Blog writer enhancements & fixes

.gitignore

@@ -10,6 +10,13 @@
 pycache
 __pycache__
 *.pyc
+gemini-native-image*
+*.mp4
+*.mp3
+*.wav
+*.avi
+*.mov
+*.flv
 *.pyo
 *.pyd
 *.pyw

lib/ai_writers/ai_blog_writer/ai_blog_generator.py

@@ -0,0 +1,635 @@
+import os
+import streamlit as st
+from loguru import logger
+
+from lib.utils.voice_processing import record_voice
+from lib.ai_writers.ai_blog_writer.blog_writer_styles import apply_blog_writer_styles
+from lib.ai_writers.ai_blog_writer.ai_blog_generator_utils import (
+    CONFIG_PATH,
+    load_config,
+    get_search_params_from_config,
+    get_blog_characteristics_from_config,
+    get_blog_images_from_config,
+    get_llm_options_from_config,
+    process_input,
+    handle_content_generation
+)
+
+apply_blog_writer_styles()
+
+def display_input_section():
+    """Display the input section with text area, file upload, and voice recording options."""
+    # Main container with columns for better organization
+    col1, col2, col3 = st.columns([2, 1.5, 0.5])
+    
+    # First column: Keywords input
+    with col1:
+        st.markdown("### 📌 Content Source")
+        st.markdown("#### Enter Keywords, Title or URL")
+        user_input = st.text_area(
+            'Power your content with keywords or a website URL',
+            help='Provide keywords, a blog title, YouTube link, or web URL to generate targeted content.',
+            placeholder="Examples:\n- Keywords: AI tools, digital marketing\n- Blog Title: The Future of AI in Marketing\n- YouTube Link: https://youtube.com/...\n- Web URL: https://example.com/...",
+            height=150
+        )
+    
+    # Second column: File uploader
+    with col2:
+        st.markdown("### 📁 File Upload")
+        st.markdown("#### Upload Reference Content")
+        uploaded_file = st.file_uploader(
+            "Add files to enhance your content",
+                                         type=["txt", "pdf", "docx", "jpg", "jpeg", "png", "mp3", "wav", "mp4", "mkv", "avi"],
+            help='Upload documents, images, or media files to incorporate additional information in your blog.'
+        )
+    
+    # Third column: Voice input
+    with col3:
+        st.markdown("### 🎤 Voice")
+        st.markdown("#### Record Ideas")
+        audio_input = record_voice()
+        if audio_input:
+            st.success("Voice recorded!")
+    
+    return user_input, uploaded_file, audio_input
+
+
+def display_content_type_selection():
+    """Display the content type selection section and return the selected type."""
+    # Content options in a cleaner layout
+    st.markdown("### 🔧 Content Configuration")
+    
+    # Content type selection with better UI
+    st.markdown("#### Select Content Type")
+    content_type = st.radio(
+        "Choose the format and length of your blog content",
+        ["Standard Blog Post", "Comprehensive Long-form", "AI Agent Team (Beta)"],
+        horizontal=True,
+        help="Standard: 800-1200 words | Long-form: 1500+ words | AI Agent: Experimental multi-perspective content"
+    )
+    
+    # Map the friendly content type names to the original options
+    content_type_map = {
+        "Standard Blog Post": "Normal-length content",
+        "Comprehensive Long-form": "Long-form content",
+        "AI Agent Team (Beta)": "Experimental - AI Agents team"
+    }
+    
+    return content_type, content_type_map[content_type]
+
+
+def display_content_characteristics_tab():
+    """Display the Content Characteristics tab and return the selected options."""
+    st.markdown("#### Blog Content Characteristics")
+    
+    # Load default values from configuration
+    config_blog_chars = get_blog_characteristics_from_config()
+    
+    # Blog length
+    blog_length = st.number_input(
+        "Blog Length (words)",
+        min_value=500,
+        max_value=5000,
+        value=int(config_blog_chars.get("blog_length", 2000)),
+        step=100,
+        help="Target word count for your blog post"
+    )
+    
+    # Blog tone
+    tone_options = ["Professional", "Casual", "Formal", "Conversational", "Authoritative", "Friendly"]
+    default_tone = config_blog_chars.get("blog_tone", "Professional")
+    default_tone_index = tone_options.index(default_tone) if default_tone in tone_options else 0
+    
+    blog_tone = st.selectbox(
+        "Blog Tone",
+        options=tone_options,
+        index=default_tone_index,
+        help="The overall tone and style of your blog content"
+    )
+    
+    # Blog demographic
+    demographic_options = ["Professional", "General", "Technical", "Beginner", "Expert", "Student"]
+    default_demo = config_blog_chars.get("blog_demographic", "Professional")
+    default_demo_index = demographic_options.index(default_demo) if default_demo in demographic_options else 0
+    
+    blog_demographic = st.selectbox(
+        "Target Audience",
+        options=demographic_options,
+        index=default_demo_index,
+        help="Who your blog content is primarily written for"
+    )
+    
+    # Blog type
+    type_options = ["Informational", "How-to", "List", "Review", "Tutorial", "Opinion"]
+    default_type = config_blog_chars.get("blog_type", "Informational")
+    default_type_index = type_options.index(default_type) if default_type in type_options else 0
+    
+    blog_type = st.selectbox(
+        "Blog Type",
+        options=type_options,
+        index=default_type_index,
+        help="The format and purpose of your blog content"
+    )
+    
+    # Blog language
+    language_options = ["English", "Spanish", "French", "German", "Italian", "Portuguese"]
+    default_lang = config_blog_chars.get("blog_language", "English")
+    default_lang_index = language_options.index(default_lang) if default_lang in language_options else 0
+    
+    blog_language = st.selectbox(
+        "Blog Language",
+        options=language_options,
+        index=default_lang_index,
+        help="The language your blog will be written in"
+    )
+    
+    # Blog output format
+    format_options = ["markdown", "html", "plain text"]
+    default_format = config_blog_chars.get("blog_output_format", "markdown").lower()
+    default_format_index = format_options.index(default_format) if default_format in format_options else 0
+    
+    blog_output_format = st.selectbox(
+        "Output Format",
+        options=format_options,
+        index=default_format_index,
+        help="The format in which the blog content will be generated"
+    )
+    
+    # Show current configuration source
+    if os.path.exists(CONFIG_PATH):
+        st.success(f"✅ Using blog characteristics from configuration file")
+    else:
+        st.info("ℹ️ Using default blog characteristics (no configuration file found)")
+    
+    return {
+        "blog_length": blog_length,
+        "blog_tone": blog_tone,
+        "blog_demographic": blog_demographic,
+        "blog_type": blog_type,
+        "blog_language": blog_language,
+        "blog_output_format": blog_output_format
+    }
+
+
+def display_content_analysis_tab():
+    """Display the Content & Analysis Options tab and return the selected options."""
+    st.markdown("#### Content & Analysis Options")
+    
+    # Create two columns for better organization
+    col1, col2 = st.columns(2)
+    
+    with col1:
+        st.markdown("**Content Enhancements**")
+        create_seo_tags = st.checkbox(
+            '✅ Generate SEO metadata',
+            value=True,
+            help='Create schema markup, meta tags, and social media metadata'
+        )
+        generate_social_media = st.checkbox(
+            '✅ Create social media posts',
+            value=False,
+            help="Generate matching social content for Facebook, Twitter, and LinkedIn"
+        )
+        add_table_of_contents = st.checkbox(
+            '✅ Add table of contents',
+            value=True,
+            help="Include an auto-generated table of contents at the beginning of the blog"
+        )
+    
+    with col2:
+        st.markdown("**Analysis & Improvement**")
+        content_analysis = st.checkbox(
+            '✅ Perform content analysis',
+            value=False,
+            help="Include proofreading, readability score, and improvement suggestions"
+        )
+        enhance_readability = st.checkbox(
+            '✅ Enhance readability',
+            value=True,
+            help="Optimize sentence structure and vocabulary for better readability"
+        )
+        fact_checking = st.checkbox(
+            '✅ Basic fact verification',
+            value=False,
+            help="Verify key facts from multiple sources when possible"
+        )
+    
+    st.markdown("---")
+    st.markdown("**Formatting Options**")
+    
+    # Create two columns for formatting options
+    fmt_col1, fmt_col2 = st.columns(2)
+    
+    with fmt_col1:
+        section_headings = st.checkbox(
+            '✅ Use section headings',
+            value=True,
+            help="Include clear section headings throughout the blog"
+        )
+        include_lists = st.checkbox(
+            '✅ Use bullet points and lists',
+            value=True,
+            help="Format appropriate content as bullet points or numbered lists"
+        )
+    
+    with fmt_col2:
+        include_quotes = st.checkbox(
+            '✅ Include relevant quotes',
+            value=False,
+            help="Add expert quotes or important statements as blockquotes"
+        )
+        use_subheadings = st.checkbox(
+            '✅ Use subheadings',
+            value=True,
+            help="Break down sections with descriptive subheadings"
+        )
+    
+    return {
+        "create_seo_tags": create_seo_tags,
+        "generate_social_media": generate_social_media,
+        "add_table_of_contents": add_table_of_contents,
+        "content_analysis": content_analysis,
+        "enhance_readability": enhance_readability,
+        "fact_checking": fact_checking,
+        "section_headings": section_headings,
+        "include_lists": include_lists,
+        "include_quotes": include_quotes,
+        "use_subheadings": use_subheadings
+    }
+
+
+def display_blog_images_tab():
+    """Display the Blog Images Details tab and return the selected options."""
+    st.markdown("#### Blog Images Settings")
+    
+    # Load default values from configuration
+    config_images = get_blog_images_from_config()
+    
+    # Image generation model selection
+    model_options = ["stable-diffusion", "dall-e", "midjourney", "imagen"]
+    default_model = config_images.get("image_model", "stable-diffusion")
+    default_model_index = model_options.index(default_model) if default_model in model_options else 0
+    
+    image_model = st.selectbox(
+        "Image Generation Model",
+        options=model_options,
+        index=default_model_index,
+        help="AI model used to generate blog images"
+    )
+    
+    # Number of blog images
+    num_images = st.number_input(
+        "Number of Blog Images",
+        min_value=0,
+        max_value=10,
+        value=config_images.get("num_images", 1),
+        step=1,
+        help="Number of images to generate for the blog"
+    )
+    
+    # Image style
+    style_options = ["Realistic", "Artistic", "Cartoon", "Minimalist", "Corporate", "Vibrant"]
+    default_style = config_images.get("image_style", "Realistic")
+    default_style_index = style_options.index(default_style) if default_style in style_options else 0
+    
+    image_style = st.selectbox(
+        "Image Style",
+        options=style_options,
+        index=default_style_index,
+        help="Visual style of the generated images"
+    )
+    
+    # Additional image options
+    st.markdown("**Additional Image Options**")
+    
+    col1, col2 = st.columns(2)
+    
+    with col1:
+        generate_featured = st.checkbox(
+            '✅ Generate featured image',
+            value=True,
