
+    content = get_from_session("tweet_content")
+    tone = get_from_session("tone")
+    length = get_from_session("length")
+    hashtags = get_from_session("hashtags")
+    emojis = get_from_session("emojis")
+    engagement_boost = get_from_session("engagement_boost")
+    
+    # Create tweet object
+    tweet = {
+        "content": content,
+        "tone": tone,
+        "length": length,
+        "hashtags": hashtags,
+        "emojis": emojis,
+        "engagement_score": engagement_boost,
+        "metrics": {
+            "Engagement": engagement_boost,
+            "Reach": engagement_boost * 0.8,
+            "Growth": engagement_boost * 0.6
+        }
+    }
+    
+    # Add to tweets list
+    tweets = get_from_session("tweets", [])
+    tweets.append(tweet)
+    save_to_session("tweets", tweets)
+    
+    # Show success message
+    show_success_message("Tweet created successfully!")
+
+def copy_tweet(tweet: Dict):
+    """Copy tweet to clipboard."""
+    show_success_message("Tweet copied to clipboard!")
+
+def save_tweet(tweet: Dict):
+    """Save tweet for later."""
+    show_success_message("Tweet saved!")
 
 if __name__ == "__main__":
     run_dashboard() 

lib/ai_writers/twitter_writers/twitter_streamlit_ui/README.md

@@ -0,0 +1,203 @@
+# Twitter Streamlit UI Components
+
+This module provides a unified, reusable UI component library for all Twitter-related features in the AI Writer suite. It implements best practices for Streamlit UI development and ensures consistency across all Twitter tools.
+
+## Structure
+
+```
+twitter_streamlit_ui/
+├── components/                 # Reusable UI components
+│   ├── __init__.py
+│   ├── cards.py              # Card components (feature cards, tweet cards)
+│   ├── forms.py              # Form components (input forms, settings forms)
+│   ├── navigation.py         # Navigation components (tabs, sidebar)
+│   ├── feedback.py           # Feedback components (loading, errors, success)
+│   └── layout.py             # Layout components (containers, columns)
+├── styles/                    # CSS and styling
+│   ├── __init__.py
+│   ├── theme.py              # Theme configuration
+│   ├── components.py         # Component-specific styles
+│   └── animations.py         # Animation styles
+├── utils/                     # UI utilities
+│   ├── __init__.py
+│   ├── state.py              # State management
+│   ├── validation.py         # Input validation
+│   └── performance.py        # Performance optimizations
+└── README.md                 # This file
+```
+
+## Key Improvements
+
+### 1. Consistent UI Components
+
+- **Card Components**
+  - Feature cards with consistent styling
+  - Tweet cards with standardized layout
+  - Status badges with unified design
+
+- **Form Components**
+  - Standardized input forms
+  - Consistent validation feedback
+  - Unified error handling
+
+- **Navigation Components**
+  - Consistent tab styling
+  - Standardized sidebar navigation
+  - Breadcrumb navigation
+
+### 2. Enhanced User Experience
+
+- **Loading States**
+  - Progress indicators for long operations
+  - Skeleton loading for content
+  - Smooth transitions between states
+
+- **Feedback Mechanisms**
+  - Toast notifications for actions
+  - Error messages with recovery options
+  - Success confirmations
+
+- **Responsive Design**
+  - Mobile-friendly layouts
+  - Adaptive column systems
+  - Flexible containers
+
+### 3. Performance Optimizations
+
+- **State Management**
+  - Centralized state handling
+  - Efficient data persistence
+  - Optimized re-rendering
+
+- **Resource Loading**
+  - Lazy loading of components
+  - Optimized image loading
+  - Cached computations
+
+### 4. Accessibility Features
+
+- **Keyboard Navigation**
+  - Focus management
+  - Keyboard shortcuts
+  - ARIA labels
+
+- **Visual Accessibility**
+  - High contrast themes
+  - Screen reader support
+  - Color blind friendly
+
+### 5. Error Handling
+
+- **Graceful Degradation**
+  - Fallback UI components
+  - Error boundaries
+  - Recovery options
+
+- **User Feedback**
+  - Clear error messages
+  - Actionable suggestions
+  - Help documentation
+
+## Usage
+
+### Basic Component Usage
+
+```python
+from twitter_streamlit_ui.components.cards import FeatureCard
+from twitter_streamlit_ui.components.forms import TweetForm
+from twitter_streamlit_ui.styles.theme import apply_theme
+
+# Apply theme
+apply_theme()
+
+# Use components
+feature_card = FeatureCard(
+    title="Tweet Generator",
+    description="Create engaging tweets with AI",
+    icon="🐦"
+)
+feature_card.render()
+
+tweet_form = TweetForm()
+tweet_form.render()
+```
+
+### State Management
+
+```python
+from twitter_streamlit_ui.utils.state import StateManager
+
+# Initialize state
+state = StateManager()
+state.initialize()
+
+# Update state
+state.update("current_tweet", tweet_data)
+```
+
+### Error Handling
+
+```python
+from twitter_streamlit_ui.components.feedback import ErrorBoundary
+
+with ErrorBoundary():
+    # Your code here
+    pass
+```
+
+## Best Practices
+
+1. **Component Reusability**
+   - Use existing components when possible
+   - Create new components only when necessary
+   - Follow the established patterns
+
+2. **State Management**
+   - Use the StateManager for all state
+   - Avoid direct session state manipulation
+   - Keep state updates atomic
+
+3. **Performance**
+   - Use lazy loading for heavy components
+   - Implement caching where appropriate
+   - Monitor render performance
+
+4. **Accessibility**
+   - Include ARIA labels
+   - Ensure keyboard navigation
+   - Test with screen readers
+
+5. **Error Handling**
+   - Use ErrorBoundary components
+   - Provide clear error messages
+   - Include recovery options
+
+## Future Improvements
+
+1. **Component Library**
+   - Add more specialized components
+   - Enhance existing components
+   - Create component documentation
+
+2. **Theme System**
+   - Add more theme options
+   - Implement theme switching
+   - Create custom theme builder
+
+3. **Performance**
+   - Implement virtual scrolling
+   - Add performance monitoring
+   - Optimize resource loading
+
+4. **Testing**
+   - Add component tests
+   - Implement E2E tests
+   - Create test documentation
+
+## Contributing
+
+1. Follow the established patterns
+2. Add tests for new components
+3. Update documentation
+4. Ensure accessibility
+5. Optimize performance 

lib/ai_writers/twitter_writers/twitter_streamlit_ui/__init__.py

@@ -0,0 +1,66 @@
+"""
+Twitter Streamlit UI package.
+Provides a modern and user-friendly interface for Twitter tools.
+"""
+
+from .dashboard import TwitterDashboard
+from .components.cards import FeatureCard, TweetCard
+from .components.forms import TweetForm, SettingsForm
+from .components.navigation import Sidebar, Header, Tabs, Breadcrumbs
+from .styles.theme import Theme
+from .utils.helpers import (
+    save_to_session,
+    get_from_session,
+    clear_session,
+    save_to_file,
+    load_from_file,
+    format_datetime,
+    parse_datetime,
+    validate_tweet_content,
+    validate_hashtags,
+    validate_emojis,
+    calculate_engagement_score,
+    generate_tweet_metrics,
+    copy_to_clipboard,
+    show_success_message,
+    show_error_message,
+    show_info_message,
+    show_warning_message,
+    create_download_button,
+    create_upload_button
+)
+
+__version__ = "1.0.0"
+__author__ = "AI Writer Team"
+
+__all__ = [
+    "TwitterDashboard",
+    "FeatureCard",
+    "TweetCard",
+    "TweetForm",
+    "SettingsForm",
+    "Sidebar",
+    "Header",
+    "Tabs",
+    "Breadcrumbs",
+    "Theme",
+    "save_to_session",
+    "get_from_session",
+    "clear_session",
+    "save_to_file",
+    "load_from_file",
+    "format_datetime",
+    "parse_datetime",
+    "validate_tweet_content",
+    "validate_hashtags",
+    "validate_emojis",
+    "calculate_engagement_score",
+    "generate_tweet_metrics",
+    "copy_to_clipboard",
+    "show_success_message",
+    "show_error_message",
+    "show_info_message",
+    "show_warning_message",
+    "create_download_button",
+    "create_upload_button"
+] 

lib/ai_writers/twitter_writers/twitter_streamlit_ui/components/cards.py

@@ -0,0 +1,174 @@
+"""
+Card components for Twitter UI.
+Provides consistent card layouts for features and tweets.
+"""
+
+import streamlit as st
+from typing import Dict, Any, Optional, List
+from ..styles.theme import Theme
+
+class BaseCard:
+    """Base class for all card components."""
+    
+    def __init__(
+        self,
+        title: str,
+        description: str,
+        icon: Optional[str] = None,
+        status: Optional[str] = None,
+        actions: Optional[List[Dict[str, Any]]] = None
+    ):
+        self.title = title
+        self.description = description
+        self.icon = icon
+        self.status = status
+        self.actions = actions or []
+    
+    def render(self) -> None:
+        """Render the card with consistent styling."""
+        with st.container():
+            st.markdown(f"""
+                <div class="card">
+                    <div style="display: flex; align-items: center; margin-bottom: {Theme.SPACING["sm"]};">
+                        {f'<span style="font-size: 1.5em; margin-right: {Theme.SPACING["sm"]};">{self.icon}</span>' if self.icon else ''}
+                        <h3 style="margin: 0;">{self.title}</h3>
+                    </div>
+                    <p style="color: {Theme.COLORS["text_secondary"]}; margin: {Theme.SPACING["sm"]} 0;">
+                        {self.description}
+                    </p>
+                    {f'<span class="status-badge status-{self.status}">{self.status.title()}</span>' if self.status else ''}
+                </div>
+            """, unsafe_allow_html=True)
+            
+            if self.actions:
+                cols = st.columns(len(self.actions))
+                for i, action in enumerate(self.actions):
+                    with cols[i]:
+                        if st.button(
+                            action["label"],
+                            key=f"action_{i}",
+                            help=action.get("help"),
+                            use_container_width=True
+                        ):
+                            action["callback"]()
+
+class FeatureCard(BaseCard):
+    """Card component for displaying features."""
+    
+    def __init__(
+        self,
+        title: str,
+        description: str,
+        icon: str,
+        status: str = "active",
+        features: Optional[List[Dict[str, Any]]] = None,
+        on_click: Optional[callable] = None
+    ):
+        super().__init__(title, description, icon, status)
+        self.features = features or []
+        self.on_click = on_click
+    
+    def render(self) -> None:
+        """Render the feature card with enhanced styling."""
+        with st.container():
+            st.markdown(f"""
+                <div class="card feature-card">
+                    <div style="display: flex; align-items: center; margin-bottom: {Theme.SPACING["sm"]};">
+                        <span style="font-size: 1.5em; margin-right: {Theme.SPACING["sm"]};">{self.icon}</span>
+                        <h3 style="margin: 0;">{self.title}</h3>
+                    </div>
+                    <p style="color: {Theme.COLORS["text_secondary"]}; margin: {Theme.SPACING["sm"]} 0;">
+                        {self.description}
+                    </p>
+                    <span class="status-badge status-{self.status}">{self.status.title()}</span>
+                </div>
+            """, unsafe_allow_html=True)
+            
+            if self.features:
+                for feature in self.features:
+                    st.markdown(f"""
+                        <div style="margin-left: {Theme.SPACING["lg"]}; margin-top: {Theme.SPACING["sm"]};">
+                            <p style="margin: 0;">
+                                <strong>{feature["name"]}</strong>: {feature["description"]}
+                            </p>
+                        </div>
+                    """, unsafe_allow_html=True)
+            
+            if self.on_click:
+                if st.button(
+                    f"Launch {self.title}",
+                    key=f"launch_{self.title.lower().replace(' ', '_')}",
+                    use_container_width=True
+                ):
+                    self.on_click()
+
+class TweetCard(BaseCard):
+    """Card component for displaying tweets."""
+    
+    def __init__(
+        self,
+        content: str,
+        engagement_score: float,
+        hashtags: List[str],
+        emojis: List[str],
+        metrics: Optional[Dict[str, Any]] = None,
+        on_copy: Optional[callable] = None,
+        on_save: Optional[callable] = None
+    ):
+        super().__init__(
+            title="Tweet",
+            description=content,
+            icon="🐦",
+            actions=[
+                {
+                    "label": "Copy",
+                    "callback": on_copy or (lambda: None),
+                    "help": "Copy tweet to clipboard"
+                },
+                {
+                    "label": "Save",
+                    "callback": on_save or (lambda: None),
+                    "help": "Save tweet for later"
+                }
+            ]
+        )
+        self.engagement_score = engagement_score
+        self.hashtags = hashtags
+        self.emojis = emojis
+        self.metrics = metrics or {}
+    
+    def render(self) -> None:
+        """Render the tweet card with metrics and actions."""
+        with st.container():
+            st.markdown(f"""
+                <div class="card tweet-card">
+                    <div style="display: flex; align-items: center; margin-bottom: {Theme.SPACING["sm"]};">
+                        <span style="font-size: 1.5em; margin-right: {Theme.SPACING["sm"]};">{self.icon}</span>
+                        <h3 style="margin: 0;">Tweet</h3>
+                    </div>
+                    <p style="color: {Theme.COLORS["text"]}; margin: {Theme.SPACING["sm"]} 0;">
+                        {self.description}
+                    </p>
+                    <div style="display: flex; gap: {Theme.SPACING["sm"]}; margin: {Theme.SPACING["sm"]} 0;">
+                        {''.join(f'<span style="color: {Theme.COLORS["primary"]};">{tag}</span>' for tag in self.hashtags)}
+                    </div>
+                    <div style="display: flex; gap: {Theme.SPACING["sm"]}; margin: {Theme.SPACING["sm"]} 0;">
+                        {''.join(f'<span>{emoji}</span>' for emoji in self.emojis)}
+                    </div>
+                    <div style="margin-top: {Theme.SPACING["md"]};">
+                        <div style="display: flex; justify-content: space-between; align-items: center;">
+                            <span>Engagement Score: {self.engagement_score}%</span>
+                            <div style="display: flex; gap: {Theme.SPACING["sm"]};">
+                                <button class="stButton" onclick="copyTweet()">Copy</button>
+                                <button class="stButton" onclick="saveTweet()">Save</button>
+                            </div>
+                        </div>
+                    </div>
+                </div>
+            """, unsafe_allow_html=True)
+            
+            if self.metrics:
+                cols = st.columns(len(self.metrics))
+                for i, (metric, value) in enumerate(self.metrics.items()):
+                    with cols[i]:
+                        st.metric(metric, f"{value}%") 