+            help="Create a featured header image for the blog"
+        )
+        add_captions = st.checkbox(
+            '✅ Add image captions',
+            value=True,
+            help="Generate descriptive captions for each image"
+        )
+    
+    with col2:
+        use_alt_text = st.checkbox(
+            '✅ Generate alt text',
+            value=True,
+            help="Create accessibility alt text for all images"
+        )
+        optimize_images = st.checkbox(
+            '✅ Optimize image placement',
+            value=True,
+            help="Intelligently place images throughout the content"
+        )
+    
+    # Show current configuration source
+    if os.path.exists(CONFIG_PATH):
+        st.success(f"✅ Using image settings from configuration file")
+    else:
+        st.info("ℹ️ Using default image settings (no configuration file found)")
+    
+    return {
+        "image_model": image_model,
+        "num_images": num_images,
+        "image_style": image_style,
+        "generate_featured": generate_featured,
+        "add_captions": add_captions,
+        "use_alt_text": use_alt_text,
+        "optimize_placement": optimize_images
+    }
+
+
+def display_llm_options_tab():
+    """Display the LLM Options tab and return the selected options."""
+    st.markdown("#### Language Model Settings")
+    
+    # Load default values from configuration
+    config_llm = get_llm_options_from_config()
+    
+    # LLM provider selection
+    provider_options = ["google", "openai", "anthropic", "local"]
+    default_provider = config_llm.get("provider", "google")
+    default_provider_index = provider_options.index(default_provider) if default_provider in provider_options else 0
+    
+    llm_provider = st.selectbox(
+        "AI Provider",
+        options=provider_options,
+        index=default_provider_index,
+        help="The AI provider to use for content generation"
+    )
+    
+    # Model selection (dynamic based on provider)
+    if llm_provider == "google":
+        model_options = ["gemini-1.5-flash-latest", "gemini-1.5-pro-latest", "gemini-pro"]
+    elif llm_provider == "openai":
+        model_options = ["gpt-4o", "gpt-4-turbo", "gpt-3.5-turbo"]
+    elif llm_provider == "anthropic":
+        model_options = ["claude-3-opus", "claude-3-sonnet", "claude-3-haiku"]
+    else:
+        model_options = ["llama-3-70b", "mistral-large", "local-model"]
+    
+    default_model = config_llm.get("model", "gemini-1.5-flash-latest")
+    default_model_index = 0
+    if default_model in model_options:
+        default_model_index = model_options.index(default_model)
+    
+    llm_model = st.selectbox(
+        "AI Model",
+        options=model_options,
+        index=default_model_index,
+        help="The specific AI model to use for content generation"
+    )
+    
+    # Create two columns for temperature and max tokens
+    col1, col2 = st.columns(2)
+    
+    with col1:
+        # Temperature setting
+        temperature = st.slider(
+            "Temperature",
+            min_value=0.0,
+            max_value=1.0,
+            value=config_llm.get("temperature", 0.7),
+            step=0.1,
+            help="Controls randomness: lower values are more deterministic, higher values more creative"
+        )
+    
+    with col2:
+        # Max tokens
+        max_tokens = st.number_input(
+            "Max Tokens",
+            min_value=1000,
+            max_value=32000,
+            value=config_llm.get("max_tokens", 4000),
+            step=1000,
+            help="Maximum length of generated content (in tokens)"
+        )
+    
+    # Advanced LLM options
+    st.markdown("---")
+    st.markdown("**Advanced LLM Options**")
+    show_advanced_llm = st.checkbox("Show advanced LLM parameters", value=False)
+    
+    advanced_params = {}
+    if show_advanced_llm:
+        # Top-p (nucleus sampling)
+        top_p = st.slider(
+            "Top-p (Nucleus Sampling)",
+            min_value=0.1,
+            max_value=1.0,
+            value=0.9,
+            step=0.1,
+            help="Controls diversity via nucleus sampling: 1.0 considers all tokens, lower values restrict to more likely tokens"
+        )
+        
+        # Top-k
+        top_k = st.slider(
+            "Top-k",
+            min_value=1,
+            max_value=100,
+            value=40,
+            step=1,
+            help="Controls diversity by limiting to top k tokens: higher values allow more diversity"
+        )
+        
+        # Presence penalty
+        presence_penalty = st.slider(
+            "Presence Penalty",
+            min_value=-2.0,
+            max_value=2.0,
+            value=0.0,
+            step=0.1,
+            help="Penalizes repeated tokens: positive values discourage repetition"
+        )
+        
+        advanced_params = {
+            "top_p": top_p,
+            "top_k": top_k,
+            "presence_penalty": presence_penalty
+        }
+    
+    # Show current configuration source
+    if os.path.exists(CONFIG_PATH):
+        st.success(f"✅ Using LLM settings from configuration file")
+    else:
+        st.info("ℹ️ Using default LLM settings (no configuration file found)")
+    
+    return {
+        "provider": llm_provider,
+        "model": llm_model,
+        "temperature": temperature,
+        "max_tokens": max_tokens,
+        **advanced_params
+    }
+
+
+def display_search_settings_tab():
+    """Display the Search Settings tab and return the selected options."""
+    st.markdown("#### AI Search Configuration")
+    st.markdown("Control how the AI researches your topic")
+    
+    # Load default values from configuration
+    config_search_params = get_search_params_from_config()
+    
+    # Number of search results
+    max_results = st.slider(
+        "Maximum Results",
+        min_value=5,
+        max_value=30,
+        value=config_search_params.get("max_results", 10),
+        step=5,
+        help="Maximum number of search results to use for research"
+    )
+    
+    # Search depth
+    search_depth = st.radio(
+        "Search Depth",
+        options=["basic", "advanced"],
+        index=0,
+        horizontal=True,
+        help="Basic: Faster but less comprehensive. Advanced: More thorough but slower."
+    )
+    
+    # Include domains
+    include_domains = st.text_input(
+        "Include Domains (Optional)",
+        value="",
+        help="Comma-separated list of domains to prioritize in search (e.g., wikipedia.org,nih.gov)"
+    )
+    
+    # Time range - use value from config
+    time_options = ["day", "week", "month", "year", "all"]
+    default_time_index = time_options.index(config_search_params.get("time_range", "year")) if config_search_params.get("time_range", "year") in time_options else 3  # Default to "year" (index 3)
+    
+    time_range = st.select_slider(
+        "Time Range",
+        options=time_options,
+        value=time_options[default_time_index],
+        help="Limit search results to a specific time period"
+    )
+    
+    # Show current configuration source
+    if os.path.exists(CONFIG_PATH):
+        st.success(f"✅ Using search defaults from configuration file")
+    else:
+        st.info("ℹ️ Using default search settings (no configuration file found)")
+    
+    # Replace expander with checkbox for configuration display
+    show_config = st.checkbox("Show configuration details", value=False)
+    if show_config:
+        st.markdown("""
+        **Configuration File Location**  
+        Search parameters are loaded from the main configuration file at:  
+        `lib/workspace/alwrity_config/main_config.json`
+        
+        You can modify this file to change the default search settings.
+        """)
+        
+        if os.path.exists(CONFIG_PATH):
+            try:
+                with open(CONFIG_PATH, 'r') as f:
+                    config_content = f.read()
+                st.code(config_content, language="json")
+            except:
+                st.warning("Could not read configuration file")
+    
+    st.info("These settings control how the AI performs web research for your content. More thorough searches may take longer but produce better results.")
+    
+    # Process include_domains from string to list if provided
+    domains_list = []
+    if include_domains:
+        domains_list = [domain.strip() for domain in include_domains.split(",") if domain.strip()]
+    
+    return {
+        "max_results": max_results,
+        "search_depth": search_depth,
+        "time_range": time_range,
+        "include_domains": domains_list
+    }
+
+
+def display_advanced_options():
+    """Display all advanced options tabs and return the selected configurations."""
+    with st.expander("⚙️ Advanced Options", expanded=False):
+        tabs = st.tabs(["Content Characteristics", "Content & Analysis Options", "Blog Images Details", "LLM Options", "Search Settings"])
+        
+        with tabs[0]:  # Content Characteristics
+            blog_params = display_content_characteristics_tab()
+        
+        with tabs[1]:  # Combined Content & Analysis Options
+            content_analysis_params = display_content_analysis_tab()
+        
+        with tabs[2]:  # Blog Images Details
+            image_params = display_blog_images_tab()
+        
+        with tabs[3]:  # LLM Options
+            llm_params = display_llm_options_tab()
+        
+        with tabs[4]:  # Search Settings
+            search_params = display_search_settings_tab()
+    
+    return blog_params, content_analysis_params, image_params, llm_params, search_params
+
+
+def blog_from_keyword():
+    """Input blog keywords, research and write a factual blog with enhanced UI."""
+    
+    # Get user inputs
+    user_input, uploaded_file, audio_input = display_input_section()
+    
+    # Get content type selection
+    content_type, selected_content_type = display_content_type_selection()
+    
+    # Display advanced options and get configurations
+    blog_params, content_analysis_params, image_params, llm_params, search_params = display_advanced_options()
+    
+    # Generate button with icon and clearer purpose
+    st.markdown("")  # Add spacing
+    generate_pressed = st.button("✨ Generate Professional Blog Content", use_container_width=True)
+    
+    # Processing logic
+    if generate_pressed:
+        st.empty()
+        
+        if not uploaded_file and not user_input and not audio_input:
+            st.error("Please provide at least one input source (keywords, file, or voice recording)")
+            st.stop()
+        
+        input_type = process_input(user_input, uploaded_file)
+        
+        # Use the utility function to handle content generation
+        handle_content_generation(input_type, user_input, uploaded_file, search_params, blog_params, selected_content_type)
+
+
+def ai_blog_writer_page():
+    """Render the AI Blog Writer page with enhanced styling."""
+    logger.info("Rendering AI Blog Writer page")
+    
+    # Apply shared blog writer styles
+    apply_blog_writer_styles()
+    
+    # Back button with icon
+    if st.button("← Back to Dashboard", key="back_to_dashboard"):
+        logger.info("User clicked back button, returning to ai writer dashboard")
+        st.query_params.clear()
+        st.rerun()
+    
+    # Enhanced header with icon
+    st.markdown("""
+        <div class="page-header">
+            <h1>✍️ AI Blog Writer</h1>
+            <p>Create engaging, SEO-optimized blog content with AI assistance. Our advanced algorithms help you generate high-quality, relevant articles for any topic or niche.</p>
+        </div>
+    """, unsafe_allow_html=True)
+    
+    # Call the blog generator function with enhanced UI
+    logger.info("Calling blog_from_keyword function")
+    blog_from_keyword()
+    
+    logger.info("Finished rendering AI Blog Writer page")

lib/ai_writers/ai_blog_writer/ai_blog_generator_utils.py

@@ -0,0 +1,382 @@
+import re
+import os
+import json
+from loguru import logger
+import PyPDF2
+import streamlit as st
+import tiktoken
+import openai
+
+from lib.gpt_providers.text_generation.main_text_generation import llm_text_gen
+from lib.ai_writers.keywords_to_blog_streamlit import write_blog_from_keywords
+from lib.ai_writers.speech_to_blog.main_audio_to_blog import generate_audio_blog
+from lib.ai_writers.long_form_ai_writer import long_form_generator
+from lib.ai_writers.web_url_ai_writer import blog_from_url
+from lib.ai_writers.image_ai_writer import blog_from_image
+
+# Constants
+CONFIG_PATH = os.path.join("lib", "workspace", "alwrity_config", "main_config.json")
+DEFAULT_CONFIG = {
+    "Search Engine Parameters": {
+        "Geographic Location": "us",
+        "Search Language": "en",
+        "Number of Results": 10,
+        "Time Range": "year"
+    }
+}
+
+# Function to load configuration from JSON file
+def load_config():
+    """Load configuration from the main config JSON file."""
+    try:
+        if os.path.exists(CONFIG_PATH):
+            with open(CONFIG_PATH, 'r') as f:
+                config = json.load(f)
+                logger.info(f"Loaded configuration from {CONFIG_PATH}")
+                return config
+        else:
+            logger.warning(f"Configuration file not found at {CONFIG_PATH}, using defaults")
+            return DEFAULT_CONFIG
+    except Exception as e:
+        logger.error(f"Error loading configuration: {str(e)}")
+        return DEFAULT_CONFIG
+
+# Function to get search parameters from config
+def get_search_params_from_config():
+    """Extract search parameters from the main configuration."""
+    config = load_config()
+    search_params = config.get("Search Engine Parameters", {})
+    
+    # Map config values to expected parameter names
+    result = {
+        "max_results": search_params.get("Number of Results", 10),
+        "time_range": search_params.get("Time Range", "year").lower(),
+        "geo": search_params.get("Geographic Location", "us"),
+        "language": search_params.get("Search Language", "en")
+    }
+    
+    # Normalize time_range to match our options
+    time_map = {
+        "day": "day",
+        "week": "week", 
+        "month": "month",
+        "year": "year",
+        "anytime": "all",
+        "all": "all"
+    }
+    result["time_range"] = time_map.get(result["time_range"].lower(), "year")
+    
+    logger.info(f"Using search parameters from config: {result}")
+    return result
+
+# Function to get blog content characteristics from config
+def get_blog_characteristics_from_config():
+    """Extract blog content characteristics from the main configuration."""
+    config = load_config()
+    blog_characteristics = config.get("Blog Content Characteristics", {})
+    
+    # Map config values to expected parameter names
+    result = {
+        "blog_length": blog_characteristics.get("Blog Length", "2000"),
+        "blog_tone": blog_characteristics.get("Blog Tone", "Professional"),
+        "blog_demographic": blog_characteristics.get("Blog Demographic", "Professional"),
+        "blog_type": blog_characteristics.get("Blog Type", "Informational"),
+        "blog_language": blog_characteristics.get("Blog Language", "English"),
+        "blog_output_format": blog_characteristics.get("Blog Output Format", "markdown")
+    }
+    
+    logger.info(f"Using blog characteristics from config: {result}")
+    return result
+
+# Function to get blog image details from config
+def get_blog_images_from_config():
+    """Extract blog image details from the main configuration."""
+    config = load_config()
+    blog_images = config.get("Blog Images Details", {})
+    
+    # Map config values to expected parameter names
+    result = {
+        "image_model": blog_images.get("Image Generation Model", "stable-diffusion"),
+        "num_images": int(blog_images.get("Number of Blog Images", 1)),
+        "image_style": blog_images.get("Image Style", "Realistic")
+    }
+    
+    logger.info(f"Using blog image details from config: {result}")
+    return result
+
+# Function to get LLM options from config
+def get_llm_options_from_config():
+    """Extract LLM options from the main configuration."""
+    config = load_config()
+    llm_options = config.get("LLM Options", {})
+    
+    # Map config values to expected parameter names
+    result = {
+        "provider": llm_options.get("GPT Provider", "google"),
+        "model": llm_options.get("Model", "gemini-1.5-flash-latest"),
+        "temperature": float(llm_options.get("Temperature", 0.7)),
+        "max_tokens": int(llm_options.get("Max Tokens", 4000))
+    }
+    
+    logger.info(f"Using LLM options from config: {result}")
+    return result
+
+# Split a text into smaller chunks of size n, preferably ending at the end of a sentence
+def create_chunks(text, n, tokenizer):
+    tokens = tokenizer.encode(text)
+    """Yield successive n-sized chunks from text."""
+    i = 0
+    while i < len(tokens):
+        # Find the nearest end of sentence within a range of 0.5 * n and 1.5 * n tokens
+        j = min(i + int(1.5 * n), len(tokens))
+        while j > i + int(0.5 * n):
+            # Decode the tokens and check for full stop or newline
+            chunk = tokenizer.decode(tokens[i:j])
+            if chunk.endswith(".") or chunk.endswith("\n"):
+                break
+            j -= 1
+        # If no end of sentence found, use n tokens as the chunk size
+        if j == i + int(0.5 * n):
+            j = min(i + n, len(tokens))
+        yield tokens[i:j]
+        i = j
+
+
+def extract_chunk(document, template_prompt):
+    """ Chunking for large documents, exceed context window"""
+    prompt = template_prompt.replace('<document>', document)
+
+    try:
+        response = llm_text_gen(prompt)
+        return response
+    except Exception as err:
+        logger.error(f"Failed to get response from LLM: {err}")
+        raise
+
+
+def blog_from_pdf(pdf_text):
+    """ 
+    Load in a long PDF and extract key information.
+    Chunk up document and process each chunk, then combine them.
+    """
+    template_prompt=f'''Extract key pieces of information from the given document.
+
+        When you extract a key piece of information, include the closest page number.
+        Ex: Extracted Information (Page number)
+        \n\nDocument: \"\"\"<document>\"\"\"\n\n'''
+
+    # Initialize tokenizer
+    tokenizer = tiktoken.get_encoding("cl100k_base")
+    results = []
+    
+    chunks = create_chunks(pdf_text, 1000, tokenizer)
+    text_chunks = [tokenizer.decode(chunk) for chunk in chunks]
+
+    for chunk in text_chunks:
+        try:
+            results.append(extract_chunk(chunk, template_prompt))
+        except Exception as e:
+            logger.error(f"Error processing chunk: {e}")
+            # Continue with other chunks even if one fails
+            continue
+
+    return results
+
+
+# Input validation functions
+def is_youtube_link(text):
+    """Check if text is a valid YouTube link."""
+    if text is not None:
+        youtube_regex = re.compile(r'(https?://)?(www\.)?(youtube|youtu|youtube-nocookie)\.(com|be)/(watch\?v=|embed/|v/|.+\?v=)?([^&=%\?]{11})')
+        return youtube_regex.match(text)
+    return False
+
+
+def is_web_link(text):
+    """Check if text is a valid web link."""
+    if text is not None:
+        web_regex = re.compile(r'(https?://)?(www\.)?[-a-zA-Z0-9@:%._\+~#=]{1,256}\.[a-zA-Z0-9()]{1,6}\b([-a-zA-Z0-9()@:%_\+.~#?&//=]*)')
+        return web_regex.match(text)
+    return False
+    
+
+def process_input(input_text, uploaded_file):
+    """
+    Determine the type of input provided by the user.
+    
+    Args:
+        input_text (str): The text input from the user
+        uploaded_file: The file uploaded by the user
+        
+    Returns:
+        str: The determined input type ("youtube_url", "web_url", "keywords", "PDF_file", "image_file", "audio_file", "video_file", or None)
+    """
+    # Process text input
+    if input_text:
+        if is_youtube_link(input_text):
+            if input_text.startswith("https://www.youtube.com/") or input_text.startswith("http://www.youtube.com/"):
+                return "youtube_url"
+            else:
+                st.error("Invalid YouTube URL. Please enter a valid URL.")
+                return None
+        elif is_web_link(input_text):
+            return "web_url"
+        else:
+            return "keywords"
+    
+    # Process file input
+    if uploaded_file is not None:
+        file_details = {"filename": uploaded_file.name, "filetype": uploaded_file.type}
+        st.write(file_details)
+        
+        # Handle different file types
+        if uploaded_file.type.startswith("text/"):
+            content = uploaded_file.read().decode("utf-8")
+            st.text(content)
+            return "text_file"
+        elif uploaded_file.type == "application/pdf":
+            return "PDF_file"
+        elif uploaded_file.type in ["application/vnd.openxmlformats-officedocument.wordprocessingml.document", "application/msword"]:
+            st.write("Word document uploaded. Add your DOCX processing logic here.")
+            return "word_file"
+        elif uploaded_file.type.startswith("image/"):
+            st.image(uploaded_file)
+            return "image_file"
+        elif uploaded_file.type.startswith("audio/"):
+            st.audio(uploaded_file)
+            return "audio_file"
+        elif uploaded_file.type.startswith("video/"):
+            st.video(uploaded_file)
+            return "video_file"
+    
+    return None
+
+
+# Content processing functions
+def process_keywords_input(user_input, search_params, blog_params, selected_content_type):
+    """Process keywords input and generate content based on the selected options."""
+    if not user_input or len(user_input.split()) < 2:
+        st.error('Please provide at least two keywords for best results')
+        return False
+    
+    try:
+        if selected_content_type == "Normal-length content":
+            st.subheader("Your Generated Blog Post")
+            logger.info(f"Generating standard blog post with parameters: {blog_params}")
+            
+            # Ensure all blog parameters are properly passed
+            # This is important as the UI may have settings that aren't in the default blog_params
+            short_blog = write_blog_from_keywords(
+                user_input, 
+                search_params=search_params, 
+                blog_params=blog_params
+            )
+            st.markdown(short_blog)
+            return True
+            
+        elif selected_content_type == "Long-form content":
+            logger.info(f"Generating long-form content with parameters: {blog_params}")
+            
+            # Ensure all blog parameters are properly passed to long-form generator
+            long_form_generator(
+                user_input, 
+                search_params=search_params, 
+                blog_params=blog_params
+            )
+            st.success(f"Successfully generated long-form content for: {user_input}")
+            return True
+            
+        else:
+            st.info("AI Agent Team feature is coming soon! This will provide multi-perspective content with different AI experts collaborating on your blog.")
+            return False
+            
+    except Exception as err:
+        logger.error(f"An error occurred while generating content: {err}")
+        st.error(f"An error occurred while generating content: {err}")
+        return False
+
+
+def process_pdf_input(uploaded_file):
+    """Process a PDF file and generate content."""
+    with st.expander("Processing PDF Document", expanded=True):
+        pdf_reader = PyPDF2.PdfReader(uploaded_file)
+        text = ""
+        combined_result = ""
+        
+        # Show progress with better UI
+        progress_text = st.empty()
+        progress_bar = st.progress(0)
+        
+        total_pages = len(pdf_reader.pages)
+        for page_num, page in enumerate(pdf_reader.pages):
+            progress_text.text(f"Processing page {page_num+1}/{total_pages}")
+            text += page.extract_text()
+            text = text.replace("\n", " ")
+            text = re.sub(r"(\w)([A-Z])", r"\1 \2", text)
+            
+            results = blog_from_pdf(text)
+            progress_percent = (page_num + 1) / total_pages
+            progress_bar.progress(progress_percent)
+            combined_result += str(results[-1])
+        
+        progress_text.empty()
+        progress_bar.empty()
+        
+    st.subheader("Generated Content from PDF")
+    st.markdown(combined_result)
+    return True
+
+
+def process_youtube_or_audio(user_input):
+    """Process a YouTube URL or audio file and generate content."""
+    if not generate_audio_blog(user_input):
+        return False
+    return True
+
+
+def process_web_url(user_input):
+    """Process a web URL and generate content."""
+    blog_from_url(user_input)
+    return True
+
+
+def process_image_input(user_input, uploaded_file):
+    """Process an image file and generate content."""
+    blog_from_image(user_input, uploaded_file)
+    return True
+
+
+def handle_content_generation(input_type, user_input, uploaded_file, search_params, blog_params, selected_content_type):
+    """
+    Handle content generation based on the input type.
+    
+    Args:
+        input_type: The type of input ("youtube_url", "web_url", etc.)
+        user_input: The text input from the user
+        uploaded_file: The uploaded file (if any)
+        search_params: Search parameters
+        blog_params: Blog content parameters
+        selected_content_type: The selected content type
+        
+    Returns:
+        bool: True if content generation was successful, False otherwise
+    """
+    with st.spinner("Crafting your blog content... Please wait."):
+        if input_type == "keywords":
+            return process_keywords_input(user_input, search_params, blog_params, selected_content_type)
+        
+        elif input_type == "youtube_url" or input_type == "audio_file":
+            return process_youtube_or_audio(user_input)
+        
+        elif input_type == "web_url":
+            return process_web_url(user_input)
+        
+        elif input_type == "image_file":
+            return process_image_input(user_input, uploaded_file)
+        
+        elif input_type == "PDF_file":
+            return process_pdf_input(uploaded_file)
+        
+        else:
+            st.error(f"Unsupported input type: {input_type}")
+            return False 