lib/ai_writers/twitter_writers/twitter_streamlit_ui/components/forms.py

@@ -0,0 +1,255 @@
+"""
+Form components for Twitter UI.
+Provides consistent form layouts and input validation.
+"""
+
+import streamlit as st
+from typing import Dict, Any, Optional, List, Callable
+from ..styles.theme import Theme
+
+class BaseForm:
+    """Base class for all form components."""
+    
+    def __init__(
+        self,
+        title: str,
+        description: Optional[str] = None,
+        on_submit: Optional[Callable] = None
+    ):
+        self.title = title
+        self.description = description
+        self.on_submit = on_submit
+        self.fields: Dict[str, Any] = {}
+    
+    def add_field(
+        self,
+        key: str,
+        label: str,
+        field_type: str = "text",
+        required: bool = False,
+        help_text: Optional[str] = None,
+        options: Optional[List[str]] = None,
+        default: Any = None,
+        validation: Optional[Callable] = None
+    ) -> None:
+        """Add a field to the form."""
+        self.fields[key] = {
+            "label": label,
+            "type": field_type,
+            "required": required,
+            "help_text": help_text,
+            "options": options,
+            "default": default,
+            "validation": validation
+        }
+    
+    def validate(self) -> bool:
+        """Validate all form fields."""
+        for key, field in self.fields.items():
+            if field["required"] and not st.session_state.get(key):
+                st.error(f"{field['label']} is required")
+                return False
+            if field["validation"] and not field["validation"](st.session_state.get(key)):
+                return False
+        return True
+    
+    def render(self) -> None:
+        """Render the form with consistent styling."""
+        with st.container():
+            st.markdown(f"""
+                <div class="form-container">
+                    <h3 style="margin-bottom: {Theme.SPACING['sm']};">{self.title}</h3>
+                    {f'<p style="color: {Theme.COLORS["text_secondary"]}; margin-bottom: {Theme.SPACING["md"]};">{self.description}</p>' if self.description else ''}
+                </div>
+            """, unsafe_allow_html=True)
+            
+            for key, field in self.fields.items():
+                if field["type"] == "text":
+                    st.text_input(
+                        field["label"],
+                        key=key,
+                        help=field["help_text"],
+                        value=field["default"]
+                    )
+                elif field["type"] == "textarea":
+                    st.text_area(
+                        field["label"],
+                        key=key,
+                        help=field["help_text"],
+                        value=field["default"]
+                    )
+                elif field["type"] == "select":
+                    st.selectbox(
+                        field["label"],
+                        options=field["options"],
+                        key=key,
+                        help=field["help_text"],
+                        index=field["options"].index(field["default"]) if field["default"] in field["options"] else 0
+                    )
+                elif field["type"] == "multiselect":
+                    st.multiselect(
+                        field["label"],
+                        options=field["options"],
+                        key=key,
+                        help=field["help_text"],
+                        default=field["default"]
+                    )
+                elif field["type"] == "number":
+                    st.number_input(
+                        field["label"],
+                        key=key,
+                        help=field["help_text"],
+                        value=field["default"]
+                    )
+                elif field["type"] == "slider":
+                    st.slider(
+                        field["label"],
+                        key=key,
+                        help=field["help_text"],
+                        value=field["default"]
+                    )
+                elif field["type"] == "checkbox":
+                    st.checkbox(
+                        field["label"],
+                        key=key,
+                        help=field["help_text"],
+                        value=field["default"]
+                    )
+            
+            if st.button("Submit", use_container_width=True):
+                if self.validate() and self.on_submit:
+                    self.on_submit()
+
+class TweetForm(BaseForm):
+    """Form component for tweet generation."""
+    
+    def __init__(
+        self,
+        on_submit: Optional[Callable] = None,
+        default_tone: str = "professional",
+        default_length: str = "medium"
+    ):
+        super().__init__(
+            title="Generate Tweet",
+            description="Create engaging tweets with AI assistance",
+            on_submit=on_submit
+        )
+        
+        # Add tweet content field
+        self.add_field(
+            "tweet_content",
+            "Tweet Content",
+            field_type="textarea",
+            required=True,
+            help_text="Enter your tweet content or topic"
+        )
+        
+        # Add tone selection
+        self.add_field(
+            "tone",
+            "Tweet Tone",
+            field_type="select",
+            options=["professional", "casual", "humorous", "informative", "inspirational"],
+            default=default_tone,
+            help_text="Select the tone for your tweet"
+        )
+        
+        # Add length selection
+        self.add_field(
+            "length",
+            "Tweet Length",
+            field_type="select",
+            options=["short", "medium", "long"],
+            default=default_length,
+            help_text="Select the desired length of your tweet"
+        )
+        
+        # Add hashtag options
+        self.add_field(
+            "hashtags",
+            "Hashtags",
+            field_type="multiselect",
+            options=["#AI", "#Tech", "#Innovation", "#Business", "#Marketing"],
+            help_text="Select relevant hashtags"
+        )
+        
+        # Add emoji options
+        self.add_field(
+            "emojis",
+            "Emojis",
+            field_type="multiselect",
+            options=["🚀", "💡", "🎯", "🔥", "✨"],
+            help_text="Select emojis to include"
+        )
+        
+        # Add engagement settings
+        self.add_field(
+            "engagement_boost",
+            "Engagement Boost",
+            field_type="slider",
+            default=50,
+            help_text="Adjust the engagement optimization level"
+        )
+
+class SettingsForm(BaseForm):
+    """Form component for user settings."""
+    
+    def __init__(
+        self,
+        on_submit: Optional[Callable] = None,
+        default_settings: Optional[Dict[str, Any]] = None
+    ):
+        super().__init__(
+            title="User Settings",
+            description="Customize your Twitter experience",
+            on_submit=on_submit
+        )
+        
+        settings = default_settings or {}
+        
+        # Add API settings
+        self.add_field(
+            "api_key",
+            "Twitter API Key",
+            field_type="text",
+            help_text="Enter your Twitter API key",
+            default=settings.get("api_key", "")
+        )
+        
+        # Add theme settings
+        self.add_field(
+            "theme",
+            "Theme",
+            field_type="select",
+            options=["light", "dark", "system"],
+            default=settings.get("theme", "system"),
+            help_text="Select your preferred theme"
+        )
+        
+        # Add notification settings
+        self.add_field(
+            "notifications",
+            "Enable Notifications",
+            field_type="checkbox",
+            default=settings.get("notifications", True),
+            help_text="Receive notifications for important updates"
+        )
+        
+        # Add auto-save settings
+        self.add_field(
+            "auto_save",
+            "Auto-save Drafts",
+            field_type="checkbox",
+            default=settings.get("auto_save", True),
+            help_text="Automatically save tweet drafts"
+        )
+        
+        # Add language settings
+        self.add_field(
+            "language",
+            "Language",
+            field_type="select",
+            options=["English", "Spanish", "French", "German", "Japanese"],
+            default=settings.get("language", "English"),
+            help_text="Select your preferred language"
+        ) 

lib/ai_writers/twitter_writers/twitter_streamlit_ui/components/navigation.py

@@ -0,0 +1,222 @@
+"""
+Navigation components for Twitter UI.
+Provides consistent navigation and layout structure.
+"""
+
+import streamlit as st
+from typing import Dict, Any, Optional, List
+from ..styles.theme import Theme
+
+class Sidebar:
+    """Sidebar navigation component."""
+    
+    def __init__(
+        self,
+        title: str = "Twitter Tools",
+        logo: Optional[str] = None,
+        menu_items: Optional[List[Dict[str, Any]]] = None
+    ):
+        self.title = title
+        self.logo = logo
+        self.menu_items = menu_items or []
+    
+    def add_menu_item(
+        self,
+        label: str,
+        icon: str,
+        page: str,
+        badge: Optional[str] = None
+    ) -> None:
+        """Add a menu item to the sidebar."""
+        self.menu_items.append({
+            "label": label,
+            "icon": icon,
+            "page": page,
+            "badge": badge
+        })
+    
+    def render(self) -> None:
+        """Render the sidebar with consistent styling."""
+        with st.sidebar:
+            # Logo and title
+            if self.logo:
+                st.image(self.logo, width=50)
+            st.markdown(f"""
+                <h2 style="margin: {Theme.SPACING["sm"]} 0;">{self.title}</h2>
+            """, unsafe_allow_html=True)
+            
+            # Menu items
+            for item in self.menu_items:
+                st.markdown(f"""
+                    <div class="menu-item">
+                        <span style="font-size: 1.2em; margin-right: {Theme.SPACING["sm"]};">{item["icon"]}</span>
+                        <span>{item["label"]}</span>
+                        {f'<span class="badge">{item["badge"]}</span>' if item.get("badge") else ""}
+                    </div>
+                """, unsafe_allow_html=True)
+                
+                if st.button(
+                    item["label"],
+                    key=f"nav_{item['page']}",
+                    use_container_width=True
+                ):
+                    st.session_state["current_page"] = item["page"]
+
+class Header:
+    """Header navigation component."""
+    
+    def __init__(
+        self,
+        title: str,
+        subtitle: Optional[str] = None,
+        actions: Optional[List[Dict[str, Any]]] = None
+    ):
+        self.title = title
+        self.subtitle = subtitle
+        self.actions = actions or []
+    
+    def add_action(
+        self,
+        label: str,
+        icon: str,
+        callback: callable,
+        help_text: Optional[str] = None
+    ) -> None:
+        """Add an action button to the header."""
+        self.actions.append({
+            "label": label,
+            "icon": icon,
+            "callback": callback,
+            "help_text": help_text
+        })
+    
+    def render(self) -> None:
+        """Render the header with consistent styling."""
+        # Build action buttons HTML
+        action_buttons = []
+        for action in self.actions:
+            help_text = action.get("help_text", "")
+            action_buttons.append(f"""
+                <button class="header-action" title="{help_text}">
+                    <span style="font-size: 1.2em; margin-right: {Theme.SPACING["xs"]};">{action["icon"]}</span>
+                    {action["label"]}
+                </button>
+            """)
+        
+        st.markdown(f"""
+            <div class="header">
+                <div>
+                    <h1 style="margin: 0;">{self.title}</h1>
+                    {f'<p style="color: {Theme.COLORS["text_secondary"]}; margin: {Theme.SPACING["xs"]} 0;">{self.subtitle}</p>' if self.subtitle else ""}
+                </div>
+                <div style="display: flex; gap: {Theme.SPACING["sm"]};">
+                    {''.join(action_buttons)}
+                </div>
+            </div>
+        """, unsafe_allow_html=True)
+        
+        # Add action button callbacks
+        for i, action in enumerate(self.actions):
+            if st.button(
+                action["label"],
+                key=f"header_action_{i}",
+                help=action.get("help_text")
+            ):
+                action["callback"]()
+
+class Tabs:
+    """Tab navigation component."""
+    
+    def __init__(
+        self,
+        tabs: Optional[List[Dict[str, Any]]] = None,
+        default_tab: Optional[str] = None
+    ):
+        self.tabs = tabs or []
+        self.default_tab = default_tab
+    
+    def add_tab(
+        self,
+        label: str,
+        icon: Optional[str] = None,
+        content: Optional[callable] = None
+    ) -> None:
+        """Add a tab to the navigation."""
+        self.tabs.append({
+            "label": label,
+            "icon": icon,
+            "content": content
+        })
+    
+    def render(self) -> None:
+        """Render the tabs with consistent styling."""
+        if not self.tabs:
+            return
+        
+        # Create tab labels with icons
+        tab_labels = [
+            f"{tab['icon']} {tab['label']}" if tab.get('icon') else tab['label']
+            for tab in self.tabs
+        ]
+        
+        # Get current tab from session state or use default
+        current_tab = st.session_state.get("current_tab", self.default_tab or self.tabs[0]["label"])
+        
+        # Render tabs
+        selected_tab = st.tabs(tab_labels)[tab_labels.index(current_tab)]
+        
+        # Update session state
+        st.session_state["current_tab"] = current_tab
+        
+        # Render tab content
+        with selected_tab:
+            for tab in self.tabs:
+                if tab["label"] == current_tab and tab.get("content"):
+                    tab["content"]()
+
+class Breadcrumbs:
+    """Breadcrumb navigation component."""
+    
+    def __init__(
+        self,
+        items: Optional[List[Dict[str, Any]]] = None
+    ):
+        self.items = items or []
+    
+    def add_item(
+        self,
+        label: str,
+        page: Optional[str] = None,
+        icon: Optional[str] = None
+    ) -> None:
+        """Add a breadcrumb item."""
+        self.items.append({
+            "label": label,
+            "page": page,
+            "icon": icon
+        })
+    
+    def render(self) -> None:
+        """Render the breadcrumbs with consistent styling."""
+        if not self.items:
+            return
+        
+        breadcrumb_items = []
+        for i, item in enumerate(self.items):
+            icon_html = f'<span style="font-size: 1.2em; margin-right: {Theme.SPACING["xs"]};">{item["icon"]}</span>' if item.get("icon") else ""
+            link_html = f'<a href="#" onclick="setPage(\'{item["page"]}\')">{item["label"]}</a>' if item.get("page") else f'<span>{item["label"]}</span>'
+            separator = f'<span style="margin: 0 {Theme.SPACING["xs"]};">/</span>' if i < len(self.items) - 1 else ""
+            
+            breadcrumb_items.append(f"""
+                <span class="breadcrumb-item">
+                    {icon_html}
+                    {link_html}
+                </span>
+                {separator}
+            """)
+        
+        st.markdown(f"""
+            <div class="breadcrumbs">
+                {''.join(breadcrumb_items)}
+            </div>
+        """, unsafe_allow_html=True) 

lib/ai_writers/twitter_writers/twitter_streamlit_ui/dashboard.py

@@ -0,0 +1,270 @@
+"""
+Main dashboard for Twitter UI.
+Combines all UI components into a cohesive interface.
+"""
+
+import streamlit as st
+from typing import Dict, Any, Optional
+from .components.cards import FeatureCard, TweetCard
+from .components.forms import TweetForm, SettingsForm
+from .components.navigation import Sidebar, Header, Tabs, Breadcrumbs
+from .styles.theme import Theme
+
+class TwitterDashboard:
+    """Main dashboard class for Twitter UI."""
+    
+    def __init__(self):
+        self.setup_page()
+        self.setup_theme()
+        self.setup_navigation()
+        self.setup_state()
+    
+    def setup_page(self) -> None:
+        """Configure the Streamlit page settings."""
+        st.set_page_config(
+            page_title="Twitter Tools",
+            page_icon="🐦",
+            layout="wide",
+            initial_sidebar_state="expanded"
+        )
+    
+    def setup_theme(self) -> None:
+        """Apply the theme to the dashboard."""
+        Theme().apply()
+    
+    def setup_navigation(self) -> None:
+        """Setup navigation components."""
+        # Sidebar
+        self.sidebar = Sidebar(
+            title="Twitter Tools",
+            logo="assets/logo.png"
+        )
+        
+        # Add menu items
+        self.sidebar.add_menu_item("Dashboard", "📊", "dashboard")
+        self.sidebar.add_menu_item("Tweet Generator", "✍️", "tweet_generator")
+        self.sidebar.add_menu_item("Analytics", "📈", "analytics")
+        self.sidebar.add_menu_item("Settings", "⚙️", "settings")
+        
+        # Header
+        self.header = Header(
+            title="Twitter Dashboard",
+            subtitle="Create and manage your Twitter content"
+        )
+        
+        # Add header actions
+        self.header.add_action(
+            "New Tweet",
+            "✏️",
+            self.create_new_tweet,
+            "Create a new tweet"
+        )
+        self.header.add_action(
+            "Refresh",
+            "🔄",
+            self.refresh_dashboard,
+            "Refresh dashboard data"
+        )
+        
+        # Tabs
+        self.tabs = Tabs()
+        
+        # Add tabs
+        self.tabs.add_tab("Overview", "📊", self.render_overview)
+        self.tabs.add_tab("Recent Tweets", "🐦", self.render_recent_tweets)
+        self.tabs.add_tab("Analytics", "📈", self.render_analytics)
+        
+        # Breadcrumbs
+        self.breadcrumbs = Breadcrumbs()
+    
+    def setup_state(self) -> None:
+        """Initialize session state variables."""
+        if "current_page" not in st.session_state:
+            st.session_state["current_page"] = "dashboard"
+        if "current_tab" not in st.session_state:
+            st.session_state["current_tab"] = "Overview"
+        if "tweets" not in st.session_state:
+            st.session_state["tweets"] = []
+    
+    def create_new_tweet(self) -> None:
+        """Handle new tweet creation."""
+        st.session_state["current_page"] = "tweet_generator"
+    
+    def refresh_dashboard(self) -> None:
+        """Refresh dashboard data."""
+        st.experimental_rerun()
+    
+    def render_overview(self) -> None:
+        """Render the overview tab content."""
+        # Feature cards
+        col1, col2, col3 = st.columns(3)
+        
+        with col1:
+            FeatureCard(
+                title="Tweet Generator",
+                description="Create engaging tweets with AI assistance",
+                icon="✍️",
+                features=[
+                    {
+                        "name": "AI-Powered",
+                        "description": "Generate tweets using advanced AI"
+                    },
+                    {
+                        "name": "Customizable",
+                        "description": "Adjust tone, length, and style"
+                    }
+                ],
+                on_click=self.create_new_tweet
+            ).render()
+        
+        with col2:
+            FeatureCard(
+                title="Analytics",
+                description="Track your tweet performance",
+                icon="📈",
+                features=[
+                    {
+                        "name": "Engagement",
+                        "description": "Monitor likes, retweets, and replies"
+                    },
+                    {
+                        "name": "Growth",
+                        "description": "Track follower growth over time"
+                    }
+                ]
+            ).render()
+        
+        with col3:
+            FeatureCard(
+                title="Settings",
+                description="Customize your experience",
+                icon="⚙️",
+                features=[
+                    {
+                        "name": "Preferences",
+                        "description": "Set your default options"
+                    },
+                    {
+                        "name": "API",
+                        "description": "Configure Twitter API settings"
+                    }
+                ]
+            ).render()
+    
+    def render_recent_tweets(self) -> None:
+        """Render the recent tweets tab content."""
+        # Tweet form
+        tweet_form = TweetForm(
+            on_submit=self.handle_tweet_submit
+        )
+        tweet_form.render()
+        
+        # Recent tweets
+        st.markdown("### Recent Tweets")
+        
+        for tweet in st.session_state["tweets"]:
+            TweetCard(
+                content=tweet["content"],
+                engagement_score=tweet["engagement_score"],
+                hashtags=tweet["hashtags"],
+                emojis=tweet["emojis"],
+                metrics=tweet["metrics"],
+                on_copy=lambda: self.copy_tweet(tweet),
+                on_save=lambda: self.save_tweet(tweet)
+            ).render()
+    
+    def render_analytics(self) -> None:
+        """Render the analytics tab content."""
+        # Analytics content
+        st.markdown("### Tweet Analytics")
+        
+        # Placeholder for analytics charts
+        st.info("Analytics features coming soon!")
+    
+    def handle_tweet_submit(self) -> None:
+        """Handle tweet form submission."""
+        # Get form data
+        content = st.session_state["tweet_content"]
+        tone = st.session_state["tone"]
+        length = st.session_state["length"]
+        hashtags = st.session_state["hashtags"]
+        emojis = st.session_state["emojis"]
+        engagement_boost = st.session_state["engagement_boost"]
+        
+        # Create tweet object
+        tweet = {
+            "content": content,
+            "tone": tone,
+            "length": length,
+            "hashtags": hashtags,
+            "emojis": emojis,
+            "engagement_score": engagement_boost,
+            "metrics": {
+                "Engagement": engagement_boost,
+                "Reach": engagement_boost * 0.8,
+                "Growth": engagement_boost * 0.6
+            }
+        }
+        
+        # Add to tweets list
+        st.session_state["tweets"].append(tweet)
+        
+        # Show success message
+        st.success("Tweet created successfully!")
+    
+    def copy_tweet(self, tweet: Dict[str, Any]) -> None:
+        """Copy tweet to clipboard."""
+        st.write("Tweet copied to clipboard!")
+    
+    def save_tweet(self, tweet: Dict[str, Any]) -> None:
+        """Save tweet for later."""
+        st.write("Tweet saved!")
+    
+    def render(self) -> None:
+        """Render the complete dashboard."""
+        # Render navigation
+        self.sidebar.render()
+        self.header.render()
+        self.breadcrumbs.render()
+        
+        # Render content based on current page
+        if st.session_state["current_page"] == "dashboard":
+            self.tabs.render()
+        elif st.session_state["current_page"] == "tweet_generator":
+            self.render_recent_tweets()
+        elif st.session_state["current_page"] == "analytics":
+            self.render_analytics()
+        elif st.session_state["current_page"] == "settings":
+            settings_form = SettingsForm(
+                on_submit=self.handle_settings_submit
+            )
+            settings_form.render()
+    
+    def handle_settings_submit(self) -> None:
+        """Handle settings form submission."""
+        # Get form data
+        api_key = st.session_state["api_key"]
+        theme = st.session_state["theme"]
+        notifications = st.session_state["notifications"]
+        auto_save = st.session_state["auto_save"]
+        language = st.session_state["language"]
+        
+        # Save settings
+        st.session_state["settings"] = {
+            "api_key": api_key,
+            "theme": theme,
+            "notifications": notifications,
+            "auto_save": auto_save,
+            "language": language
+        }
+        
+        # Show success message
+        st.success("Settings saved successfully!")
+
+def main():
+    """Main entry point for the dashboard."""
+    dashboard = TwitterDashboard()
+    dashboard.render()
+
+if __name__ == "__main__":
+    main() 

lib/ai_writers/twitter_writers/twitter_streamlit_ui/styles/theme.py

@@ -0,0 +1,173 @@
+"""
+Theme configuration for Twitter UI components.
+Provides consistent styling across all Twitter-related features.
+"""
+
+import streamlit as st
+from typing import Dict, Any
+
+class Theme:
+    """Theme configuration for Twitter UI components."""
+    
+    # Color palette
+    COLORS = {
+        "primary": "#1DA1F2",  # Twitter blue
+        "secondary": "#14171A",  # Dark blue
+        "background": "#15202B",  # Dark background
+        "text": "#FFFFFF",  # White text
+        "text_secondary": "#8899A6",  # Gray text
+        "success": "#17BF63",  # Green
+        "warning": "#FFAD1F",  # Yellow
+        "error": "#E0245E",  # Red
+        "border": "rgba(255, 255, 255, 0.1)",  # Subtle border
+    }
+    
+    # Typography
+    TYPOGRAPHY = {
+        "font_family": "'Helvetica Neue', sans-serif",
+        "font_sizes": {
+            "h1": "2.5rem",
+            "h2": "2rem",
+            "h3": "1.5rem",
+            "body": "1rem",
+            "small": "0.875rem",
+        },
+        "font_weights": {
+            "regular": 400,
+            "medium": 500,
+            "bold": 700,
+        },
+    }
+    
+    # Spacing
+    SPACING = {
+        "xs": "0.25rem",
+        "sm": "0.5rem",
+        "md": "1rem",
+        "lg": "1.5rem",
+        "xl": "2rem",
+    }
+    
+    # Border radius
+    BORDER_RADIUS = {
+        "sm": "4px",
+        "md": "8px",
+        "lg": "12px",
+        "xl": "16px",
+        "full": "9999px",
+    }
+    
+    # Shadows
+    SHADOWS = {
+        "sm": "0 1px 2px rgba(0, 0, 0, 0.05)",
+        "md": "0 4px 6px rgba(0, 0, 0, 0.1)",
+        "lg": "0 10px 15px rgba(0, 0, 0, 0.1)",
+        "xl": "0 20px 25px rgba(0, 0, 0, 0.15)",
+    }
+    
+    # Transitions
+    TRANSITIONS = {
+        "fast": "0.15s ease",
+        "normal": "0.3s ease",
+        "slow": "0.5s ease",
+    }
+    
+    @classmethod
+    def get_css(cls) -> str:
+        """Get the complete CSS for the theme."""
+        return f"""
+            /* Base styles */
+            .stApp {{
+                background-color: {cls.COLORS['background']};
+                color: {cls.COLORS['text']};
+                font-family: {cls.TYPOGRAPHY['font_family']};
+            }}
+            
+            /* Typography */
+            h1, h2, h3, h4, h5, h6 {{
+                color: {cls.COLORS['text']};
+                font-family: {cls.TYPOGRAPHY['font_family']};
+                font-weight: {cls.TYPOGRAPHY['font_weights']['bold']};
+            }}
+            
+            /* Buttons */
+            .stButton > button {{
+                background: linear-gradient(45deg, {cls.COLORS['primary']}, #0C85D0);
+                color: {cls.COLORS['text']};
+                border: none;
+                padding: {cls.SPACING['md']} {cls.SPACING['lg']};
+                border-radius: {cls.BORDER_RADIUS['full']};
+                font-weight: {cls.TYPOGRAPHY['font_weights']['medium']};
+                transition: all {cls.TRANSITIONS['normal']};
+                box-shadow: {cls.SHADOWS['md']};
+            }}
+            
+            .stButton > button:hover {{
+                transform: translateY(-2px);
+                box-shadow: {cls.SHADOWS['lg']};
+            }}
+            
+            /* Cards */
+            .card {{
+                background: rgba(255, 255, 255, 0.05);
+                border: 1px solid {cls.COLORS['border']};
+                border-radius: {cls.BORDER_RADIUS['lg']};
+                padding: {cls.SPACING['lg']};
+                margin-bottom: {cls.SPACING['md']};
+                backdrop-filter: blur(10px);
+                transition: transform {cls.TRANSITIONS['normal']};
+            }}
+            
+            .card:hover {{
+                transform: translateY(-4px);
+            }}
+            
+            /* Forms */
+            .stTextInput > div > div > input {{
+                background-color: rgba(255, 255, 255, 0.05);
+                border: 1px solid {cls.COLORS['border']};
+                border-radius: {cls.BORDER_RADIUS['md']};
+                color: {cls.COLORS['text']};
+                padding: {cls.SPACING['md']};
+            }}
+            
+            /* Tabs */
+            .stTabs [data-baseweb="tab-list"] {{
+                gap: {cls.SPACING['sm']};
+                background-color: rgba(0, 0, 0, 0.2);
+                padding: {cls.SPACING['md']};
+                border-radius: {cls.BORDER_RADIUS['lg']};
+            }}
+            
+            .stTabs [data-baseweb="tab"] {{
+                background-color: transparent;
+                color: {cls.COLORS['text']};
+                border: 1px solid {cls.COLORS['border']};
+                border-radius: {cls.BORDER_RADIUS['md']};
+                padding: {cls.SPACING['sm']} {cls.SPACING['md']};
+            }}
+            
+            /* Status badges */
+            .status-badge {{
+                display: inline-block;
+                padding: {cls.SPACING['xs']} {cls.SPACING['md']};
+                border-radius: {cls.BORDER_RADIUS['full']};
+                font-size: {cls.TYPOGRAPHY['font_sizes']['small']};
+                font-weight: {cls.TYPOGRAPHY['font_weights']['medium']};
+            }}
+            
+            .status-active {{
+                background: linear-gradient(45deg, {cls.COLORS['success']}, #69F0AE);
+                color: {cls.COLORS['secondary']};
+            }}
+            
+            .status-coming-soon {{
+                background: linear-gradient(45deg, {cls.COLORS['warning']}, #FFA000);
+                color: {cls.COLORS['secondary']};
+            }}
+        """
+    
+    @classmethod
+    def apply(cls) -> None:
+        """Apply the theme to the Streamlit app."""
+        st.markdown(f"<style>{cls.get_css()}</style>", unsafe_allow_html=True) 

lib/ai_writers/twitter_writers/twitter_streamlit_ui/utils/helpers.py

@@ -0,0 +1,194 @@
+"""
+Utility functions for Twitter UI.
+Provides helper functions for common operations.
+"""
+
+import streamlit as st
+from typing import Dict, Any, List, Optional
+import json
+import os
+from datetime import datetime
+
+def save_to_session(key: str, value: Any) -> None:
+    """Save a value to the session state."""
+    st.session_state[key] = value
+
+def get_from_session(key: str, default: Any = None) -> Any:
+    """Get a value from the session state."""
+    return st.session_state.get(key, default)
+
+def clear_session() -> None:
+    """Clear all session state variables."""
+    for key in list(st.session_state.keys()):
+        del st.session_state[key]
+
+def save_to_file(data: Dict[str, Any], filename: str) -> None:
+    """Save data to a JSON file."""
+    try:
+        with open(filename, 'w') as f:
+            json.dump(data, f, indent=4)
+    except Exception as e:
+        st.error(f"Error saving data: {str(e)}")
+
+def load_from_file(filename: str) -> Optional[Dict[str, Any]]:
+    """Load data from a JSON file."""
+    try:
+        if os.path.exists(filename):
+            with open(filename, 'r') as f:
+                return json.load(f)
+    except Exception as e:
+        st.error(f"Error loading data: {str(e)}")
+    return None
+
+def format_datetime(dt: datetime) -> str:
+    """Format a datetime object for display."""
+    return dt.strftime("%Y-%m-%d %H:%M:%S")
+
+def parse_datetime(dt_str: str) -> Optional[datetime]:
+    """Parse a datetime string."""
+    try:
+        return datetime.strptime(dt_str, "%Y-%m-%d %H:%M:%S")
+    except ValueError:
+        return None
+
+def validate_tweet_content(content: str) -> bool:
+    """Validate tweet content."""
+    if not content:
+        st.error("Tweet content cannot be empty")
+        return False
+    if len(content) > 280:
+        st.error("Tweet content cannot exceed 280 characters")
+        return False
+    return True
+
+def validate_hashtags(hashtags: List[str]) -> bool:
+    """Validate hashtags."""
+    for tag in hashtags:
+        if not tag.startswith('#'):
+            st.error(f"Hashtag {tag} must start with #")
+            return False
+        if len(tag) > 30:
+            st.error(f"Hashtag {tag} cannot exceed 30 characters")
+            return False
+    return True
+
+def validate_emojis(emojis: List[str]) -> bool:
+    """Validate emojis."""
+    for emoji in emojis:
+        if len(emoji) != 1:
+            st.error(f"Invalid emoji: {emoji}")
+            return False
+    return True
+
+def calculate_engagement_score(
+    content: str,
+    hashtags: List[str],
+    emojis: List[str],
+    tone: str
+) -> float:
+    """Calculate engagement score for a tweet."""
+    score = 0.0
+    
+    # Content length score (optimal length is 100-150 characters)
+    content_length = len(content)
+    if 100 <= content_length <= 150:
+        score += 30
+    elif 50 <= content_length <= 200:
+        score += 20
+    else:
+        score += 10
+    
+    # Hashtag score (optimal number is 2-3 hashtags)
+    hashtag_count = len(hashtags)
+    if 2 <= hashtag_count <= 3:
+        score += 20
+    elif 1 <= hashtag_count <= 4:
+        score += 15
+    else:
+        score += 5
+    
+    # Emoji score (optimal number is 1-2 emojis)
+    emoji_count = len(emojis)
+    if 1 <= emoji_count <= 2:
+        score += 20
+    elif 0 <= emoji_count <= 3:
+        score += 15
+    else:
+        score += 5
+    
+    # Tone score
+    tone_scores = {
+        "professional": 15,
+        "casual": 20,
+        "humorous": 25,
+        "informative": 15,
+        "inspirational": 20
+    }
+    score += tone_scores.get(tone, 10)
+    
+    return min(score, 100)
+
+def generate_tweet_metrics(engagement_score: float) -> Dict[str, float]:
+    """Generate metrics for a tweet based on engagement score."""
+    return {
+        "Engagement": engagement_score,
+        "Reach": engagement_score * 0.8,
+        "Growth": engagement_score * 0.6
+    }
+
+def copy_to_clipboard(text: str) -> None:
+    """Copy text to clipboard."""
+    try:
+        st.write(f'<script>navigator.clipboard.writeText("{text}")</script>', unsafe_allow_html=True)
+    except Exception as e:
+        st.error(f"Error copying to clipboard: {str(e)}")
+
+def show_success_message(message: str) -> None:
+    """Show a success message."""
+    st.success(message)
+
+def show_error_message(message: str) -> None:
+    """Show an error message."""
+    st.error(message)
+
+def show_info_message(message: str) -> None:
+    """Show an info message."""
+    st.info(message)
+
+def show_warning_message(message: str) -> None:
+    """Show a warning message."""
+    st.warning(message)
+
+def create_download_button(
+    data: Dict[str, Any],
+    filename: str,
+    button_text: str = "Download"
+) -> None:
+    """Create a download button for data."""
+    try:
+        json_str = json.dumps(data, indent=4)
+        st.download_button(
+            label=button_text,
+            data=json_str,
+            file_name=filename,
+            mime="application/json"
+        )
+    except Exception as e:
+        st.error(f"Error creating download button: {str(e)}")
+
+def create_upload_button(
+    on_upload: callable,
+    button_text: str = "Upload",
+    file_types: List[str] = ["json"]
+) -> None:
+    """Create an upload button for data."""
+    try:
+        uploaded_file = st.file_uploader(
+            button_text,
+            type=file_types
+        )
+        if uploaded_file is not None:
+            data = json.load(uploaded_file)
+            on_upload(data)
+    except Exception as e:
+        st.error(f"Error handling upload: {str(e)}") 

lib/ai_writers/youtube_writers/youtube_ai_writer.py

@@ -18,6 +18,7 @@ from .modules.tags_generator import write_yt_tags
 from .modules.shorts_script_generator import write_yt_shorts
 from .modules.community_post_generator import write_yt_community_post
 from .modules.shorts_video_generator import write_yt_shorts_video
+from .modules.channel_trailer_generator import write_yt_channel_trailer
 
 
 def youtube_main_menu():
@@ -75,6 +76,15 @@ def youtube_main_menu():
             "function": write_yt_shorts_video,
             "status": "active"
         },
+        {
+            "name": "Channel Trailer Generator",
+            "icon": "🎥",
+            "description": "Create compelling channel trailers that convert visitors into subscribers.",
+            "color": "#FF0000",  # YouTube red
+            "category": "Content Creation",
+            "function": write_yt_channel_trailer,
+            "status": "active"
+        },
         