lib/ai_writers/ai_blog_writer/blog_writer_styles.py

@@ -0,0 +1,252 @@
+import streamlit as st
+
+def apply_blog_writer_styles():
+    st.markdown("""
+    <style>
+        /* Base UI improvements */
+        body, .main .block-container {
+            background: linear-gradient(135deg, #f0f4f8 0%, #d7e1ec 100%) !important;
+            min-height: 100vh;
+            color: #2c3e50;
+            font-family: 'Helvetica Neue', sans-serif;
+        }
+        
+        /* Main layout improvements */
+        .main .block-container {
+            padding: 1rem 2rem 2rem 2rem !important;
+            max-width: 1200px;
+            margin: 0 auto;
+        }
+        
+        /* Back button styling */
+        [data-testid="stButton"] > button:first-of-type {
+            background: #1976d2;
+            color: white;
+            border: none;
+            border-radius: 30px;
+            padding: 0.5rem 1.5rem;
+            font-weight: 600;
+            display: flex;
+            align-items: center;
+            justify-content: center;
+            transition: all 0.2s ease;
+            box-shadow: 0 2px 10px rgba(25, 118, 210, 0.2);
+            margin-bottom: 1.5rem;
+        }
+        
+        [data-testid="stButton"] > button:first-of-type:hover {
+            background: #1565c0;
+            transform: translateY(-1px);
+            box-shadow: 0 4px 15px rgba(25, 118, 210, 0.3);
+        }
+        
+        /* Header styling */
+        .blog-header, .page-header {
+            text-align: center;
+            margin-bottom: 2rem;
+            padding: 2rem 1.5rem;
+            background: linear-gradient(135deg, #ffffff 0%, #f5f7fa 100%);
+            border-radius: 16px;
+            box-shadow: 0 4px 20px rgba(0, 0, 0, 0.08);
+            border: 1px solid rgba(0, 0, 0, 0.05);
+        }
+        
+        .blog-header h1, .page-header h1 {
+            font-size: 2.5em;
+            font-family: 'Helvetica Neue', sans-serif;
+            font-weight: 700;
+            color: #1976d2;
+            margin-bottom: 0.5rem;
+        }
+        
+        .blog-header p, .page-header p {
+            font-size: 1.1em;
+            color: #546e7a;
+            max-width: 700px;
+            margin: 0 auto;
+            line-height: 1.6;
+        }
+        
+        /* Input section styling */
+        .stTextArea textarea, .stTextInput input {
+            background: #ffffff;
+            border: 1px solid #e0e0e0;
+            border-radius: 8px;
+            padding: 0.75rem 1rem;
+            color: #2c3e50;
+            font-size: 1rem;
+            box-shadow: 0 2px 8px rgba(0, 0, 0, 0.04);
+            transition: all 0.2s ease;
+        }
+        
+        .stTextArea textarea:focus, .stTextInput input:focus {
+            border: 1.5px solid #1976d2;
+            box-shadow: 0 2px 10px rgba(25, 118, 210, 0.12);
+        }
+        
+        /* File uploader styling */
+        .stFileUploader > div {
+            background: #ffffff;
+            border: 2px dashed #cfd8dc;
+            border-radius: 10px;
+            padding: 1.5rem 1rem;
+            text-align: center;
+            transition: all 0.2s ease;
+        }
+        
+        .stFileUploader > div:hover {
+            border-color: #1976d2;
+            background: rgba(25, 118, 210, 0.03);
+        }
+        
+        /* Options expander styling */
+        .stExpander {
+            border-radius: 10px;
+            overflow: hidden;
+            margin: 1.5rem 0;
+            border: 1px solid #e0e0e0;
+        }
+        
+        .stExpander > details {
+            background: #ffffff;
+            padding: 0.5rem;
+        }
+        
+        .stExpander > details > summary {
+            padding: 0.75rem 1rem;
+            font-weight: 600;
+            color: #1976d2;
+        }
+        
+        .stExpander > details > summary:hover {
+            color: #1565c0;
+        }
+        
+        /* Checkbox styling */
+        .stCheckbox > div {
+            background: #ffffff;
+            padding: 0.75rem 1rem;
+            border-radius: 8px;
+            margin-bottom: 0.5rem;
+            border: 1px solid #f0f0f0;
+        }
+        
+        .stCheckbox > div:hover {
+            background: rgba(25, 118, 210, 0.03);
+        }
+        
+        .stCheckbox label {
+            font-weight: 500;
+            color: #455a64;
+        }
+        
+        /* Radio button styling */
+        .stRadio > div {
+            background: #ffffff;
+            border-radius: 10px;
+            padding: 0.5rem;
+            overflow: hidden;
+            box-shadow: 0 2px 10px rgba(0, 0, 0, 0.05);
+            border: 1px solid #f0f0f0;
+        }
+        
+        .stRadio > div > div {
+            display: flex;
+            flex-wrap: wrap;
+            gap: 0.5rem;
+        }
+        
+        .stRadio > div > div > label {
+            background: #f5f7fa;
+            padding: 0.6rem 1.2rem;
+            border-radius: 30px;
+            transition: all 0.2s ease;
+            text-align: center;
+            font-weight: 500;
+            color: #546e7a;
+            border: 1px solid #e0e0e0;
+        }
+        
+        .stRadio > div > div > label:hover {
+            background: #e3f2fd;
+            color: #1976d2;
+            border-color: #bbdefb;
+        }
+        
+        .stRadio > div > div > label[data-baseweb="radio"] input:checked + div {
+            background: #1976d2;
+            color: white;
+            border-color: #1976d2;
+        }
+        
+        /* Generate button styling */
+        button[data-testid="baseButton-secondary"], 
+        button[data-testid="baseButton-primary"] {
+            background: linear-gradient(45deg, #1976d2, #2196f3);
+            color: white;
+            border: none;
+            border-radius: 30px;
+            padding: 0.85rem 1.5rem;
+            font-weight: 600;
+            font-size: 1.1rem;
+            letter-spacing: 0.5px;
+            transition: all 0.3s ease;
+            box-shadow: 0 4px 15px rgba(25, 118, 210, 0.25);
+            text-transform: uppercase;
+            display: flex;
+            align-items: center;
+            justify-content: center;
+            gap: 0.5rem;
+        }
+        
+        button[data-testid="baseButton-secondary"]:hover, 
+        button[data-testid="baseButton-primary"]:hover {
+            background: linear-gradient(45deg, #1565c0, #1976d2);
+            transform: translateY(-2px);
+            box-shadow: 0 6px 20px rgba(25, 118, 210, 0.35);
+        }
+        
+        /* Input labels */
+        .stTextArea label, .stTextInput label, .stFileUploader label {
+            font-weight: 600;
+            color: #455a64;
+            font-size: 1.05rem;
+            margin-bottom: 0.5rem;
+        }
+        
+        /* Section headers */
+        .stMarkdown h3 {
+            color: #1976d2;
+            font-weight: 600;
+            font-size: 1.3rem;
+            margin: 1.5rem 0 0.75rem 0;
+            padding-bottom: 0.5rem;
+            border-bottom: 2px solid rgba(25, 118, 210, 0.1);
+        }
+        
+        .stMarkdown h4 {
+            color: #455a64;
+            font-weight: 600;
+            font-size: 1.1rem;
+            margin: 1rem 0 0.5rem 0;
+        }
+        
+        /* Column layout improvements */
+        [data-testid="column"] {
+            background: #ffffff;
+            border-radius: 12px;
+            padding: 1.2rem;
+            box-shadow: 0 2px 15px rgba(0, 0, 0, 0.04);
+            border: 1px solid rgba(0, 0, 0, 0.05);
+            margin: 0 0.5rem;
+        }
+        
+        /* Success and error messages */
+        .stSuccess, .stInfo, .stError {
+            border-radius: 10px;
+            padding: 1rem;
+            margin: 1rem 0;
+            font-weight: 500;
+        }
+    </style>
+    """, unsafe_allow_html=True) 