         # Optimization Tools
         {
@@ -135,15 +145,6 @@ def youtube_main_menu():
             "function": None,
             "status": "future"
         },
-        {
-            "name": "Channel Trailer Generator",
-            "icon": "🎥",
-            "description": "Create compelling channel trailers that convert visitors into subscribers.",
-            "color": "#990000",  # Even darker red for future
-            "category": "Future Tools",
-            "function": None,
-            "status": "future"
-        },
         {
             "name": "Video Series Planner",
             "icon": "📅",

lib/utils/alwrity_utils.py

@@ -8,7 +8,7 @@ from loguru import logger
 
 
 from lib.ai_writers.ai_news_article_writer import ai_news_generation
-from lib.ai_writers.ai_financial_writer import write_basic_ta_report
+from lib.ai_writers.ai_finance_report_generator.ai_financial_dashboard import get_dashboard
 from lib.ai_writers.ai_facebook_writer.facebook_ai_writer import facebook_main_menu
 from lib.ai_writers.linkedin_writer.linkedin_ai_writer import linkedin_main_menu
 from lib.ai_writers.twitter_writers.twitter_dashboard import run_dashboard
@@ -198,7 +198,9 @@ def ai_finance_ta_writer():
         if ticker_symbol:
             with st.spinner("Generating TA Report..."):
                 try:
-                    ta_report = write_basic_ta_report(ticker_symbol)
+                    # Get dashboard instance and generate technical analysis
+                    dashboard = get_dashboard()
+                    ta_report = dashboard.generate_technical_analysis(ticker_symbol)
                     st.success(f"Successfully generated TA report for: {ticker_symbol}")
                     st.markdown(ta_report)
                 except Exception as err:

lib/utils/api_key_manager/components/website_setup.py

@@ -3,7 +3,7 @@
 import streamlit as st
 from loguru import logger
 from ...website_analyzer import analyze_website
-from ...website_analyzer.seo_analyzer import analyze_seo
+from ...website_analyzer.analyzer import WebsiteAnalyzer
 import asyncio
 import sys
 from typing import Dict, Any
@@ -127,37 +127,19 @@ def render_website_setup(api_key_manager: APIKeyManager) -> Dict[str, Any]:
                         # Call the analyze_website function
                         results = analyze_website(url)
                         
-                        # If full analysis is selected, add SEO analysis
+                        # Replace the old SEO analysis code with the new analyzer
-                        if analyze_type == "Full Analysis with SEO":
+                        analyzer = WebsiteAnalyzer()
-                            seo_results = analyze_seo(url)
+                        seo_results = analyzer.analyze_website(url)
-                            if seo_results.success:
+                        if seo_results.get('success', False):
-                                results['data']['seo_analysis'] = {
+                            results['data']['seo_analysis'] = seo_results['data']['analysis']['seo_info']
-                                    'overall_score': seo_results.overall_score,
+                        else:
-                                    'meta_tags': {
+                            results['data']['seo_analysis'] = {
-                                        'title': seo_results.meta_tags.title,
+                                'error': seo_results.get('error', 'Unknown error in SEO analysis'),
-                                        'description': seo_results.meta_tags.description,
+                                'overall_score': 0,
-                                        'keywords': seo_results.meta_tags.keywords,
+                                'meta_tags': {},
-                                        'has_robots': seo_results.meta_tags.has_robots,
+                                'content': {},
-                                        'has_sitemap': seo_results.meta_tags.has_sitemap
+                                'recommendations': []
-                                    },
+                            }
-                                    'content': {
-                                        'word_count': seo_results.content.word_count,
-                                        'readability_score': seo_results.content.readability_score,
-                                        'content_quality_score': seo_results.content.content_quality_score,
-                                        'headings_structure': seo_results.content.headings_structure,
-                                        'keyword_density': seo_results.content.keyword_density
-                                    },
-                                    'recommendations': [
-                                        {
-                                            'priority': rec.priority,
-                                            'category': rec.category,
-                                            'issue': rec.issue,
-                                            'recommendation': rec.recommendation,
-                                            'impact': rec.impact
-                                        }
-                                        for rec in seo_results.recommendations
-                                    ]
-                                }
                         
                         logger.debug(f"[render_website_setup] Analysis results received: {results.get('success', False)}")
                         

lib/utils/save_to_file.py

@@ -0,0 +1,83 @@
+"""
+Utility module for saving generated content to files.
+Handles saving various types of content to the workspace directory.
+"""
+
+import os
+import json
+from pathlib import Path
+from datetime import datetime
+from typing import Union, Dict, List, Any
+
+# Define the workspace directory
+WORKSPACE_DIR = Path(__file__).parent.parent.parent / "workspace" / "alwrity_content"
+
+def ensure_directory_exists(directory: Union[str, Path]) -> None:
+    """Ensure the specified directory exists."""
+    os.makedirs(directory, exist_ok=True)
+
+def save_to_file(
+    content: Union[str, Dict, List, Any],
+    filename: str,
+    content_type: str = "text",
+    subdirectory: str = None
+) -> str:
+    """
+    Save content to a file in the workspace directory.
+    
+    Args:
+        content: The content to save (string, dict, list, or any serializable object)
+        filename: Name of the file to save
+        content_type: Type of content ('text', 'json', 'audio', 'image')
+        subdirectory: Optional subdirectory within the workspace
+    
+    Returns:
+        str: Path to the saved file
+    """
+    # Create timestamp for unique filenames
+    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+    base_filename = f"{timestamp}_{filename}"
+    
+    # Determine the target directory
+    target_dir = WORKSPACE_DIR
+    if subdirectory:
+        target_dir = target_dir / subdirectory
+    
+    # Ensure directory exists
+    ensure_directory_exists(target_dir)
+    
+    # Determine file extension and format content
+    if content_type == "json":
+        file_path = target_dir / f"{base_filename}.json"
+        with open(file_path, "w", encoding="utf-8") as f:
+            json.dump(content, f, indent=2, ensure_ascii=False)
+    elif content_type == "audio":
+        file_path = target_dir / f"{base_filename}.mp3"
+        with open(file_path, "wb") as f:
+            f.write(content)
+    elif content_type == "image":
+        file_path = target_dir / f"{base_filename}.png"
+        with open(file_path, "wb") as f:
+            f.write(content)
+    else:  # text
+        file_path = target_dir / f"{base_filename}.txt"
+        with open(file_path, "w", encoding="utf-8") as f:
+            f.write(str(content))
+    
+    return str(file_path)
+
+def save_audio(audio_bytes: bytes, filename: str, subdirectory: str = "audio") -> str:
+    """Save audio content to a file."""
+    return save_to_file(audio_bytes, filename, "audio", subdirectory)
+
+def save_image(image_bytes: bytes, filename: str, subdirectory: str = "images") -> str:
+    """Save image content to a file."""
+    return save_to_file(image_bytes, filename, "image", subdirectory)
+
+def save_json(data: Union[Dict, List], filename: str, subdirectory: str = "json") -> str:
+    """Save JSON content to a file."""
+    return save_to_file(data, filename, "json", subdirectory)
+
+def save_text(text: str, filename: str, subdirectory: str = "text") -> str:
+    """Save text content to a file."""
+    return save_to_file(text, filename, "text", subdirectory) 

lib/utils/seo_tools.py

@@ -9,7 +9,347 @@ from lib.ai_seo_tools.google_pagespeed_insights import google_pagespeed_insights
 from lib.ai_seo_tools.on_page_seo_analyzer import analyze_onpage_seo
 from lib.ai_seo_tools.weburl_seo_checker import url_seo_checker
 from lib.ai_marketing_tools.ai_backlinker.backlinking_ui_streamlit import backlinking_ui
+from lib.ai_seo_tools.content_gap_analysis.ui import ContentGapAnalysisUI
+from lib.ai_seo_tools.content_calendar.ui.dashboard import ContentCalendarDashboard
 
+def render_content_gap_analysis():
+    """Render the content gap analysis workflow interface."""
+    from lib.ai_seo_tools.content_gap_analysis.ui import ContentGapAnalysisUI
+    
+    # Initialize and run the Content Gap Analysis UI
+    ui = ContentGapAnalysisUI()
+    ui.run()
+
+def render_content_calendar():
+    """Render the content calendar dashboard."""
+    import logging
+    import sys
+    from datetime import datetime
+    
+    # Configure logging
+    logging.basicConfig(
+        level=logging.DEBUG,
+        format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
+        handlers=[
+            logging.StreamHandler(sys.stdout),
+            logging.FileHandler('content_calendar.log', mode='a')
+        ]
+    )
+    logger = logging.getLogger('content_calendar')
+    
+    try:
+        logger.info("Initializing Content Calendar Dashboard")
+        dashboard = ContentCalendarDashboard()
+        logger.info("Rendering Content Calendar Dashboard")
+        dashboard.render()
+        logger.info("Content Calendar Dashboard rendered successfully")
+    except Exception as e:
+        logger.error(f"Error rendering content calendar: {str(e)}", exc_info=True)
+        st.error(f"An error occurred while loading the content calendar: {str(e)}")
+
+def render_seo_tools_dashboard():
+    """Render a modern dashboard for SEO tools with improved UI and navigation."""
+    selected_section = st.session_state.get('seo_dashboard_section', 'combinations')
+
+    # Define card gradients at the top so it's available in all sections
+    card_gradients = {
+        "Content Optimization Suite": "linear-gradient(135deg, #43e97b 0%, #38f9d7 100%)",
+        "Technical SEO Audit": "linear-gradient(135deg, #667eea 0%, #764ba2 100%)",
+        "Image Optimization Suite": "linear-gradient(135deg, #f7971e 0%, #ffd200 100%)",
+        "Social Media Optimization": "linear-gradient(135deg, #f953c6 0%, #b91d73 100%)",
+        "Content Gap Analysis": "linear-gradient(135deg, #667eea 0%, #764ba2 100%)",
+        "Content Calendar": "linear-gradient(135deg, #4CAF50 0%, #2196F3 100%)",
+        "Structured Data Generator": "linear-gradient(135deg, #43e97b 0%, #38f9d7 100%)",
+        "Blog Title Generator": "linear-gradient(135deg, #2193b0 0%, #6dd5ed 100%)",
+        "Meta Description Generator": "linear-gradient(135deg, #f7971e 0%, #ffd200 100%)",
+        "Image Alt Text Generator": "linear-gradient(135deg, #f953c6 0%, #b91d73 100%)",
+        "OpenGraph Tags Generator": "linear-gradient(135deg, #f857a6 0%, #ff5858 100%)",
+        "Image Optimizer": "linear-gradient(135deg, #43cea2 0%, #185a9d 100%)",
+        "PageSpeed Insights": "linear-gradient(135deg, #ff9966 0%, #ff5e62 100%)",
+        "On-Page SEO Analyzer": "linear-gradient(135deg, #56ab2f 0%, #a8e063 100%)",
+        "URL SEO Checker": "linear-gradient(135deg, #3a7bd5 0%, #00d2ff 100%)",
+        "AI Backlinking Tool": "linear-gradient(135deg, #e96443 0%, #904e95 100%)"
+    }
+
+    # Navigation bar only (no dashboard title/description)
+    nav_cols = st.columns([1,1,1,1])
+    nav_labels = ["Tool Combos", "Advanced", "Individual", "About"]
+    nav_keys = ["combinations", "advanced", "individual", "about"]
+    for i, label in enumerate(nav_labels):
+        if nav_cols[i].button(label, key=f"nav_{label}"):
+            st.session_state['seo_dashboard_section'] = nav_keys[i]
+            selected_section = nav_keys[i]
+
+    st.markdown("<hr style='margin:1.5rem 0;'>", unsafe_allow_html=True)
+
+    # Define tool combinations for cross-tool analysis
+    tool_combinations = {
+        "Content Optimization Suite": {
+            "icon": "📊",
+            "description": "Comprehensive content optimization combining title generation, meta descriptions, and structured data.",
+            "tools": ["Blog Title Generator", "Meta Description Generator", "Structured Data Generator"],
+            "path": "content_optimization",
+            "color": "#4CAF50"
+        },
+        "Technical SEO Audit": {
+            "icon": "🔧",
+            "description": "Complete technical SEO analysis including page speed, on-page SEO, and URL structure.",
+            "tools": ["PageSpeed Insights", "On-Page SEO Analyzer", "URL SEO Checker"],
+            "path": "technical_audit",
+            "color": "#2196F3"
+        },
+        "Image Optimization Suite": {
+            "icon": "🖼️",
+            "description": "End-to-end image optimization with alt text generation and performance optimization.",
+            "tools": ["Image Alt Text Generator", "Image Optimizer"],
+            "path": "image_optimization",
+            "color": "#FF9800"
+        },
+        "Social Media Optimization": {
+            "icon": "📱",
+            "description": "Enhance social media presence with OpenGraph tags and backlink analysis.",
+            "tools": ["OpenGraph Tags Generator", "AI Backlinking Tool"],
+            "path": "social_optimization",
+            "color": "#9C27B0"
+        }
+    }
+
+    # Define individual SEO tools
+    seo_tools = {
+        "Structured Data Generator": {
+            "icon": "📋",
+            "description": "Generate structured data (Rich Snippets) to enhance your search results with additional information.",
+            "color": "#4CAF50",
+            "path": "structured_data",
+            "status": "active"
+        },
+        "Blog Title Generator": {
+            "icon": "✏️",
+            "description": "Create SEO-optimized blog titles that attract clicks and improve search rankings.",
+            "color": "#2196F3",
+            "path": "blog_title",
+            "status": "active"
+        },
+        "Meta Description Generator": {
+            "icon": "📝",
+            "description": "Generate compelling meta descriptions that improve click-through rates from search results.",
+            "color": "#FF9800",
+            "path": "meta_description",
+            "status": "active"
+        },
+        "Image Alt Text Generator": {
+            "icon": "🖼️",
+            "description": "Create descriptive alt text for images to improve accessibility and image SEO.",
+            "color": "#9C27B0",
+            "path": "alt_text",
+            "status": "active"
+        },
+        "OpenGraph Tags Generator": {
+            "icon": "📱",
+            "description": "Generate OpenGraph tags for better social media sharing and visibility.",
+            "color": "#F44336",
+            "path": "opengraph",
+            "status": "active"
+        },
+        "Image Optimizer": {
+            "icon": "📉",
+            "description": "Optimize and resize images for better website performance and SEO.",
+            "color": "#607D8B",
+            "path": "image_optimizer",
+            "status": "active"
+        },
+        "PageSpeed Insights": {
+            "icon": "⚡",
+            "description": "Analyze your website's performance using Google PageSpeed Insights.",
+            "color": "#795548",
+            "path": "pagespeed",
+            "status": "active"
+        },
+        "On-Page SEO Analyzer": {
+            "icon": "🔍",
+            "description": "Analyze and optimize your webpage's SEO elements and content.",
+            "color": "#009688",
+            "path": "onpage_seo",
+            "status": "active"
+        },
+        "URL SEO Checker": {
+            "icon": "🌐",
+            "description": "Check the SEO health of specific URLs and get improvement suggestions.",
+            "color": "#3F51B5",
+            "path": "url_checker",
+            "status": "active"
+        },
+        "AI Backlinking Tool": {
+            "icon": "🔗",
+            "description": "Discover and analyze backlink opportunities using AI-powered insights.",
+            "color": "#E91E63",
+            "path": "backlinking",
+            "status": "active"
+        }
+    }
+
+    # --- Tool Combinations Section ---
+    if selected_section == 'combinations':
+        combo_cols = st.columns(2)
+        for idx, (combo_name, details) in enumerate(tool_combinations.items()):
+            gradient = card_gradients.get(combo_name, "linear-gradient(135deg, #f5f7fa 0%, #c3cfe2 100%)")
+            with combo_cols[idx % 2]:
+                st.markdown(f"""
+                    <div class="seo-card" style="background: {gradient}; position: relative; overflow: hidden;">
+                        <div class="seo-card-overlay"></div>
+                        <div class="seo-icon">{details['icon']}</div>
+                        <div class="seo-title">{combo_name}</div>
+                        <div class="seo-description">{details['description']}</div>
+                        <div>
+                            {''.join([f'<span class="tool-badge">{tool}</span>' for tool in details['tools']])}
+                        </div>
+                    </div>
+                """, unsafe_allow_html=True)
+                if st.button(f"Launch {combo_name}", key=f"combo_{combo_name}", use_container_width=True):
+                    st.query_params["tool"] = details["path"]
+                    st.rerun()
+
+    # --- Advanced Features Section ---
+    elif selected_section == 'advanced':
+        adv_cols = st.columns(2)
+        adv_features = [
+            {
+                "name": "Content Gap Analysis",
+                "icon": "🎯",
+                "description": "Identify content opportunities and optimize your content strategy with AI-powered insights.",
+                "badges": ["Website Analysis", "Competitor Research", "Keyword Opportunities", "AI Recommendations"],
+                "gradient": card_gradients["Content Gap Analysis"],
+                "button": "Start Content Gap Analysis",
+                "key": "content_gap_analysis",
+                "path": "content_gap_analysis"
+            },
+            {
+                "name": "Content Calendar",
+                "icon": "📅",
+                "description": "Plan, schedule, and manage your content strategy with our AI-powered content calendar.",
+                "badges": ["Content Planning", "Scheduling", "Performance Tracking", "AI Insights"],
+                "gradient": card_gradients["Content Calendar"],
+                "button": "Open Content Calendar",
+                "key": "content_calendar",
+                "path": "content_calendar"
+            }
+        ]
+        for idx, feature in enumerate(adv_features):
+            with adv_cols[idx % 2]:
+                st.markdown(f"""
+                    <div class="seo-card" style="background: {feature['gradient']}; position: relative; overflow: hidden;">
+                        <div class="seo-card-overlay"></div>
+                        <div class="seo-icon">{feature['icon']}</div>
+                        <div class="seo-title">{feature['name']}</div>
+                        <div class="seo-description">{feature['description']}</div>
+                        <div>
+                            {''.join([f'<span class="tool-badge">{badge}</span>' for badge in feature['badges']])}
+                        </div>
+                    </div>
+                """, unsafe_allow_html=True)
+                if st.button(feature['button'], key=feature['key'], use_container_width=True):
+                    st.query_params["tool"] = feature["path"]
+                    st.rerun()
+
+    # --- Individual Tools Section ---
+    elif selected_section == 'individual':
+        cols = st.columns(3)
+        for idx, (tool_name, details) in enumerate(seo_tools.items()):
+            gradient = card_gradients.get(tool_name, "linear-gradient(135deg, #f5f7fa 0%, #c3cfe2 100%)")
+            with cols[idx % 3]:
+                st.markdown(f"""
+                    <div class="seo-card" style="background: {gradient}; position: relative; overflow: hidden;">
+                        <div class="seo-card-overlay"></div>
+                        <div class="seo-icon">{details['icon']}</div>
+                        <div class="seo-title">{tool_name}</div>
+                        <div class="seo-description">{details['description']}</div>
+                    </div>
+                """, unsafe_allow_html=True)
+                if st.button(f"Use {tool_name}", key=f"btn_{tool_name}", use_container_width=True):
+                    st.query_params["tool"] = details["path"]
+                    st.rerun()
+
+    # --- About Section ---
+    elif selected_section == 'about':
+        st.markdown("""
+            <div style='text-align: center; margin: 2rem 0;'>
+                <h2>About This Dashboard</h2>
+                <p style='color: #666;'>This dashboard brings together powerful AI-driven SEO tools and workflows to help you optimize your website and content strategy. Use the navigation above to explore combinations, advanced features, or individual tools.</p>
+            </div>
+        """, unsafe_allow_html=True)
+
+    st.markdown("""
+        <style>
+            .seo-card {
+                border-radius: 14px;
+                padding: 24px;
+                margin-bottom: 24px;
+                box-shadow: 0 4px 16px rgba(44, 62, 80, 0.10), 0 1.5px 4px rgba(44,62,80,0.06);
+                transition: transform 0.2s cubic-bezier(.4,2,.6,1), box-shadow 0.2s;
+                height: 100%;
+                border: 1.5px solid #e3e8ee;
+                position: relative;
+                overflow: hidden;
+                display: flex;
+                flex-direction: column;
+                align-items: center;
+                justify-content: center;
+            }
+            .seo-card-overlay {
+                position: absolute;
+                top: 0; left: 0; right: 0; bottom: 0;
+                background: rgba(255,255,255,0.72);
+                z-index: 1;
+                pointer-events: none;
+                border-radius: 14px;
+                box-shadow: 0 2px 8px rgba(44,62,80,0.08);
+            }
+            .seo-card:hover {
+                transform: translateY(-6px) scale(1.025);
+                box-shadow: 0 8px 32px rgba(44, 62, 80, 0.18), 0 2px 8px rgba(44,62,80,0.10);
+                border-color: #4CAF50;
+            }
+            .seo-icon {
+                font-size: 2.7rem;
+                margin-bottom: 18px;
+                z-index: 2;
+                position: relative;
+                text-shadow: 0 2px 8px rgba(44,62,80,0.10);
+            }
+            .seo-title {
+                font-size: 1.25rem;
+                font-weight: 800;
+                margin-bottom: 12px;
+                color: #222b45;
+                z-index: 2;
+                position: relative;
+                text-shadow: 0 2px 8px rgba(44,62,80,0.10);
+                letter-spacing: 0.01em;
+            }
+            .seo-description {
+                color: #34495e;
+                font-size: 1.08rem;
+                margin-bottom: 15px;
+                z-index: 2;
+                position: relative;
+                text-align: center;
+                line-height: 1.5;
+                text-shadow: 0 1px 4px rgba(44,62,80,0.08);
+            }
+            .tool-badge {
+                display: inline-block;
+                padding: 4px 12px;
+                border-radius: 12px;
+                font-size: 0.9rem;
+                margin-right: 8px;
+                margin-bottom: 8px;
+                background: rgba(255, 255, 255, 0.95);
+                color: #2196F3;
+                font-weight: 600;
+                border: 1px solid #e3e8ee;
+            }
+        </style>
+    """, unsafe_allow_html=True)
 
 def ai_seo_tools():
     """
@@ -17,71 +357,83 @@ def ai_seo_tools():
     such as generating structured data, optimizing images, checking page speed, 
     and analyzing on-page SEO.
     """
-    st.markdown(
+    # Check if a specific tool is selected
-        """
+    selected_tool = st.query_params.get("tool")
-        Welcome to your one-stop solution for AI-driven SEO optimization. Select a tool from the options below 
-        to improve your website’s SEO with cutting-edge AI technology.
-        """
-    ) 
-    # List of SEO tools with unique emojis for each option
-    options = [
-        "📝 Generate Structured Data - Rich Snippet",
-        "✏️ Generate SEO Optimized Blog Titles",
-        "📝 Generate Meta Description for SEO",
-        "🖼️ Generate Image Alt Text",
-        "📄 Generate OpenGraph Tags",
-        "📉 Optimize/Resize Image",
-        "⚡ Run Google PageSpeed Insights",
-        "🔍 Analyze On-Page SEO",
-        "🌐 URL SEO Checker",
-        "🔗 AI Backlinking Tool"
-    ]
     
-    # User selection of SEO tools using radio buttons
+    if selected_tool:
-    choice = st.radio(
+        # Map tool paths to their respective functions
-        "**👇 Select an AI SEO Tool:**", 
+        tool_functions = {
-        options, 
+            # Individual tools
-        index=0, 
+            "structured_data": ai_structured_data,
-        format_func=lambda x: x
+            "blog_title": ai_title_generator,
-    )
+            "meta_description": metadesc_generator_main,
+            "alt_text": alt_text_gen,
+            "opengraph": og_tag_generator,
+            "image_optimizer": main_img_optimizer,
+            "pagespeed": google_pagespeed_insights,
+            "onpage_seo": analyze_onpage_seo,
+            "url_checker": url_seo_checker,
+            "backlinking": backlinking_ui,
+            
+            # Tool combinations
+            "content_optimization": lambda: run_tool_combination([
+                ai_title_generator,
+                metadesc_generator_main,
+                ai_structured_data
+            ], "Content Optimization Suite"),
+            "technical_audit": lambda: run_tool_combination([
+                google_pagespeed_insights,
+                analyze_onpage_seo,
+                url_seo_checker
+            ], "Technical SEO Audit"),
+            "image_optimization": lambda: run_tool_combination([
+                alt_text_gen,
+                main_img_optimizer
+            ], "Image Optimization Suite"),
+            "social_optimization": lambda: run_tool_combination([
+                og_tag_generator,
+                backlinking_ui
+            ], "Social Media Optimization"),
+            
+            # Add Content Gap Analysis and Content Calendar
+            "content_gap_analysis": render_content_gap_analysis,
+            "content_calendar": render_content_calendar
+        }
+        
+        if selected_tool in tool_functions:
+            # Clear any existing content
+            st.empty()
+            # Execute the selected tool's function
+            tool_functions[selected_tool]()
+        else:
+            st.error(f"Invalid tool selected: {selected_tool}")
+            render_seo_tools_dashboard()
+    else:
+        # Show the dashboard if no tool is selected
+        render_seo_tools_dashboard()
+
+def run_tool_combination(tools, combination_name):
+    """Run a combination of tools and provide cross-tool analysis."""
+    st.markdown(f"# {combination_name}")
+    st.markdown("Running comprehensive analysis...")
     
-    # Call the respective functions based on the user selection
+    # Create tabs for each tool in the combination
-    if choice == "📝 Generate Structured Data - Rich Snippet":
+    tabs = st.tabs([f"Step {i+1}" for i in range(len(tools))])
-        # Generate Structured Data for Rich Snippets
+    
-        ai_structured_data()
+    # Run each tool in its own tab
-
+    for i, (tab, tool) in enumerate(zip(tabs, tools)):
-    elif choice == "📝 Generate Meta Description for SEO":
+        with tab:
-        # Generate SEO-optimized meta descriptions
+            st.markdown(f"### Step {i+1}")
-        metadesc_generator_main()
+            tool()
-
+    
-    elif choice == "✏️ Generate SEO Optimized Blog Titles":
+    # Add cross-tool analysis section
-        # Generate SEO-friendly blog titles
+    st.markdown("## 📊 Cross-Tool Analysis")
-        ai_title_generator()
+    st.markdown("Analyzing results across all tools...")
-
+    
-    elif choice == "🖼️ Generate Image Alt Text":
+    # Add recommendations based on combined results
-        # Generate alternative text for images
+    st.markdown("## 💡 Recommendations")
-        alt_text_gen()
+    st.markdown("Based on the combined analysis, here are the key recommendations:")
-
+    
-    elif choice == "📄 Generate OpenGraph Tags":
+    # Add a button to export the complete analysis
-        # Generate OpenGraph tags for social media sharing
+    if st.button("📥 Export Complete Analysis", use_container_width=True):
-        og_tag_generator()
+        st.info("Analysis export functionality coming soon!")
-
-    elif choice == "📉 Optimize/Resize Image":
-        # Optimize images by resizing or compressing them
-        main_img_optimizer()
-
-    elif choice == "⚡ Run Google PageSpeed Insights":
-        # Run Google PageSpeed Insights for performance analysis
-        google_pagespeed_insights()
-
-    elif choice == "🔍 Analyze On-Page SEO":
-        # Analyze on-page SEO elements
-        analyze_onpage_seo()
-
-    elif choice == "🌐 URL SEO Checker":
-        # Check SEO health of a specific URL
-        url_seo_checker()
-
-    elif choice == "🔗 AI Backlinking Tool":
-        # Run AI Backlinking tool for link-building opportunities
-        backlinking_ui()

lib/utils/ui_setup.py

@@ -1,3 +1,8 @@
+"""
+UI setup module for ALwrity application.
+Provides consistent navigation and layout structure.
+"""
+
 import os
 import streamlit as st
 from lib.utils.file_processor import load_image
@@ -15,6 +20,99 @@ from lib.ai_writers.insta_ai_writer import insta_writer
 from lib.ai_writers.youtube_writers.youtube_ai_writer import youtube_main_menu
 from lib.ai_writers.ai_writer_dashboard import get_ai_writers, list_ai_writers
 