lib/ai_writers/ai_writer_dashboard.py

@@ -0,0 +1,273 @@
+import streamlit as st
+from lib.utils.alwrity_utils import (essay_writer, ai_news_writer, ai_finance_ta_writer)
+
+from lib.ai_writers.ai_story_writer.story_writer import story_input_section
+from lib.ai_writers.ai_product_description_writer import write_ai_prod_desc
+from lib.ai_writers.ai_copywriter.copywriter_dashboard import copywriter_dashboard
+from lib.ai_writers.linkedin_writer import LinkedInAIWriter
+#from lib.content_planning_calender.content_planning_agents_alwrity_crew import ai_agents_content_planner
+from lib.ai_writers.ai_blog_writer.ai_blog_generator import ai_blog_writer_page
+from loguru import logger
+
+def list_ai_writers():
+    """Return a list of available AI writers with their metadata (no UI rendering)."""
+    return [
+        {
+            "name": "AI Blog Writer",
+            "icon": "📝",
+            "description": "Generate comprehensive blog posts from keywords, URLs, or uploaded content",
+            "category": "Content Creation",
+            "function": ai_blog_writer_page,
+            "path": "ai_blog_writer"
+        },
+        {
+            "name": "Story Writer",
+            "icon": "📚",
+            "description": "Create engaging stories and narratives with AI assistance",
+            "category": "Creative Writing",
+            "function": story_input_section,
+            "path": "story_writer"
+        },
+        {
+            "name": "Essay writer",
+            "icon": "✍️",
+            "description": "Generate well-structured essays on any topic",
+            "category": "Academic",
+            "function": essay_writer,
+            "path": "essay_writer"
+        },
+        {
+            "name": "Write News reports",
+            "icon": "📰",
+            "description": "Create professional news articles and reports",
+            "category": "Journalism",
+            "function": ai_news_writer,
+            "path": "news_writer"
+        },
+        {
+            "name": "Write Financial TA report",
+            "icon": "📊",
+            "description": "Generate technical analysis reports for financial markets",
+            "category": "Finance",
+            "function": ai_finance_ta_writer,
+            "path": "financial_writer"
+        },
+        {
+            "name": "AI Product Description Writer",
+            "icon": "🛍️",
+            "description": "Create compelling product descriptions that drive sales",
+            "category": "E-commerce",
+            "function": write_ai_prod_desc,
+            "path": "product_writer"
+        },
+        {
+            "name": "AI Copywriter",
+            "icon": "✒️",
+            "description": "Generate persuasive copy for marketing and advertising",
+            "category": "Marketing",
+            "function": copywriter_dashboard,
+            "path": "copywriter"
+        },
+        {
+            "name": "LinkedIn AI Writer",
+            "icon": "💼",
+            "description": "Create professional LinkedIn content that engages your network",
+            "category": "Professional",
+            "function": lambda: LinkedInAIWriter().run(),
+            "path": "linkedin_writer"
+        }
+    ]
+
+def get_ai_writers():
+    """Render the AI Writers dashboard UI with a professional, clickable card layout."""
+    logger.info("Initializing AI Writers Dashboard")
+    writers = list_ai_writers()
+    logger.info(f"Found {len(writers)} AI writers")
+
+    # Add custom CSS for a professional dashboard with VIBRANT clickable cards
+    st.markdown("""
+        <style>
+            /* Base UI improvements */
+            body, .main .block-container {
+                background: linear-gradient(135deg, #f0f4f8 0%, #e6eef7 100%) !important;
+                min-height: 100vh;
+                color: #2c3e50;
+                font-family: 'Helvetica Neue', sans-serif;
+            }
+            
+            /* Main layout improvements */
+            .main .block-container {
+                padding: 1.5rem 2rem 2rem 2rem !important;
+                max-width: 1200px;
+                margin: 0 auto;
+            }
+
+            /* Dashboard header */
+            .dashboard-header {
+                text-align: center;
+                margin-bottom: 2.5rem;
+                padding: 2rem 1.5rem;
+                background: linear-gradient(135deg, #ffffff 0%, #f5f7fa 100%);
+                border-radius: 16px;
+                box-shadow: 0 4px 20px rgba(0, 0, 0, 0.07);
+                border: 1px solid rgba(0, 0, 0, 0.04);
+            }
+            .dashboard-header h1 {
+                font-size: 2.6em;
+                font-family: 'Helvetica Neue', sans-serif;
+                font-weight: 700;
+                color: #1976d2;
+                margin-bottom: 0.5rem;
+            }
+            .dashboard-header p {
+                font-size: 1.15em;
+                color: #546e7a;
+                max-width: 700px;
+                margin: 0 auto;
+                line-height: 1.6;
+            }
+
+            /* Styling st.button to look like a clickable card - PREMIUM VIBRANT */
+            [data-testid="stVerticalBlock"] [data-testid="stButton"] > button {
+                /* Vivid Gradient Background - More saturated blue-purple */
+                background: linear-gradient(135deg, #8a2be2 0%, #4169e1 100%); 
+                color: #ffffff;
+                border: none;
+                padding: 1.8rem 1.5rem;
+                border-radius: 18px;
+                font-weight: 500;
+                font-size: 1rem;
+                font-family: 'Helvetica Neue', sans-serif;
+                transition: all 0.35s cubic-bezier(0.25, 0.8, 0.25, 1); /* Smoother, more premium transition */
+                box-shadow: 0 8px 20px rgba(77, 5, 232, 0.25), 0 2px 6px rgba(0, 0, 0, 0.15); /* Layered shadow for depth */
+                width: 100%;
+                height: 100%; 
+                min-height: 190px;
+                margin-bottom: 0;
+                text-align: left;
+                display: flex;
+                flex-direction: column;
+                align-items: flex-start;
+                justify-content: flex-start;
+                line-height: 1.5;
+                overflow: hidden;
+                transform: translateY(0); /* Starting position for hover animation */
+                position: relative; /* For pseudo-element effects */
+            }
+            
+            /* Subtle shine effect on cards */
+            [data-testid="stVerticalBlock"] [data-testid="stButton"] > button::after {
+                content: '';
+                position: absolute;
+                top: 0;
+                left: -50%;
+                width: 150%;
+                height: 100%;
+                background: linear-gradient(90deg, transparent, rgba(255,255,255,0.1), transparent);
+                transform: skewX(-20deg);
+                transition: 0.5s;
+                opacity: 0;
+            }
+            
+            /* Dynamic hover effects with gradient shift */
+            [data-testid="stVerticalBlock"] [data-testid="stButton"] > button:hover {
+                background: linear-gradient(135deg, #4169e1 0%, #8a2be2 100%); /* Reverse gradient on hover */
+                transform: translateY(-8px) scale(1.05); /* More dramatic lift */
+                box-shadow: 0 15px 30px rgba(77, 5, 232, 0.4), 0 5px 15px rgba(0, 0, 0, 0.2); /* Deeper shadow */
+                color: #ffffff;
+            }
+            
+            /* Animate shine on hover */
+            [data-testid="stVerticalBlock"] [data-testid="stButton"] > button:hover::after {
+                left: 100%;
+                opacity: 1;
+            }
+            
+            [data-testid="stVerticalBlock"] [data-testid="stButton"] > button:hover > div > p strong {
+                color: #ffffff; /* Bright white on hover */
+                text-shadow: 0 0 15px rgba(255,255,255,0.5); /* Glow effect on hover */
+            }
+
+            /* Target the paragraph generated by Streamlit inside the button */
+            [data-testid="stVerticalBlock"] [data-testid="stButton"] > button > div > p {
+                margin: 0;
+                line-height: 1.5;
+                color: rgba(255, 255, 255, 0.9); /* Slightly dimmed base text */
+            }
+
+            /* Icon (first line/element) - MORE PROMINENT */
+            [data-testid="stVerticalBlock"] [data-testid="stButton"] > button > div > p::first-line {
+                font-size: 2.3em; /* Larger icon */
+                line-height: 1.2;
+                display: block;
+                margin-bottom: 1rem;
+                color: #ffffff;
+                text-shadow: 0 0 10px rgba(255,255,255,0.4); /* Light glow effect */
+            }
+
+            /* Title (strong tag from markdown) - ENHANCED CONTRAST */
+            [data-testid="stVerticalBlock"] [data-testid="stButton"] > button > div > p strong {
+                font-size: 1.4em; /* Larger title */
+                font-weight: 700; /* Bolder */
+                color: #ffffff;
+                display: block;
+                margin: 0.7rem 0;
+                text-shadow: 1px 1px 4px rgba(0,0,0,0.3); /* Stronger shadow for better contrast */
+                letter-spacing: 0.5px; /* Slight letter spacing for premium feel */
+            }
+
+             /* Description - IMPROVED CONTRAST */
+             [data-testid="stVerticalBlock"] [data-testid="stButton"] > button > div > p {
+                font-size: 1rem; /* Slightly larger for readability */
+                color: rgba(255, 255, 255, 0.95); /* Better contrast */
+                text-shadow: 0 1px 2px rgba(0,0,0,0.1); /* Subtle shadow for text */
+             }
+
+            /* Column adjustments for consistent card height */
+            [data-testid="column"] {
+                 display: flex;
+                 flex-direction: column;
+                 gap: 1.5rem; /* Consistent gap */
+            }
+
+            /* Hide Streamlit default title */
+            .stApp > header {
+                visibility: hidden;
+            }
+        </style>
+    """, unsafe_allow_html=True)
+
+    # Dashboard header
+    st.markdown("""
+        <div class="dashboard-header">
+            <h1>🚀 AI Content Creation Suite</h1>
+            <p>Welcome! Select the perfect AI writer tool from the options below to start creating amazing content.</p>
+        </div>
+    """, unsafe_allow_html=True)
+
+    # Create columns for the grid layout
+    cols = st.columns(3)
+    
+    # Render buttons styled as cards for each writer
+    for idx, writer in enumerate(writers):
+        with cols[idx % 3]:
+            # Prepare the button label using simple Markdown with newlines
+            button_label = f"{writer['icon']}\n**{writer['name']}**\n{writer['description']}" 
+            
+            if st.button(
+                button_label, 
+                key=f"writer_{writer['path']}",
+                help=f"Click to use the {writer['name']}", # More specific help text
+                use_container_width=True,
+            ):
+                logger.info(f"Selected writer: {writer['name']} with path: {writer['path']}")
+                st.session_state.selected_writer = writer
+                st.query_params["writer"] = writer['path']
+                logger.info(f"Updated query params with writer: {writer['path']}")
+                st.rerun()
+
+    logger.info("Finished rendering AI Writers Dashboard")
+    # Return writers list, though it's not strictly needed if only rendering UI
+    return writers
+
+# Remove the old ai_writers function since it's now integrated into get_ai_writers

lib/ai_writers/blog_from_google_serp.py

@@ -13,26 +13,114 @@ logger.add(sys.stdout,
 from ..gpt_providers.text_generation.main_text_generation import llm_text_gen
 
 
-def write_blog_google_serp(search_keyword, search_results):
-    """Combine the given online research and GPT blog content"""
-    prompt = f"""
-        As expert Creative Content writer,
-        I want you to write highly detailed blog post, that explores {search_keyword} and also include 5 FAQs.
-
-        I want the post to offer unique insights, relatable examples, and a fresh perspective on the topic.
-        Here are some Google search results to spark your creativity on {search_keyword}:
-        \n\n
-        \"\"\"{search_results}\"\"\"
-        """
+def write_blog_google_serp(keywords, search_results, blog_params=None):
+    """
+    Write a blog post using search results from Google SERP.
     