+def render_social_tools_dashboard():
+    """Render a modern dashboard for social media tools."""
+    st.markdown("""
+        <style>
+            .social-card {
+                background: white;
+                border-radius: 10px;
+                padding: 20px;
+                box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
+                transition: transform 0.3s ease;
+                height: 100%;
+            }
+            .social-card:hover {
+                transform: translateY(-5px);
+            }
+            .social-icon {
+                font-size: 2.5rem;
+                margin-bottom: 15px;
+            }
+            .social-title {
+                font-size: 1.2rem;
+                font-weight: 600;
+                margin-bottom: 10px;
+            }
+            .social-description {
+                color: #666;
+                font-size: 0.9rem;
+                margin-bottom: 15px;
+            }
+            .social-button {
+                width: 100%;
+                padding: 8px 16px;
+                border-radius: 5px;
+                border: none;
+                font-weight: 500;
+                cursor: pointer;
+                transition: all 0.3s ease;
+            }
+        </style>
+    """, unsafe_allow_html=True)
+
+    # Define social tools with their details and paths
+    social_tools = {
+        "Facebook": {
+            "icon": "📘",
+            "description": "Create engaging Facebook posts and manage your content strategy",
+            "color": "#4267B2",
+            "path": "facebook"
+        },
+        "LinkedIn": {
+            "icon": "💼",
+            "description": "Generate professional LinkedIn content and optimize your profile",
+            "color": "#0077B5",
+            "path": "linkedin"
+        },
+        "Twitter": {
+            "icon": "🐦",
+            "description": "Craft viral tweets and manage your Twitter presence",
+            "color": "#1DA1F2",
+            "path": "twitter"
+        },
+        "Instagram": {
+            "icon": "📸",
+            "description": "Create Instagram captions and plan your visual content",
+            "color": "#E1306C",
+            "path": "instagram"
+        },
+        "YouTube": {
+            "icon": "🎥",
+            "description": "Generate video scripts and optimize your YouTube content",
+            "color": "#FF0000",
+            "path": "youtube"
+        }
+    }
+
+    # Create a grid of cards
+    cols = st.columns(3)
+    for idx, (platform, details) in enumerate(social_tools.items()):
+        with cols[idx % 3]:
+            st.markdown(f"""
+                <div class="social-card">
+                    <div class="social-icon">{details['icon']}</div>
+                    <div class="social-title">{platform}</div>
+                    <div class="social-description">{details['description']}</div>
+                </div>
+            """, unsafe_allow_html=True)
+            
+            if st.button(f"Open {platform}", key=f"btn_{platform}", 
+                        help=f"Launch {platform} tools",
+                        use_container_width=True):
+                # Set query parameters to redirect to the specific tool
+                st.query_params["tool"] = details["path"]
+                st.rerun()
 
 def setup_ui():
     """Set up the UI with custom styling."""
@@ -314,29 +412,18 @@ def setup_alwrity_ui():
         "AI Writers": ("📝", get_ai_writers),
         "Content Planning": ("📅", content_planning_tools),
         "AI SEO Tools": ("🔍", ai_seo_tools),
-        "AI Social Tools": ("📱", None),  # Set to None as we'll handle this separately
+        "AI Social Tools": ("📱", render_social_tools_dashboard),
+        "ALwrity Settings": ("⚙️", render_settings_page),
         "Agents Teams(TBD)": ("🤝", lambda: st.subheader("Agents Teams - Coming Soon!")),
         "Ask Alwrity(TBD)": ("💬", lambda: (
             st.subheader("Chat with your Data, Chat with any Data.. COMING SOON !"),
             st.markdown("Create a collection by uploading files (PDF, MD, CSV, etc), or crawl a data source (Websites, more sources coming soon."),
             st.markdown("One can ask/chat, summarize and do semantic search over the uploaded data")
-        )),
+        ))
-        "ALwrity Settings": ("⚙️", render_settings_page)
     }
     
     logger.info(f"Defined {len(nav_items)} navigation items")
 
-    # Define sub-menu items for AI Social Tools
-    social_tools_submenu = {
-        "Facebook": ("📘", lambda: facebook_main_menu()),
-        "LinkedIn": ("💼", lambda: linkedin_main_menu()),
-        "Twitter": ("🐦", lambda: run_dashboard()),
-        "Instagram": ("📸", lambda: insta_writer()),
-        "YouTube": ("🎥", lambda: youtube_main_menu())
-    }
-    
-    logger.info(f"Defined {len(social_tools_submenu)} social tools submenu items")
-
     # Create sidebar navigation
     st.sidebar.markdown("### ALwrity Options")
     st.sidebar.markdown('<div class="sidebar-nav">', unsafe_allow_html=True)
@@ -345,53 +432,12 @@ def setup_alwrity_ui():
     for name, (icon, func) in nav_items.items():
         button_class = "nav-button active" if st.session_state.active_tab == name else "nav-button"
         
-        if name == "AI Social Tools":
+        if st.sidebar.button(f"{icon} {name}", key=f"nav_{name}", 
-            # For AI Social Tools, we'll create a button that toggles the sub-menu
+                           help=f"Navigate to {name}", use_container_width=True):
-            if st.sidebar.button(f"{icon} {name}", key=f"nav_{name}", 
+            st.session_state.active_tab = name
-                               help=f"Navigate to {name}", use_container_width=True):
+            # Reset sub-tab when main tab changes
-                st.session_state.active_tab = name
+            st.session_state.active_sub_tab = None
-                # Reset sub-tab when main tab changes
+            logger.info(f"Selected main tab: {name}")
-                st.session_state.active_sub_tab = None
-                logger.info(f"Selected main tab: {name}")
-            
-            # If AI Social Tools is active, show the sub-menu
-            if st.session_state.active_tab == "AI Social Tools":
-                st.sidebar.markdown('<div class="sub-menu">', unsafe_allow_html=True)
-                
-                # Create sub-menu buttons
-                for sub_name, (sub_icon, sub_func) in social_tools_submenu.items():
-                    # Create the button with a custom key that includes the platform name
-                    button_key = f"sub_{sub_name}"
-                    
-                    # Determine if this button is active
-                    is_active = st.session_state.active_sub_tab == sub_name
-                    
-                    # Create a container with the platform-specific class
-                    platform_class = f"{sub_name.lower()}-button"
-                    if is_active:
-                        platform_class += " active"
-                    
-                    # Add the platform-specific class to the button container
-                    st.sidebar.markdown(f'<div class="{platform_class}">', unsafe_allow_html=True)
-                    
-                    # Create the button
-                    if st.sidebar.button(f"{sub_icon} {sub_name}", key=button_key, 
-                                       help=f"Navigate to {sub_name}", use_container_width=True):
-                        st.session_state.active_sub_tab = sub_name
-                        logger.info(f"Selected social tool: {sub_name}")
-                    
-                    # Close the div
-                    st.sidebar.markdown('</div>', unsafe_allow_html=True)
-                
-                st.sidebar.markdown('</div>', unsafe_allow_html=True)
-        else:
-            # For other navigation items, create regular buttons
-            if st.sidebar.button(f"{icon} {name}", key=f"nav_{name}", 
-                               help=f"Navigate to {name}", use_container_width=True):
-                st.session_state.active_tab = name
-                # Reset sub-tab when main tab changes
-                st.session_state.active_sub_tab = None
-                logger.info(f"Selected main tab: {name}")
 
     st.sidebar.markdown('</div>', unsafe_allow_html=True)
 
@@ -402,10 +448,36 @@ def setup_alwrity_ui():
         st.sidebar.image(icon_path, use_container_width=False)
     st.sidebar.markdown('</div>', unsafe_allow_html=True)
 
-    # Display content based on active tab
+    # Display content based on active tab and tool selection
     if st.session_state.active_tab == "AI Social Tools":
-        if not st.session_state.active_sub_tab:
+        # Check if a specific tool is selected
-            # Only show title and info when no sub-tab is selected
+        selected_tool = st.query_params.get("tool")
+        if selected_tool:
+            # Add a back button at the top
+            if st.button("← Back to Social Tools Dashboard", key=f"back_to_dashboard_{selected_tool}"):
+                # Clear the tool query parameter
+                st.query_params.clear()
+                st.rerun()
+            
+            # Map tool paths to their respective functions
+            tool_functions = {
+                "facebook": facebook_main_menu,
+                "linkedin": linkedin_main_menu,
+                "twitter": run_dashboard,
+                "instagram": insta_writer,
+                "youtube": youtube_main_menu
+            }
+            
+            if selected_tool in tool_functions:
+                # Clear any existing content
+                st.empty()
+                # Execute the selected tool's function
+                tool_functions[selected_tool]()
+            else:
+                st.error(f"Invalid tool selected: {selected_tool}")
+                render_social_tools_dashboard()
+        else:
+            # Show the dashboard if no tool is selected
             st.markdown("""
                 <style>
                     .main .block-container {
@@ -414,40 +486,14 @@ def setup_alwrity_ui():
                 </style>
             """, unsafe_allow_html=True)
             st.title(f"{nav_items[st.session_state.active_tab][0]} {st.session_state.active_tab}")
-            st.info("Please select a social media platform from the sidebar.")
+            render_social_tools_dashboard()
-        else:
-            # When a platform is selected, show no title and minimize spacing
-            st.markdown("""
-                <style>
-                    .main .block-container {
-                        padding-top: 0 !important;
-                        padding-bottom: 0;
-                    }
-                    
-                    /* Remove all margins and padding from content area */
-                    .element-container {
-                        margin: 0 !important;
-                        padding: 0 !important;
-                    }
-                    
-                    /* Hide any automatic headers */
-                    .main .block-container > div:first-child {
-                        margin-top: 0 !important;
-                        padding-top: 0 !important;
-                    }
-                </style>
-            """, unsafe_allow_html=True)
-            # Call the function directly without any title
-            social_tools_submenu[st.session_state.active_sub_tab][1]()
     else:
-        # Check if we're in the AI Writers section and handle writer selection
+        # Handle other tabs as before
         if st.session_state.active_tab == "AI Writers":
-            # Get the writer parameter from the URL using st.query_params
             writer = st.query_params.get("writer")
             logger.info(f"Current writer from query params: {writer}")
             
             if writer:
-                # Get the list of writers without rendering the dashboard
                 writers = list_ai_writers()
                 logger.info(f"Found {len(writers)} writers")
                 
@@ -457,9 +503,7 @@ def setup_alwrity_ui():
                     if w["path"] == writer:
                         writer_found = True
                         logger.info(f"Found matching writer: {w['name']}, executing function")
-                        # Clear any existing content
                         st.empty()
-                        # Execute the writer function
                         w["function"]()
                         break
                 
@@ -467,11 +511,9 @@ def setup_alwrity_ui():
                     logger.error(f"No writer found with path: {writer}")
                     st.error(f"No writer found with path: {writer}")
             else:
-                # If no writer selected, show the dashboard
                 logger.info("No writer selected, showing dashboard")
                 get_ai_writers()
         else:
-            # For all other tabs, show the title
             st.markdown("""
                 <style>
                     .main .block-container {

lib/utils/website_analyzer/__init__.py

@@ -1,7 +1,6 @@
 """Website analyzer module for AI-powered website analysis."""
 
-from .analyzer import analyze_website
+from .analyzer import analyze_website, WebsiteAnalyzer
-from .seo_analyzer import analyze_seo
 from .models import SEOAnalysisResult
 
-__all__ = ['analyze_seo', 'SEOAnalysisResult', 'analyze_website'] 
+__all__ = ['analyze_website', 'WebsiteAnalyzer', 'SEOAnalysisResult'] 

lib/utils/website_analyzer/analyzer.py

@@ -1,7 +1,7 @@
-"""Website scraping and AI analysis module."""
+"""Website and SEO analysis module."""
 
 import asyncio
-from typing import Dict, List, Optional
+from typing import Dict, List, Optional, Tuple
 from bs4 import BeautifulSoup
 from urllib.parse import urljoin, urlparse
 import streamlit as st
@@ -21,51 +21,29 @@ import whois
 import dns.resolver
 from requests.exceptions import RequestException
 from concurrent.futures import ThreadPoolExecutor
+from .models import (
+    SEOAnalysisResult,
+    MetaTagAnalysis,
+    ContentAnalysis,
+    SEORecommendation
+)
 
 # Configure logging
 logging.basicConfig(
-    level=logging.DEBUG,
+    level=logging.INFO,
     format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
     handlers=[
         logging.StreamHandler(),
         logging.FileHandler('logs/website_analyzer.log')
     ]
 )
+
+# Create a logger for the website analyzer
 logger = logging.getLogger(__name__)
 
-def analyze_website(url: str) -> Dict:
+# Create a separate logger for scraping operations
-    """
+scraping_logger = logging.getLogger('website_analyzer.scraping')
-    Analyze a website and return comprehensive results.
+scraping_logger.setLevel(logging.WARNING)
-    
-    Args:
-        url (str): The URL to analyze
-        
-    Returns:
-        Dict: Analysis results including various metrics and checks
-    """
-    logger.info(f"Starting website analysis for URL: {url}")
-    try:
-        analyzer = WebsiteAnalyzer()
-        results = analyzer.analyze_website(url)
-        
-        # Add success status to results
-        if "error" in results:
-            return {
-                "success": False,
-                "error": results["error"]
-            }
-        
-        # Add success status and wrap results
-        return {
-            "success": True,
-            "data": results
-        }
-    except Exception as e:
-        logger.error(f"Error in analyze_website: {str(e)}", exc_info=True)
-        return {
-            "success": False,
-            "error": str(e)
-        }
 
 class WebsiteAnalyzer:
     def __init__(self):
@@ -89,13 +67,17 @@ class WebsiteAnalyzer:
         try:
             # Validate URL
             if not self._validate_url(url):
-                logger.error(f"Invalid URL format: {url}")
+                error_msg = f"Invalid URL format: {url}"
-                return {"error": "Invalid URL format"}
+                logger.error(error_msg)
+                return {
+                    "success": False,
+                    "error": error_msg,
+                    "error_details": {"stage": "url_validation"}
+                }
 
             # Basic URL parsing
             parsed_url = urlparse(url)
             domain = parsed_url.netloc
-            logger.debug(f"Parsed domain: {domain}")
 
             # Initialize results dictionary
             results = {
@@ -107,36 +89,105 @@ class WebsiteAnalyzer:
 
             # Perform various analyses
             with ThreadPoolExecutor(max_workers=4) as executor:
+                logger.info("Starting parallel analysis tasks")
+                
                 # Basic website info
+                logger.info("Starting basic info analysis")
                 basic_info = executor.submit(self._get_basic_info, url).result()
+                if "error" in basic_info:
+                    error_msg = f"Basic info analysis failed: {basic_info['error']}"
+                    logger.error(error_msg)
+                    return {
+                        "success": False,
+                        "error": error_msg,
+                        "error_details": {
+                            "stage": "basic_info",
+                            "details": basic_info.get("error_details", {})
+                        }
+                    }
                 results["analysis"]["basic_info"] = basic_info
 
                 # SSL/TLS info
+                logger.info("Starting SSL analysis")
                 ssl_info = executor.submit(self._check_ssl, domain).result()
                 results["analysis"]["ssl_info"] = ssl_info
 
                 # DNS info
+                logger.info("Starting DNS analysis")
                 dns_info = executor.submit(self._check_dns, domain).result()
                 results["analysis"]["dns_info"] = dns_info
 
                 # WHOIS info
+                logger.info("Starting WHOIS analysis")
                 whois_info = executor.submit(self._get_whois_info, domain).result()
                 results["analysis"]["whois_info"] = whois_info
 
                 # Content analysis
+                logger.info("Starting content analysis")
                 content_info = executor.submit(self._analyze_content, url).result()
+                if "error" in content_info:
+                    error_msg = f"Content analysis failed: {content_info['error']}"
+                    logger.error(error_msg)
+                    return {
+                        "success": False,
+                        "error": error_msg,
+                        "error_details": {
+                            "stage": "content_analysis",
+                            "details": content_info.get("error_details", {})
+                        }
+                    }
                 results["analysis"]["content_info"] = content_info
 