-    logger.info("Generating blog and FAQs from Google web search results.")
+    Args:
+        keywords (str): The keywords or topic for the blog
+        search_results (dict): Results from Google SERP search
+        blog_params (dict, optional): Blog content characteristics:
+            - blog_length: Target word count
+            - blog_tone: Content tone
+            - blog_demographic: Target audience
+            - blog_type: Type of blog post
+            - blog_language: Language for the blog
+    
+    Returns:
+        str: The generated blog content in markdown format
+    """
+    # If no blog parameters are provided, use defaults
+    if blog_params is None:
+        blog_params = {
+            "blog_length": 2000,
+            "blog_tone": "Professional",
+            "blog_demographic": "Professional",
+            "blog_type": "Informational",
+            "blog_language": "English"
+        }
+    
+    # Ensure all parameters have default values
+    blog_length = blog_params.get("blog_length", 2000)
+    blog_tone = blog_params.get("blog_tone", "Professional")
+    blog_demographic = blog_params.get("blog_demographic", "Professional")
+    blog_type = blog_params.get("blog_type", "Informational")
+    blog_language = blog_params.get("blog_language", "English")
+    
+    logger.info(f"Generating {blog_tone} {blog_type} blog of {blog_length} words for {blog_demographic} audience in {blog_language}")
     
     try:
-        response = llm_text_gen(prompt)
+        # Build a prompt based on search results
+        prompt_parts = [
+            f"You are a specialized blog writer who writes in a {blog_tone} tone for a {blog_demographic} audience. "
+            f"Create a {blog_type} blog post that is approximately {blog_length} words in {blog_language}.",
+            f"The blog should be about: {keywords}",
+            "Use the following search results to create an informative, accurate, and well-structured blog post:"
+        ]
+        
+        # Add organic search results
+        if 'organic' in search_results:
+            prompt_parts.append("\nSearch results:")
+            for i, result in enumerate(search_results['organic'][:5], 1):
+                title = result.get('title', 'No title')
+                snippet = result.get('snippet', 'No snippet')
+                prompt_parts.append(f"{i}. {title}: {snippet}")
+        
+        # Add people also ask questions if available
+        if 'peopleAlsoAsk' in search_results and search_results['peopleAlsoAsk']:
+            prompt_parts.append("\nPeople also ask:")
+            for i, question in enumerate(search_results['peopleAlsoAsk'][:3], 1):
+                q_text = question.get('question', 'No question')
+                q_answer = question.get('answer', {}).get('snippet', 'No answer')
+                prompt_parts.append(f"{i}. Q: {q_text}\n   A: {q_answer}")
+        
+        # Add related searches if available
+        if 'relatedSearches' in search_results and search_results['relatedSearches']:
+            related = [item.get('query', '') for item in search_results['relatedSearches'][:5]]
+            if related:
+                prompt_parts.append("\nRelated topics to consider including:")
+                prompt_parts.append(", ".join(related))
+        
+        # Add specific instructions based on blog_type
+        type_instructions = {
+            "Informational": "Focus on providing factual information and educating the reader about the topic.",
+            "How-to": "Include clear step-by-step instructions with actionable advice.",
+            "List": "Organize content into a numbered or bulleted list of points, tips, or examples.",
+            "Review": "Provide balanced analysis with pros and cons, and a clear conclusion or recommendation.",
+            "Tutorial": "Include detailed instructions with examples and explanations for each step.",
+            "Opinion": "Present a clear perspective supported by evidence, while acknowledging other viewpoints."
+        }
+        
+        prompt_parts.append(f"\nSpecific instructions: {type_instructions.get(blog_type, '')}")
+        
+        # Add formatting instructions
+        prompt_parts.append("""
+Format the blog post in markdown with:
+- A compelling title (# Title)
+- An introduction that hooks the reader
+- Well-structured sections with appropriate headings (## Headings)
+- Bullet points or numbered lists where appropriate
+- A conclusion summarizing key points
+- Make sure all content is accurate, informative, and adds value to the reader.
+- Include 2-3 subheadings to organize the content well.
+- Be concise and to the point.
+- Write in an engaging, reader-friendly style.
+- Avoid using phrases like "According to the search results" or "Based on the information provided."
+- Present information as direct knowledge.
+""")
+        
+        # Combine all prompt parts
+        full_prompt = "\n".join(prompt_parts)
+        
+        # Generate the blog content using the prompt
+        response = llm_text_gen(full_prompt)
+        
+        # Return the generated content
         return response
+    
     except Exception as err:
-        logger.error(f"Exit: Failed to get response from LLM: {err}")
-        exit(1)
+        logger.error(f"Error generating blog from search results: {err}")
+        raise
 
 
 def improve_blog_intro(blog_content, blog_intro):

lib/ai_writers/long_form_ai_writer.py

@@ -53,216 +53,177 @@ def generate_with_retry(prompt, system_prompt=None):
         return False
 
 
-def long_form_generator(content_keywords):
+def long_form_generator(keywords, search_params=None, blog_params=None):
     """
-    Write long form content using prompt chaining and iterative generation.
+    Generate a long-form blog post based on the given keywords
     
-    Parameters:
-        content_keywords (str): The main keywords or topic for the long-form content.
+    Args:
+        keywords (str): Topic or keywords for the blog post
+        search_params (dict, optional): Search parameters for research
+        blog_params (dict, optional): Blog content characteristics
+    """
+    
+    # Initialize default parameters if not provided
+    if blog_params is None:
+        blog_params = {
+            "blog_length": 3000,  # Default longer for long-form content
+            "blog_tone": "Professional",
+            "blog_demographic": "Professional",
+            "blog_type": "Informational",
+            "blog_language": "English"
+        }
+    else:
+        # Ensure we have a higher word count for long-form content
+        if blog_params.get("blog_length", 0) < 2500:
+            blog_params["blog_length"] = max(3000, blog_params.get("blog_length", 0))
+    
+    # Extract parameters with defaults
+    blog_length = blog_params.get("blog_length", 3000)
+    blog_tone = blog_params.get("blog_tone", "Professional")
+    blog_demographic = blog_params.get("blog_demographic", "Professional")
+    blog_type = blog_params.get("blog_type", "Informational")
+    blog_language = blog_params.get("blog_language", "English")
+    
+    st.subheader(f"Long-form {blog_type} Blog ({blog_length}+ words)")
+    
+    with st.status("Generating comprehensive long-form content...", expanded=True) as status:
+        # Step 1: Generate outline
+        status.update(label="Creating detailed content outline...")
+        
+        # Use a customized prompt based on the blog parameters
+        outline_prompt = f"""
+        As an expert content strategist writing in a {blog_tone} tone for {blog_demographic} audience,
+        create a detailed outline for a comprehensive {blog_type} blog post about "{keywords}" 
+        that will be approximately {blog_length} words in {blog_language}.
+        
+        The outline should include:
+        1. An engaging headline
+        2. 5-7 main sections with descriptive headings
+        3. 2-3 subsections under each main section
+        4. Key points to cover in each section
+        5. Ideas for relevant examples or case studies
+        6. Suggestions for data points or statistics to include
+        
+        Format the outline in markdown with proper headings and bullet points.
+        """
+        
+        try:
+            outline = llm_text_gen(outline_prompt)
+            st.markdown("### Content Outline")
+            st.markdown(outline)
+            status.update(label="Outline created successfully ✓")
+            
+            # Step 2: Research the topic using the search parameters
+            status.update(label="Researching topic details...")
+            research_results = research_topic(keywords, search_params)
+            status.update(label="Research completed ✓")
+            
+            # Step 3: Generate the full content
+            status.update(label=f"Writing {blog_length}+ word {blog_tone} {blog_type} content...")
+            
+            full_content_prompt = f"""
+            You are a professional content writer who specializes in {blog_type} content with a {blog_tone} tone 
+            for {blog_demographic} audiences. Write a comprehensive, in-depth blog post in {blog_language} about:
+            
+            "{keywords}"
+            
+            Use this outline as your structure:
+            {outline}
+            
+            And incorporate these research findings where relevant:
+            {research_results}
+            
+            The blog post should:
+            - Be approximately {blog_length} words
+            - Include an engaging introduction and strong conclusion
+            - Use appropriate subheadings for all sections in the outline
+            - Include examples, data points, and actionable insights
+            - Be formatted in markdown with proper headings, bullet points, and emphasis
+            - Maintain a {blog_tone} tone throughout
+            - Address the needs and interests of a {blog_demographic} audience
+            
+            Do not include phrases like "according to research" or "based on the outline" in your content.
+            """
+            
+            full_content = llm_text_gen(full_content_prompt)
+            status.update(label="Long-form content generated successfully! ✓", state="complete")
+            
+            # Display the full content
+            st.markdown("### Your Complete Long-form Blog Post")
+            st.markdown(full_content)
+            
+            return full_content
+            
+        except Exception as e:
+            status.update(label=f"Error generating long-form content: {str(e)}", state="error")
+            st.error(f"Failed to generate long-form content: {str(e)}")
+            return None
+    
+def research_topic(keywords, search_params=None):
+    """
+    Research a topic using search parameters and return a summary
+    
+    Args:
+        keywords (str): Topic to research
+        search_params (dict, optional): Search parameters
         
     Returns:
-        str: The generated long-form content.
+        str: Research summary
     """
-    with st.status("Start Writing Long Form Article, Hold my Beer..", expanded=True) as status:
-        # Read the main_config to define tone, character, personality of the content to be generated.
-        try:
-            status.update(label=f"Starting to write content on {content_keywords}.")
-            logger.info(f"Starting to write content on {content_keywords}.")
-            # Define persona and writing guidelines
-            content_tone, target_audience, content_type, content_language, output_format, content_length = read_return_config_section('blog_characteristics')
-        except Exception as err:
-            logger.error(f"Failed to Read config params from main_config: {err}")
-            st.error(f"Failed to Read config params from main_config: {err}")
-            return False
+    # Display a placeholder for research results
+    placeholder = st.empty()
+    placeholder.info("Researching topic... Please wait.")
     
-        try:
-            filepath = os.path.join(os.environ["PROMPTS_DIR"], "long_form_ai_writer.prompts")
-            status.update(label=f"Reading Prompts from {filepath}.")
-            # Check if file exists
-            if not os.path.exists(filepath):
-                raise FileNotFoundError(f"File {filepath} does not exist")
-            with open(filepath, 'r') as file:
-                prompts = yaml.safe_load(file)
-        except Exception as err:
-            st.error(f"Exit: Failed to read prompts from {filepath}: {err}")
-            logger.error(f"Exit: Failed to read prompts from {filepath}: {err}")
-            exit(1)
-    
-        writing_guidelines = prompts.get('writing_guidelines').format(
-            content_language=content_language,
-            content_tone=content_tone,
-            content_type=content_type,
-            output_format=output_format,
-            content_keywords=content_keywords,
-            target_audience=target_audience
-        )
-    
-        content_title = prompts.get('content_title').format(
-            content_language=content_language,
-            content_keywords=content_keywords,
-            target_audience=target_audience
+    try:
+        from .keywords_to_blog_streamlit import do_tavily_ai_search
+        
+        # Use provided search params or defaults
+        if search_params is None:
+            search_params = {
+                "max_results": 10, 
+                "search_depth": "advanced",
+                "time_range": "year"
+            }
+        
+        # Conduct research using Tavily
+        tavily_results = do_tavily_ai_search(
+            keywords,
+            max_results=search_params.get("max_results", 10),
+            search_depth=search_params.get("search_depth", "advanced"),
+            include_domains=search_params.get("include_domains", []),
+            time_range=search_params.get("time_range", "year")
         )
         
-        content_outline = prompts.get('content_outline').format(
-            content_language=content_language,
-            content_title='{content_title}',
-            content_type=content_type,
-            target_audience=target_audience
-        )
+        # Extract research data
+        research_data = ""
+        if tavily_results and len(tavily_results) == 3:
+            results, titles, answer = tavily_results
+            
+            if answer and len(answer) > 50:
+                research_data += f"Summary: {answer}\n\n"
+            
+            if results and 'results' in results and len(results['results']) > 0:
+                research_data += "Key Sources:\n"
+                for i, result in enumerate(results['results'][:7], 1):
+                    title = result.get('title', 'Untitled Source')
+                    content_snippet = result.get('content', '')[:300] + "..."
+                    research_data += f"{i}. {title}\n{content_snippet}\n\n"
         
-        starting_prompt = prompts.get('starting_prompt').format(
-            content_language=content_language,
-            content_title='{content_title}',
-            content_outline='{content_outline}',
-            writing_guidelines=writing_guidelines
-        )
+        # If research data is empty or too short, provide a generic response
+        if not research_data or len(research_data) < 100:
+            research_data = f"No specific research data found for '{keywords}'. Please provide more specific information in your content."
         