                 # Performance metrics
+                logger.info("Starting performance analysis")
                 performance = executor.submit(self._check_performance, url).result()
+                if "error" in performance:
+                    error_msg = f"Performance analysis failed: {performance['error']}"
+                    logger.error(error_msg)
+                    return {
+                        "success": False,
+                        "error": error_msg,
+                        "error_details": {
+                            "stage": "performance_analysis",
+                            "details": performance.get("error_details", {})
+                        }
+                    }
                 results["analysis"]["performance"] = performance
 
+                # SEO analysis
+                logger.info("Starting SEO analysis")
+                seo_analysis = executor.submit(self._analyze_seo, url).result()
+                if "error" in seo_analysis:
+                    error_msg = f"SEO analysis failed: {seo_analysis['error']}"
+                    logger.error(error_msg)
+                    return {
+                        "success": False,
+                        "error": error_msg,
+                        "error_details": {
+                            "stage": "seo_analysis",
+                            "details": seo_analysis.get("error_details", {})
+                        }
+                    }
+                results["analysis"]["seo_info"] = seo_analysis
+
             logger.info(f"Analysis completed successfully for {url}")
-            return results
+            logger.debug(f"Final results: {json.dumps(results, indent=2)}")
+            return {
+                "success": True,
+                "data": results
+            }
 
         except Exception as e:
-            logger.error(f"Error during website analysis: {str(e)}", exc_info=True)
+            error_msg = f"Error during website analysis: {str(e)}"
-            return {"error": str(e)}
+            logger.error(error_msg, exc_info=True)
+            return {
+                "success": False,
+                "error": error_msg,
+                "error_details": {
+                    "type": type(e).__name__,
+                    "traceback": str(e.__traceback__)
+                }
+            }
 
     def _validate_url(self, url: str) -> bool:
         """Validate URL format."""
@@ -149,7 +200,7 @@ class WebsiteAnalyzer:
 
     def _get_basic_info(self, url: str) -> Dict:
         """Get basic website information."""
-        logger.debug(f"Getting basic info for {url}")
+        scraping_logger.debug(f"Getting basic info for {url}")
         try:
             response = self.session.get(url, timeout=10)
             response.raise_for_status()
@@ -165,13 +216,31 @@ class WebsiteAnalyzer:
                 "robots_txt": self._get_robots_txt(url),
                 "sitemap": self._get_sitemap(url)
             }
+        except requests.exceptions.RequestException as e:
+            error_msg = f"Request error in basic info: {str(e)}"
+            logger.error(error_msg, exc_info=True)
+            return {
+                "error": error_msg,
+                "error_details": {
+                    "type": "RequestException",
+                    "status_code": getattr(e.response, 'status_code', None) if hasattr(e, 'response') else None,
+                    "url": url
+                }
+            }
         except Exception as e:
-            logger.error(f"Error getting basic info: {str(e)}", exc_info=True)
+            error_msg = f"Error getting basic info: {str(e)}"
-            return {"error": str(e)}
+            logger.error(error_msg, exc_info=True)
+            return {
+                "error": error_msg,
+                "error_details": {
+                    "type": type(e).__name__,
+                    "traceback": str(e.__traceback__)
+                }
+            }
 
     def _check_ssl(self, domain: str) -> Dict:
         """Check SSL/TLS certificate information."""
-        logger.debug(f"Checking SSL for {domain}")
+        scraping_logger.debug(f"Checking SSL for {domain}")
         try:
             context = ssl.create_default_context()
             with socket.create_connection((domain, 443)) as sock:
@@ -190,7 +259,7 @@ class WebsiteAnalyzer:
 
     def _check_dns(self, domain: str) -> Dict:
         """Check DNS records."""
-        logger.debug(f"Checking DNS for {domain}")
+        scraping_logger.debug(f"Checking DNS for {domain}")
         try:
             records = {}
             for record_type in ['A', 'AAAA', 'MX', 'NS', 'TXT']:
@@ -200,7 +269,7 @@ class WebsiteAnalyzer:
                 except dns.resolver.NoAnswer:
                     records[record_type] = []
                 except Exception as e:
-                    logger.warning(f"Error resolving {record_type} record: {str(e)}")
+                    scraping_logger.warning(f"Error resolving {record_type} record: {str(e)}")
                     records[record_type] = []
             return records
         except Exception as e:
@@ -209,6 +278,7 @@ class WebsiteAnalyzer:
 
     def _get_whois_info(self, domain: str) -> Dict:
         """Get WHOIS information for a domain."""
+        scraping_logger.debug(f"Getting WHOIS info for {domain}")
         try:
             w = whois.whois(domain)
             
@@ -240,7 +310,7 @@ class WebsiteAnalyzer:
 
     def _analyze_content(self, url: str) -> Dict:
         """Analyze website content."""
-        logger.debug(f"Analyzing content for {url}")
+        scraping_logger.debug(f"Analyzing content for {url}")
         try:
             response = self.session.get(url, timeout=10)
             response.raise_for_status()
@@ -255,6 +325,14 @@ class WebsiteAnalyzer:
             
             # Count headings
             headings = soup.find_all(['h1', 'h2', 'h3', 'h4', 'h5', 'h6'])
+            heading_counts = {
+                'h1': len(soup.find_all('h1')),
+                'h2': len(soup.find_all('h2')),
+                'h3': len(soup.find_all('h3')),
+                'h4': len(soup.find_all('h4')),
+                'h5': len(soup.find_all('h5')),
+                'h6': len(soup.find_all('h6'))
+            }
             
             # Count images
             images = soup.find_all('img')
@@ -262,22 +340,52 @@ class WebsiteAnalyzer:
             # Count links
             links = soup.find_all('a')
             
+            # Count paragraphs
+            paragraphs = soup.find_all('p')
+            
             return {
                 "word_count": word_count,
                 "heading_count": len(headings),
+                "heading_structure": heading_counts,
                 "image_count": len(images),
                 "link_count": len(links),
+                "paragraph_count": len(paragraphs),
                 "has_meta_description": bool(self._get_meta_description(soup)),
                 "has_robots_txt": bool(self._get_robots_txt(url)),
                 "has_sitemap": bool(self._get_sitemap(url))
             }
+        except requests.exceptions.RequestException as e:
+            logger.error(f"Request error in content analysis: {str(e)}", exc_info=True)
+            return {
+                "word_count": 0,
+                "heading_count": 0,
+                "heading_structure": {'h1': 0, 'h2': 0, 'h3': 0, 'h4': 0, 'h5': 0, 'h6': 0},
+                "image_count": 0,
+                "link_count": 0,
+                "paragraph_count": 0,
+                "has_meta_description": False,
+                "has_robots_txt": False,
+                "has_sitemap": False,
+                "error": str(e)
+            }
         except Exception as e:
             logger.error(f"Content analysis error: {str(e)}", exc_info=True)
-            return {"error": str(e)}
+            return {
+                "word_count": 0,
+                "heading_count": 0,
+                "heading_structure": {'h1': 0, 'h2': 0, 'h3': 0, 'h4': 0, 'h5': 0, 'h6': 0},
+                "image_count": 0,
+                "link_count": 0,
+                "paragraph_count": 0,
+                "has_meta_description": False,
+                "has_robots_txt": False,
+                "has_sitemap": False,
+                "error": str(e)
+            }
 
     def _check_performance(self, url: str) -> Dict:
         """Check website performance metrics."""
-        logger.debug(f"Checking performance for {url}")
+        scraping_logger.debug(f"Checking performance for {url}")
         try:
             start_time = datetime.now()
             response = self.session.get(url, timeout=10)
@@ -289,11 +397,29 @@ class WebsiteAnalyzer:
                 "load_time": load_time,
                 "status_code": response.status_code,
                 "content_length": len(response.content),
-                "headers": dict(response.headers)
+                "headers": dict(response.headers),
+                "response_time": response.elapsed.total_seconds()
+            }
+        except requests.exceptions.RequestException as e:
+            logger.error(f"Request error in performance check: {str(e)}", exc_info=True)
+            return {
+                "load_time": 0,
+                "status_code": 0,
+                "content_length": 0,
+                "headers": {},
+                "response_time": 0,
+                "error": str(e)
             }
         except Exception as e:
             logger.error(f"Performance check error: {str(e)}", exc_info=True)
-            return {"error": str(e)}
+            return {
+                "load_time": 0,
+                "status_code": 0,
+                "content_length": 0,
+                "headers": {},
+                "response_time": 0,
+                "error": str(e)
+            }
 
     def _get_meta_description(self, soup: BeautifulSoup) -> Optional[str]:
         """Extract meta description from HTML."""
@@ -308,7 +434,7 @@ class WebsiteAnalyzer:
             if response.status_code == 200:
                 return response.text
         except Exception as e:
-            logger.warning(f"Error fetching robots.txt: {str(e)}")
+            scraping_logger.warning(f"Error fetching robots.txt: {str(e)}")
         return None
 
     def _get_sitemap(self, url: str) -> Optional[str]:
@@ -319,5 +445,253 @@ class WebsiteAnalyzer:
             if response.status_code == 200:
                 return response.text
         except Exception as e:
-            logger.warning(f"Error fetching sitemap.xml: {str(e)}")
+            scraping_logger.warning(f"Error fetching sitemap.xml: {str(e)}")
-        return None
+        return None
+
+    def _analyze_seo(self, url: str) -> Dict:
+        """Analyze website SEO."""
+        try:
+            # Extract content
+            content, soup, extract_errors = self._extract_content(url)
+            if not content or not soup:
+                return {
+                    "error": "Failed to extract content",
+                    "error_details": {"errors": extract_errors}
+                }
+
+            # Analyze meta tags
+            meta_analysis = self._analyze_meta_tags(soup)
+            
+            # Analyze content with AI
+            content_analysis, recommendations = self._analyze_content_with_ai(content)
+            
+            # Calculate overall score
+            meta_score = sum([
+                1 if meta_analysis.title['status'] == 'good' else 0,
+                1 if meta_analysis.description['status'] == 'good' else 0,
+                1 if meta_analysis.keywords['status'] == 'good' else 0,
+                1 if meta_analysis.has_robots else 0,
+                1 if meta_analysis.has_sitemap else 0
+            ]) * 20  # Scale to 100
+
+            overall_score = (
+                meta_score * 0.3 +  # 30% weight for meta tags
+                content_analysis.readability_score * 0.3 +  # 30% weight for readability
+                content_analysis.content_quality_score * 0.4  # 40% weight for content quality
+            )
+
+            return {
+                "overall_score": overall_score,
+                "meta_tags": meta_analysis.__dict__,
+                "content": content_analysis.__dict__,
+                "recommendations": [rec.__dict__ for rec in recommendations]
+            }
+
+        except Exception as e:
+            error_msg = f"Error in SEO analysis: {str(e)}"
+            logger.error(error_msg, exc_info=True)
+            return {
+                "error": error_msg,
+                "error_details": {
+                    "type": type(e).__name__,
+                    "traceback": str(e.__traceback__)
+                }
+            }
+
+    def _extract_content(self, url: str) -> Tuple[Optional[str], Optional[BeautifulSoup], List[str]]:
+        """Extract content from URL."""
+        errors = []
+        try:
+            response = self.session.get(url, timeout=10)
+            response.raise_for_status()
+            soup = BeautifulSoup(response.text, 'html.parser')
+            return response.text, soup, errors
+        except requests.RequestException as e:
+            error_msg = f"Error fetching URL: {str(e)}"
+            logger.error(error_msg)
+            errors.append(error_msg)
+            return None, None, errors
+
+    def _analyze_meta_tags(self, soup: BeautifulSoup) -> MetaTagAnalysis:
+        """Analyze meta tags using BeautifulSoup."""
+        # Title analysis
+        title = soup.title.string if soup.title else ""
+        title_analysis = {
+            'status': 'good' if title and 30 <= len(title) <= 60 else 'needs_improvement',
+            'value': title,
+            'recommendation': '' if title and 30 <= len(title) <= 60 else 'Title should be between 30-60 characters'
+        }
+
+        # Meta description analysis
+        meta_desc = soup.find('meta', attrs={'name': 'description'})
+        desc = meta_desc.get('content', '') if meta_desc else ""
+        desc_analysis = {
+            'status': 'good' if desc and 120 <= len(desc) <= 160 else 'needs_improvement',
+            'value': desc,
+            'recommendation': '' if desc and 120 <= len(desc) <= 160 else 'Description should be between 120-160 characters'
+        }
+
+        # Keywords analysis
+        meta_keywords = soup.find('meta', attrs={'name': 'keywords'})
+        keywords = meta_keywords.get('content', '') if meta_keywords else ""
+        keywords_analysis = {
+            'status': 'good' if keywords else 'needs_improvement',
+            'value': keywords,
+            'recommendation': '' if keywords else 'Add relevant keywords meta tag'
+        }
+
+        return MetaTagAnalysis(
+            title=title_analysis,
+            description=desc_analysis,
+            keywords=keywords_analysis,
+            has_robots=bool(soup.find('meta', attrs={'name': 'robots'})),
+            has_sitemap=bool(soup.find('link', attrs={'rel': 'sitemap'}))
+        )
+
+    def _analyze_content_with_ai(self, content: str) -> Tuple[ContentAnalysis, List[SEORecommendation]]:
+        """Analyze content using AI."""
+        try:
+            # Prepare prompt for content analysis
+            prompt = f"""Analyze the following webpage content for SEO and provide a structured analysis:
+            Content: {content[:4000]}...  # Truncate to avoid token limits
+            
+            Provide analysis in the following format:
+            1. Word count
+            2. Heading structure analysis
+            3. Keyword density for main topics
+            4. Readability score (0-100)
+            5. Content quality score (0-100)
+            6. List of SEO recommendations with priority (high/medium/low), category, issue, recommendation, and impact
+            
+            Format the response as JSON."""
+
+            try:
+                # Get AI analysis using llm_text_gen
+                analysis = llm_text_gen(
+                    prompt=prompt,
+                    system_prompt="You are an SEO expert analyzing website content.",
+                    response_format="json_object"
+                )
+                
+                if not analysis:
+                    logger.error("Empty response from AI analysis")
+                    return self._get_fallback_analysis(content)
+                
+                # Create ContentAnalysis object
+                content_analysis = ContentAnalysis(
+                    word_count=len(content.split()),
+                    headings_structure=analysis.get('heading_structure', {}),
+                    keyword_density=analysis.get('keyword_density', {}),
+                    readability_score=analysis.get('readability_score', 0),
+                    content_quality_score=analysis.get('content_quality_score', 0)
+                )
+
+                # Create recommendations
+                recommendations = [
+                    SEORecommendation(
+                        priority=rec['priority'],
+                        category=rec['category'],
+                        issue=rec['issue'],
+                        recommendation=rec['recommendation'],
+                        impact=rec['impact']
+                    )
+                    for rec in analysis.get('recommendations', [])
+                ]
+
+                return content_analysis, recommendations
+
+            except Exception as e:
+                logger.error(f"Error in AI analysis: {str(e)}")
+                return self._get_fallback_analysis(content)
+
+        except Exception as e:
+            logger.error(f"Error in AI analysis setup: {str(e)}")
+            return self._get_fallback_analysis(content)
+
+    def _get_fallback_analysis(self, content: str) -> Tuple[ContentAnalysis, List[SEORecommendation]]:
+        """Provide fallback analysis when AI analysis is not available."""
+        try:
+            # Basic content analysis
+            words = content.split()
+            word_count = len(words)
+            
+            # Simple readability score based on word count
+            readability_score = min(100, max(0, word_count / 10))
+            
+            # Basic content quality score
+            content_quality_score = min(100, max(0, word_count / 20))
+            
+            # Create basic recommendations
+            recommendations = [
+                SEORecommendation(
+                    priority="high",
+                    category="content",
+                    issue="AI analysis unavailable",
+                    recommendation="Consider running the analysis again with a valid API key for more detailed insights",
+                    impact="Limited analysis capabilities"
+                )
+            ]
+            
+            return ContentAnalysis(
+                word_count=word_count,
+                headings_structure={},
+                keyword_density={},
+                readability_score=readability_score,
+                content_quality_score=content_quality_score
+            ), recommendations
+            
+        except Exception as e:
+            logger.error(f"Error in fallback analysis: {str(e)}")
+            return ContentAnalysis(
+                word_count=0,
+                headings_structure={},
+                keyword_density={},
+                readability_score=0,
+                content_quality_score=0
+            ), []
+
+def analyze_website(url: str) -> Dict:
+    """
+    Analyze a website and return comprehensive results.
+    
+    Args:
+        url (str): The URL to analyze
+        
+    Returns:
+        Dict: Analysis results including various metrics and checks
+    """
+    logger.info(f"Starting website analysis for URL: {url}")
+    try:
+        analyzer = WebsiteAnalyzer()
+        
+        results = analyzer.analyze_website(url)
+        
+        # Add success status to results
+        if "error" in results:
+            error_msg = f"Error in base analysis: {results['error']}"
+            logger.error(error_msg)
+            logger.error(f"Error details: {json.dumps(results.get('error_details', {}), indent=2)}")
+            return {
+                "success": False,
+                "error": error_msg,
+                "error_details": results.get("error_details", {})
+            }
+        
+        # Add success status and wrap results
+        logger.info("Analysis completed successfully")
+        logger.debug(f"Analysis results: {json.dumps(results, indent=2)}")
+        return {
+            "success": True,
+            "data": results
+        }
+    except Exception as e:
+        error_msg = f"Error in analyze_website: {str(e)}"
+        logger.error(error_msg, exc_info=True)
+        return {
+            "success": False,
+            "error": error_msg,
+            "error_details": {
+                "type": type(e).__name__,
+                "traceback": str(e.__traceback__)
+            }
+        }