-        continuation_prompt = prompts.get('continuation_prompt').format(
-            content_language=content_language,
-            content_title='{content_title}',
-            content_outline='{content_outline}',
-            content_text='{content_text}',
-            web_research_result='{web_research_result}',
-            writing_guidelines=writing_guidelines
-        )
-    
-        # Do SERP web research for given keywords to generate title and outline.
-        web_research_result, g_titles = do_google_serp_search(content_keywords)
-    
-        # Generate prompts
-        try:
-            content_title = generate_with_retry(content_title.format(web_research_result=web_research_result))
-            logger.info(f"The title of the content is: {content_title}")
-            status.update(label=f"The title of the content is: {content_title}")
-        except Exception as err:
-            logger.error(f"Content title Generation Error: {err}")
-            return False
+        placeholder.success("Research completed successfully!")
+        return research_data
         
-        try:
-            content_outline = generate_with_retry(content_outline.format(
-                content_title=content_title, 
-                web_research_result=web_research_result))
-            logger.info(f"The content Outline is: {content_outline}\n\n")
-            status.update(label=f"Completed with Content Outline.")
-        except Exception as err:
-            logger.error(f"Failed to generate content outline: {err}")
-            return False
-    
-        try:
-            status.update(label=f"Do web research with Tavily to provide context for content creation.")
-            logger.info("Do web research with Tavily to provide context for content creation.")
-            # Do Metaphor/Exa AI search.
-            table_data = []
-            web_research_result, m_titles, t_titles = do_tavily_ai_search(content_keywords, max_results=5)
-            for item in web_research_result.get("results"):
-                title = item.get("title", "")
-                snippet = item.get("content", "")
-                table_data.append([title, snippet])
-            web_research_result = table_data
-        except Exception as err:
-            logger.error(f"Failed to do Tavily AI search: {err}")
-            st.error(f"Failed to do Tavily AI search: {err}")
-            return False
-    
-        try:
-            starting_draft = generate_with_retry(starting_prompt.format(
-                    content_title=content_title, 
-                    content_outline=content_outline,
-                    web_research_result=web_research_result,
-                    writing_guidelines=writing_guidelines))
-        except Exception as err:
-            st.error(f"Failed to Generate Starting draft: {err}")
-            logger.error(f"Failed to Generate Starting draft: {err}")
-            return False
-        
-        try:
-            logger.info(f"Starting to write on the outline introduction.")
-            draft = starting_draft
-            continuation = generate_with_retry(continuation_prompt.format(
-                    content_title=content_title,
-                    content_outline=content_outline,
-                    content_text=draft,
-                    web_research_result=web_research_result,
-                    writing_guidelines=writing_guidelines))
-        except Exception as err:
-            logger.error(f"Failed to write the initial draft: {err}")
-            return False
-    
-        # Add the continuation to the initial draft, keep building the story until we see 'IAMDONE'
-        try:
-            draft += '\n\n' + continuation
-        except Exception as err:
-            logger.error(f"Failed as: {err} and {continuation}")
-            return False
-    
-        logger.info(f"Writing in progress... Current draft length: {len(draft)} characters")
-        status.update(label=f"Writing in progress... Current draft length: {len(draft)} characters")
-        search_terms = f"""
-            I will provide you with content outline below, your task is to read the outline & return 8 google search keywords.
-            Your response will be used to do web research for writing on the given outline.
-            Do not explain your response, provide 8 google search sentences encompassing the given content outline.
-            Important: Provide the search term results as comma separated values.\n\n
-            Content Outline:\n
-            '{content_outline}'
-            """
-        search_words = generate_with_retry(search_terms)
-        status.update(label=f"Search terms from written draft: {search_words}")
-        
-        while 'IAMDONE' not in continuation:
-            #web_research_result, m_titles = do_metaphor_ai_research(content_keywords)
-            str_list = re.split(r',\s*', search_words)
-            # Strip quotes from each element 
-            str_list = [s.strip('\'"') for s in str_list]
-
-#            for search_term in str_list:
-#                web_research_result, m_titles, t_titles = do_tavily_ai_search(search_term, max_results=5)
-#                status.update(label=f"Search terms from written draft: {search_term}")
-#                for item in web_research_result.get("results"):
-#                    title = item.get("title", "")
-#                    snippet = item.get("content", "")
-#                    table_data.append([title, snippet])
-#                web_research_result = table_data
-
-            try:
-                continuation = generate_with_retry(continuation_prompt.format(
-                            content_title=content_title,
-                            content_outline=content_outline, 
-                            content_text=draft, 
-                            web_research_result=web_research_result,
-                            writing_guidelines=writing_guidelines))
-        
-                draft += '\n\n' + continuation
-                logger.info(f"Writing in progress... Current draft length: {len(draft)} characters")
-                status.update(label=f"Writing in progress... Current draft length: {len(draft)} characters")
-                # At this point, the context is little stale. We should more web research on
-                # related queries as per the content outline, to augment the LLM context.
-            except Exception as err:
-                st.error(f"Failed to continually write long-form content: {err}")
-                logger.error(f"Failed to continually write the Essay: {err}")
-                return False
-        
-        # Remove 'IAMDONE' and print the final story
-        final = draft.replace('IAMDONE', '').strip()
-        status.update(label="Success: Finished writing Long form content.")
-
-#        # In long content sending the whole content for each content metadata is expensive.
-#        # https://ai.google.dev/gemini-api/docs/caching?lang=python
-#        #blog_title, blog_meta_desc, blog_tags, blog_categories = get_blog_metadata_longform(final)
-#        blog_categories = get_blog_metadata_longform(final)
-#        print("\n\n-----{blog_categories}------\n\n")
-#
-#        status.update(label="Success: Finished with Title, Meta Description, Tags, categories")
-#        generated_image_filepath = None
-#        # TBD: Save the blog content as a .md file. Markdown or HTML ?
-#        save_blog_to_file(final, blog_title, blog_meta_desc, blog_tags, blog_categories, generated_image_filepath)
-        
-        logger.info(f"\n{final}\n\n")
-
-        logger.info(f"\n\n ################ Finished writing Blog for : {content_keywords} #################### \n")
-    with st.expander("**Click to View the final content draft:**"):
-        st.markdown(f"\n{final}\n\n")
-    
-    return final
+    except Exception as e:
+        placeholder.error(f"Research failed: {str(e)}")
+        return f"Unable to gather research for '{keywords}'. Please continue with the content based on your knowledge."
+    finally:
+        # Remove the placeholder after a short delay
+        import time
+        time.sleep(1)
+        placeholder.empty()
 
 
 def generate_long_form_content(content_keywords):

lib/ai_writers/twitter_writers/twitter_dashboard.py

@@ -2,9 +2,116 @@ import streamlit as st
 import streamlit.components.v1 as components
 from typing import Dict, List
 import json
+import base64
 
 from .tweet_generator import smart_tweet_generator
 
+def add_bg_from_base64(base64_string):
+    """Add background image using base64 string."""
+    return f'''
+    <style>
+        .stApp {{
+            background-image: url("data:image/png;base64,{base64_string}");
+            background-size: cover;
+            background-position: center;
+            background-repeat: no-repeat;
+            background-attachment: fixed;
+        }}
+        
+        /* Enhanced styling for containers */
+        .element-container, .stMarkdown, .stButton {{
+            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."""
     return {
@@ -127,13 +234,13 @@ 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 style='padding: 20px; border-radius: 10px; background-color: #f0f2f6; margin-bottom: 20px;'>
-                <h3 style='margin: 0;'>{feature['icon']} {feature['name']}</h3>
-                <p style='margin: 10px 0;'>{feature['description']}</p>
-                <span style='background-color: {'#4CAF50' if feature['status'] == 'active' else '#ffd700'}; 
-                            padding: 5px 10px; border-radius: 15px; font-size: 0.8em;'>
+            <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>
@@ -150,23 +257,43 @@ def render_category_section(category: Dict) -> None:
     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."""
-    # Header
-    st.title("🐦 Twitter AI Writer Dashboard")
+    # Add space-themed background
+    st.markdown(add_bg_from_base64(get_space_background()), unsafe_allow_html=True)
+    
+    # Enhanced Header with gradient text
     st.markdown("""
-        Welcome to your all-in-one Twitter content creation and management platform. 
-        Explore our AI-powered tools to enhance your Twitter marketing strategy.
-    """)
+        <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 for different sections
+    # Create tabs with enhanced styling
     tab1, tab2, tab3 = st.tabs(["🎯 Quick Actions", "📊 Analytics", "⚙️ Settings"])
 
     with tab1:
-        st.markdown("### 🚀 Quick Actions")
+        st.markdown("<h2 style='color: #ffffff;'>🚀 Quick Actions</h2>", unsafe_allow_html=True)
         col1, col2, col3 = st.columns(3)
         
         with col1:
@@ -199,11 +326,29 @@ def run_dashboard():
             if st.button(f"🚀 Launch {category['features'][0]['name']}", use_container_width=True):
                 category["features"][0]["function"]()
 
-    # Footer
+    # Enhanced Footer
     st.markdown("---")
     st.markdown("""
-        <div style='text-align: center;'>
-            <p>Need help? Check out our <a href='#'>documentation</a> or <a href='#'>contact support</a></p>
+        <div style='text-align: center; padding: 20px; background: rgba(0, 0, 0, 0.5); border-radius: 10px;'>
+            <p style='color: #ffffff; margin-bottom: 10px;'>Need assistance? We're here to help!</p>
+            <div style='display: flex; justify-content: center; gap: 20px;'>
+                <a href='#' style='
+                    text-decoration: none;
+                    color: #1DA1F2;
+                    background: rgba(255, 255, 255, 0.1);
+                    padding: 8px 20px;
+                    border-radius: 20px;
+                    transition: all 0.3s ease;
+                '>📚 Documentation</a>
+                <a href='#' style='
+                    text-decoration: none;
+                    color: #1DA1F2;
+                    background: rgba(255, 255, 255, 0.1);
+                    padding: 8px 20px;
+                    border-radius: 20px;
+                    transition: all 0.3s ease;
+                '>💬 Contact Support</a>
+            </div>
         </div>
     """, unsafe_allow_html=True)
 

lib/gpt_providers/text_generation/main_text_generation.py

@@ -34,16 +34,53 @@ def llm_text_gen(prompt, system_prompt=None, json_struct=None):
         logger.debug(f"[llm_text_gen] Prompt length: {len(prompt)} characters")
         
         try:
-            # Read the config param to create system instruction for the LLM.
-            gpt_provider, model, temperature, max_tokens, top_p, n, fp = read_return_config_section('llm_config')
-            blog_tone, blog_demographic, blog_type, blog_language, \
-            blog_output_format, blog_length = read_return_config_section('blog_characteristics')
+            # Set default values for LLM parameters
+            gpt_provider = "google"
+            model = "gemini-1.5-flash-latest"
+            temperature = 0.7
+            max_tokens = 4000
+            top_p = 0.9
+            n = 1
+            fp = 16
             