lib/utils/website_analyzer/content_gap_analyzer.py

@@ -0,0 +1,134 @@
+from typing import Dict
+import json
+
+class ContentGapAnalyzer:
+    def __init__(self, analyzer):
+        self.analyzer = analyzer
+
+    def analyze(self, url: str) -> Dict:
+        """
+        Analyze content gaps for a given URL.
+        
+        Args:
+            url (str): The URL to analyze
+            
+        Returns:
+            Dict: Analysis results including content gaps and recommendations
+        """
+        try:
+            # Get base analysis
+            logger.info(f"Starting content gap analysis for URL: {url}")
+            base_analysis = self.analyzer.analyze_website(url)
+            
+            # Check for errors in base analysis
+            if not base_analysis.get("success", False):
+                error_msg = base_analysis.get("error", "Unknown error in website analysis")
+                error_details = base_analysis.get("error_details", {})
+                logger.error(f"Base analysis failed: {error_msg}")
+                logger.error(f"Error details: {json.dumps(error_details, indent=2)}")
+                return {
+                    "success": False,
+                    "error": error_msg,
+                    "error_details": error_details,
+                    "stage": "base_analysis"
+                }
+            
+            # Extract required sections
+            analysis_data = base_analysis.get("data", {}).get("analysis", {})
+            required_sections = ["content_info", "basic_info", "performance"]
+            missing_sections = [section for section in required_sections if section not in analysis_data]
+            
+            if missing_sections:
+                error_msg = f"Missing required analysis sections: {', '.join(missing_sections)}"
+                logger.error(error_msg)
+                logger.error(f"Available sections: {list(analysis_data.keys())}")
+                return {
+                    "success": False,
+                    "error": error_msg,
+                    "error_details": {
+                        "missing_sections": missing_sections,
+                        "available_sections": list(analysis_data.keys())
+                    },
+                    "stage": "section_validation"
+                }
+            
+            # Extract content metrics
+            try:
+                content_info = analysis_data["content_info"]
+                basic_info = analysis_data["basic_info"]
+                performance = analysis_data["performance"]
+            except KeyError as e:
+                error_msg = f"Error extracting analysis section: {str(e)}"
+                logger.error(error_msg)
+                return {
+                    "success": False,
+                    "error": error_msg,
+                    "error_details": {
+                        "type": "KeyError",
+                        "missing_key": str(e),
+                        "available_keys": list(analysis_data.keys())
+                    },
+                    "stage": "data_extraction"
+                }
+            
+            # Analyze content gaps
+            try:
+                gaps = self._analyze_content_gaps(content_info, basic_info, performance)
+            except Exception as e:
+                error_msg = f"Error analyzing content gaps: {str(e)}"
+                logger.error(error_msg, exc_info=True)
+                return {
+                    "success": False,
+                    "error": error_msg,
+                    "error_details": {
+                        "type": type(e).__name__,
+                        "traceback": str(e.__traceback__)
+                    },
+                    "stage": "gap_analysis"
+                }
+            
+            # Generate recommendations
+            try:
+                recommendations = self._generate_recommendations(gaps)
+            except Exception as e:
+                error_msg = f"Error generating recommendations: {str(e)}"
+                logger.error(error_msg, exc_info=True)
+                return {
+                    "success": False,
+                    "error": error_msg,
+                    "error_details": {
+                        "type": type(e).__name__,
+                        "traceback": str(e.__traceback__)
+                    },
+                    "stage": "recommendation_generation"
+                }
+            
+            return {
+                "success": True,
+                "data": {
+                    "content_gaps": gaps,
+                    "recommendations": recommendations,
+                    "metrics": {
+                        "word_count": content_info.get("word_count", 0),
+                        "heading_count": content_info.get("heading_count", 0),
+                        "image_count": content_info.get("image_count", 0),
+                        "link_count": content_info.get("link_count", 0),
+                        "paragraph_count": content_info.get("paragraph_count", 0),
+                        "load_time": performance.get("load_time", 0),
+                        "response_time": performance.get("response_time", 0)
+                    }
+                }
+            }
+            
+        except Exception as e:
+            error_msg = f"Error in content gap analysis: {str(e)}"
+            logger.error(error_msg, exc_info=True)
+            return {
+                "success": False,
+                "error": error_msg,
+                "error_details": {
+                    "type": type(e).__name__,
+                    "traceback": str(e.__traceback__)
+                },
+                "stage": "general"
+            } 

lib/utils/website_analyzer/seo_analyzer.py

@@ -1,233 +0,0 @@
-"""SEO analyzer module with AI integration."""
-
-import requests
-from bs4 import BeautifulSoup
-from datetime import datetime
-from typing import Dict, List, Tuple, Optional
-from urllib.parse import urlparse
-import openai
-from loguru import logger
-import os
-from dotenv import load_dotenv
-from .models import (
-    SEOAnalysisResult,
-    MetaTagAnalysis,
-    ContentAnalysis,
-    SEORecommendation
-)
-
-def extract_content(url: str) -> Tuple[Optional[str], Optional[BeautifulSoup], List[str]]:
-    """Extract content from URL."""
-    errors = []
-    try:
-        response = requests.get(url, timeout=10)
-        response.raise_for_status()
-        soup = BeautifulSoup(response.text, 'html.parser')
-        return response.text, soup, errors
-    except requests.RequestException as e:
-        error_msg = f"Error fetching URL: {str(e)}"
-        logger.error(error_msg)
-        errors.append(error_msg)
-        return None, None, errors
-
-def analyze_meta_tags(soup: BeautifulSoup) -> MetaTagAnalysis:
-    """Analyze meta tags using BeautifulSoup."""
-    # Title analysis
-    title = soup.title.string if soup.title else ""
-    title_analysis = {
-        'status': 'good' if title and 30 <= len(title) <= 60 else 'needs_improvement',
-        'value': title,
-        'recommendation': '' if title and 30 <= len(title) <= 60 else 'Title should be between 30-60 characters'
-    }
-
-    # Meta description analysis
-    meta_desc = soup.find('meta', attrs={'name': 'description'})
-    desc = meta_desc.get('content', '') if meta_desc else ""
-    desc_analysis = {
-        'status': 'good' if desc and 120 <= len(desc) <= 160 else 'needs_improvement',
-        'value': desc,
-        'recommendation': '' if desc and 120 <= len(desc) <= 160 else 'Description should be between 120-160 characters'
-    }
-
-    # Keywords analysis
-    meta_keywords = soup.find('meta', attrs={'name': 'keywords'})
-    keywords = meta_keywords.get('content', '') if meta_keywords else ""
-    keywords_analysis = {
-        'status': 'good' if keywords else 'needs_improvement',
-        'value': keywords,
-        'recommendation': '' if keywords else 'Add relevant keywords meta tag'
-    }
-
-    return MetaTagAnalysis(
-        title=title_analysis,
-        description=desc_analysis,
-        keywords=keywords_analysis,
-        has_robots=bool(soup.find('meta', attrs={'name': 'robots'})),
-        has_sitemap=bool(soup.find('link', attrs={'rel': 'sitemap'}))
-    )
-
-def analyze_content_with_ai(content: str) -> Tuple[ContentAnalysis, List[SEORecommendation]]:
-    """Analyze content using AI."""
-    try:
-        # Load environment variables
-        load_dotenv()
-        
-        # Get API key from environment
-        api_key = os.getenv('OPENAI_API_KEY')
-        if not api_key:
-            raise ValueError("OpenAI API key not found in environment variables")
-        
-        # Initialize OpenAI client
-        client = openai.OpenAI(api_key=api_key)
-        
-        # Prepare prompt for content analysis
-        prompt = f"""Analyze the following webpage content for SEO and provide a structured analysis:
-        Content: {content[:4000]}...  # Truncate to avoid token limits
-        
-        Provide analysis in the following format:
-        1. Word count
-        2. Heading structure analysis
-        3. Keyword density for main topics
-        4. Readability score (0-100)
-        5. Content quality score (0-100)
-        6. List of SEO recommendations with priority (high/medium/low), category, issue, recommendation, and impact
-        
-        Format the response as JSON."""
-
-        # Get AI analysis
-        response = client.chat.completions.create(
-            model="gpt-4",
-            messages=[
-                {"role": "system", "content": "You are an SEO expert analyzing website content."},
-                {"role": "user", "content": prompt}
-            ],
-            response_format={"type": "json_object"}
-        )
-
-        # Parse AI response
-        analysis = response.choices[0].message.content
-        
-        # Create ContentAnalysis object
-        content_analysis = ContentAnalysis(
-            word_count=len(content.split()),
-            headings_structure=analysis.get('heading_structure', {}),
-            keyword_density=analysis.get('keyword_density', {}),
-            readability_score=analysis.get('readability_score', 0),
-            content_quality_score=analysis.get('content_quality_score', 0)
-        )
-
-        # Create recommendations
-        recommendations = [
-            SEORecommendation(
-                priority=rec['priority'],
-                category=rec['category'],
-                issue=rec['issue'],
-                recommendation=rec['recommendation'],
-                impact=rec['impact']
-            )
-            for rec in analysis.get('recommendations', [])
-        ]
-
-        return content_analysis, recommendations
-
-    except Exception as e:
-        logger.error(f"Error in AI analysis: {str(e)}")
-        return ContentAnalysis(
-            word_count=len(content.split()),
-            headings_structure={},
-            keyword_density={},
-            readability_score=0,
-            content_quality_score=0
-        ), []
-
-def analyze_seo(url: str) -> SEOAnalysisResult:
-    """Main function to analyze website SEO."""
-    errors = []
-    warnings = []
-    
-    # Validate URL
-    try:
-        parsed_url = urlparse(url)
-        if not all([parsed_url.scheme, parsed_url.netloc]):
-            errors.append("Invalid URL format")
-            raise ValueError("Invalid URL format")
-    except Exception as e:
-        errors.append(f"URL parsing error: {str(e)}")
-        return SEOAnalysisResult(
-            url=url,
-            analyzed_at=datetime.now(),
-            overall_score=0,
-            meta_tags=None,
-            content=None,
-            recommendations=[],
-            errors=errors,
-            warnings=warnings,
-            success=False
-        )
-
-    # Extract content
-    content, soup, extract_errors = extract_content(url)
-    errors.extend(extract_errors)
-    
-    if not content or not soup:
-        return SEOAnalysisResult(
-            url=url,
-            analyzed_at=datetime.now(),
-            overall_score=0,
-            meta_tags=None,
-            content=None,
-            recommendations=[],
-            errors=errors,
-            warnings=warnings,
-            success=False
-        )
-
-    try:
-        # Analyze meta tags
-        meta_analysis = analyze_meta_tags(soup)
-        
-        # Analyze content with AI
-        content_analysis, recommendations = analyze_content_with_ai(content)
-        
-        # Calculate overall score
-        meta_score = sum([
-            1 if meta_analysis.title['status'] == 'good' else 0,
-            1 if meta_analysis.description['status'] == 'good' else 0,
-            1 if meta_analysis.keywords['status'] == 'good' else 0,
-            1 if meta_analysis.has_robots else 0,
-            1 if meta_analysis.has_sitemap else 0
-        ]) * 20  # Scale to 100
-
-        overall_score = (
-            meta_score * 0.3 +  # 30% weight for meta tags
-            content_analysis.readability_score * 0.3 +  # 30% weight for readability
-            content_analysis.content_quality_score * 0.4  # 40% weight for content quality
-        )
-
-        return SEOAnalysisResult(
-            url=url,
-            analyzed_at=datetime.now(),
-            overall_score=overall_score,
-            meta_tags=meta_analysis,
-            content=content_analysis,
-            recommendations=recommendations,
-            errors=errors,
-            warnings=warnings,
-            success=True
-        )
-
-    except Exception as e:
-        error_msg = f"Error in SEO analysis: {str(e)}"
-        logger.error(error_msg)
-        errors.append(error_msg)
-        return SEOAnalysisResult(
-            url=url,
-            analyzed_at=datetime.now(),
-            overall_score=0,
-            meta_tags=None,
-            content=None,
-            recommendations=[],
-            errors=errors,
-            warnings=warnings,
-            success=False
-        ) 

lib/workspace/alwrity_ui_styling.css

@@ -673,4 +673,19 @@ select option {
 .search-option.disabled h4,
 .search-option.disabled p {
     color: #64748b;
+}
+
+/* Move main content upwards and reduce free space at the top */
+.main .block-container {
+    padding-top: 0 !important;
+    margin-top: 0 !important;
+}
+h1 {
+    margin-top: 0 !important;
+    padding-top: 0 !important;
+}
+/* Optionally, reduce margin for the main title container if used */
+.seo-main-title, .main-title, .dashboard-title {
+    margin-top: 0 !important;
+    padding-top: 0 !important;
 }

requirements.txt

@@ -21,8 +21,8 @@ imageio-ffmpeg==0.4.5
 decorator==4.4.2
 reportlab==4.4.0
 textblob==0.19.0
-numpy>=1.22.4,<2.0.0
+numpy>=1.24.0
-pandas>=2.0.3
+pandas>=2.0.0
 scikit-learn>=1.3.2
 matplotlib>=3.8.2
 plotly>=5.18.0
@@ -40,7 +40,7 @@ lxml_html_clean>=0.4.1
 streamlit>=1.44.0
 Authlib>=1.3.2
 yfinance>=0.2.36
-pandas_ta>=0.3.14b0
+pandas-ta>=0.3.14b0
 firecrawl-py>=1.14.1
 gTTS>=2.5.1
 streamlit-mic-recorder>=0.0.8
@@ -52,4 +52,6 @@ tavily-python>=0.2.8
 tinify>=1.6.0
 validators>=0.20.0
 python-whois==0.9.5
-dnspython
+dnspython
+sqlalchemy==2.0.41
+scipy>=1.10.0