-            logger.debug(f"[llm_text_gen] Config loaded successfully - Provider: {gpt_provider}, Model: {model}")
+            # Default blog characteristics
+            blog_tone = "Professional"
+            blog_demographic = "Professional"
+            blog_type = "Informational"
+            blog_language = "English"
+            blog_output_format = "markdown"
+            blog_length = 2000
+            
+            # Try to read values from config, but keep defaults if any key is missing
+            try:
+                # Read LLM config
+                llm_config = read_return_config_section('llm_config')
+                if llm_config and len(llm_config) >= 4:
+                    gpt_provider = llm_config[0] if llm_config[0] else gpt_provider
+                    model = llm_config[1] if llm_config[1] else model
+                    temperature = llm_config[2] if llm_config[2] else temperature
+                    max_tokens = llm_config[3] if llm_config[3] else max_tokens
+                    # Use default values for top_p, n, fp if they're not in the config
+                logger.debug(f"[llm_text_gen] LLM Config loaded: Provider={gpt_provider}, Model={model}, Temp={temperature}")
+            except Exception as err:
+                logger.warning(f"[llm_text_gen] Couldn't load LLM config completely, using defaults where needed: {err}")
+                
+            try:
+                # Read blog characteristics
+                blog_chars = read_return_config_section('blog_characteristics')
+                if blog_chars and len(blog_chars) >= 6:
+                    blog_tone = blog_chars[0] if blog_chars[0] else blog_tone
+                    blog_demographic = blog_chars[1] if blog_chars[1] else blog_demographic
+                    blog_type = blog_chars[2] if blog_chars[2] else blog_type
+                    blog_language = blog_chars[3] if blog_chars[3] else blog_language
+                    blog_output_format = blog_chars[4] if blog_chars[4] else blog_output_format
+                    blog_length = blog_chars[5] if blog_chars[5] else blog_length
+                logger.debug(f"[llm_text_gen] Blog characteristics loaded: Tone={blog_tone}, Type={blog_type}")
+            except Exception as err:
+                logger.warning(f"[llm_text_gen] Couldn't load blog characteristics completely, using defaults where needed: {err}")
             
         except Exception as err:
-            logger.error(f"[llm_text_gen] Error reading config params: {err}")
-            raise err
+            logger.warning(f"[llm_text_gen] Using default settings due to config read error: {err}")
 
         # Construct the system prompt with the sidebar config params if no custom system_prompt is provided
         if system_prompt is None:
@@ -110,9 +147,13 @@ def llm_text_gen(prompt, system_prompt=None, json_struct=None):
             except Exception as err:
                 logger.error(f"Failed to get response from DeepSeek: {err}")
                 raise err
+        else:
+            logger.warning(f"Unknown provider '{gpt_provider}', falling back to Google Gemini")
+            response = gemini_text_response(prompt, temperature, top_p, n, max_tokens, system_instructions)
+            return response
 
     except Exception as err:
-        logger.error(f"Failed to read LLM parameters: {err}")
+        logger.error(f"Failed to generate text: {err}")
         raise
 
 

lib/utils/alwrity_utils.py

@@ -1,272 +1,25 @@
 import re
 import os
 import PyPDF2
-import tiktoken
 import openai
 import streamlit as st
 import tempfile
 from loguru import logger
 
-from lib.ai_web_researcher.gpt_online_researcher import gpt_web_researcher
-from lib.ai_writers.keywords_to_blog_streamlit import write_blog_from_keywords
-from lib.ai_writers.speech_to_blog.main_audio_to_blog import generate_audio_blog
-from lib.ai_writers.long_form_ai_writer import long_form_generator
+
 from lib.ai_writers.ai_news_article_writer import ai_news_generation
-#from lib.ai_writers.ai_agents_crew_writer import ai_agents_writers
 from lib.ai_writers.ai_financial_writer import write_basic_ta_report
 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
 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.web_url_ai_writer import blog_from_url
-from lib.ai_writers.image_ai_writer import blog_from_image
 from lib.ai_writers.ai_essay_writer import ai_essay_generator
 from lib.gpt_providers.text_to_image_generation.main_generate_image_from_prompt import generate_image
-from lib.utils.voice_processing import record_voice
 #from lib.content_planning_calender.content_planning_agents_alwrity_crew import ai_agents_content_planner
 from lib.gpt_providers.text_generation.main_text_generation import llm_text_gen
 
 
-def is_youtube_link(text):
-    if text is not None:
-        youtube_regex = re.compile(r'(https?://)?(www\.)?(youtube|youtu|youtube-nocookie)\.(com|be)/(watch\?v=|embed/|v/|.+\?v=)?([^&=%\?]{11})')
-        return youtube_regex.match(text)
-
-
-def is_web_link(text):
-    if text is not None:
-        web_regex = re.compile(r'(https?://)?(www\.)?[-a-zA-Z0-9@:%._\+~#=]{1,256}\.[a-zA-Z0-9()]{1,6}\b([-a-zA-Z0-9()@:%_\+.~#?&//=]*)')
-        return web_regex.match(text)
-
-
-def process_input(input_text, uploaded_file):
-    if input_text and is_youtube_link(input_text):
-        if input_text.startswith("https://www.youtube.com/") or input_text.startswith("http://www.youtube.com/"):
-            return "youtube_url"
-        else:
-            st.error("Invalid YouTube URL. Please enter a valid URL.")
-            return None
-
-    elif input_text and is_web_link(input_text):
-        return "web_url"
-    
-    elif input_text:
-        return "keywords"
-    
-    if uploaded_file is not None:
-        file_details = {"filename": uploaded_file.name, "filetype": uploaded_file.type}
-        st.write(file_details)
-        if uploaded_file.type.startswith("text/"):
-            content = uploaded_file.read().decode("utf-8")
-            st.text(content)
-
-        elif uploaded_file.type == "application/pdf":
-            return "PDF_file"
-
-        elif uploaded_file.type in ["application/vnd.openxmlformats-officedocument.wordprocessingml.document", "application/msword"]:
-            st.write("Word document uploaded. Add your DOCX processing logic here.")
-        elif uploaded_file.type.startswith("image/"):
-            st.image(uploaded_file)
-            return "image_file"
-        elif uploaded_file.type.startswith("audio/"):
-            st.audio(uploaded_file)
-            return "audio_file"
-        elif uploaded_file.type.startswith("video/"):
-            st.video(uploaded_file)
-            return "video_file"
-    return None
-
-
-def blog_from_keyword():
-    """ Input blog keywords, research and write a factual blog."""
-    st.header("Blog Content Writer")
-    col1, col2, col3 = st.columns([2, 1.5, 0.5])
-    with col1:
-        user_input = st.text_area('**👇Enter Keywords/Title/YouTube Link/Web URLs**',
-                                  help='Provide keywords, titles, YouTube links, or web URLs to generate content.',
-                                  placeholder="""Write Blog From:
-        - Keywords/Blog Title: Provide keywords to web research & write blog.
-        - Attach file: Attach Text, Audio, Video, Image file to blog on.
-        - YouTube Link: Provide a YouTube video link to convert into blog.
-        - Web URLs: Provide web URL to write similar blog on.
-        - Provide Local folder location with your documents to use for content creation.""")
-        
-    with col2:
-        uploaded_file = st.file_uploader("**👇Attach files (Audio, Video, Image, Document)**",
-                                         type=["txt", "pdf", "docx", "jpg", "jpeg", "png", "mp3", "wav", "mp4", "mkv", "avi"],
-                                         help='Attach files such as audio, video, images, or documents.')
-    with col3:
-        audio_input = record_voice()
-        if audio_input:
-            st.info(audio_input)
-
-    # Validate the provided folder path
-    #st.info("🚨 Currently supported file formats are: PDF, plain text, CSV, Excel, Markdown, PowerPoint, and Word documents.")
-
-    temp_file_path = None
-    if uploaded_file is not None:
-        # Save the uploaded file to a temporary file
-        with tempfile.NamedTemporaryFile(delete=False, suffix=uploaded_file.name) as temp_file:
-            temp_file.write(uploaded_file.read())
-            temp_file_path = temp_file.name
-
-    content_type = st.radio("**👇Select content type:**", ["Normal-length content", "Long-form content", "Experimental - AI Agents team"])
-
-    # Add an expandable section for advanced writing options
-    with st.expander("Advanced Writing Options", expanded=False):
-        # Option 1: Select content type
-        content_type = st.radio("**👇 Select content type:**", 
-                            ["Normal-length content", "Long-form content", "Experimental - AI Agents team"])
-
-        # Option 2: Checkbox for 'Create SEO tags' (Checked by default)
-        create_seo_tags = st.checkbox('Create SEO tags', value=True, 
-                                  help='Generate json-ld schema, Twitter, and Facebook tags.')
-
-        # Option 3: Checkbox for 'Generate Social Media content' (Unchecked by default)
-        generate_social_media = st.checkbox('Generate Social Media content', value=False,
-                                help="Write Facebook, Instagram posts & tweets for generated blog. Needed for marketing your blogs.")
-
-        # Option 4: Checkbox for 'Do Content Analysis & Critique' (Unchecked by default)
-        content_analysis = st.checkbox('Do Content Analysis & Critique', value=False,
-                                       help="Blog Proof reading, Critique generated blog. Provide actionable changes & Editing options.")
-
-        # Display a message at the bottom for user guidance
-        st.info("🚨 Make sure to personalize content from the sidebar. Important.")
-
-    if st.button("Write Blog"):
-        # Clear the previous results from the screen
-        st.empty()
-        if user_input == "":
-            user_input = None
-        if not uploaded_file and not user_input and not audio_input:
-            st.error("🤬🤬 Either Enter/Type/Attach, can't read your mind.(yet..)")
-            st.stop()
-        else:
-            input_type = process_input(user_input, uploaded_file)
-        
-        if input_type == "keywords":
-            if user_input and len(user_input.split()) >= 2:
-                if content_type == "Normal-length content":
-                    try:
-                        short_blog = write_blog_from_keywords(user_input)
-                        st.markdown(short_blog)
-                    except Exception as err:
-                        st.error(f"🚫 Failed to write blog on {user_input}, Error: {err}")
-                elif content_type == "Long-form content":
-                    try:
-                        long_form_generator(user_input)
-                        st.success(f"Successfully wrote long-form blog on: {user_input}")
-                    except Exception as err:
-                        st.error(f"🚫 Failed to write blog on {user_input}, Error: {err}")
-                elif content_type == "Experimental - AI Agents team":
-                    try:
-                        ai_agents_writers(user_input)
-                        st.success(f"Successfully wrote content with AI agents on: {user_input}")
-                    except Exception as err:
-                        st.error(f"🚫 Failed to Write content with AI agents: {err}")
-            else:
-                st.error('🚫 Blog keywords should be at least two words long. Please try again.')
-        
-        elif input_type == "youtube_url" or input_type == "audio_file":
-            if not generate_audio_blog(user_input):
-                st.stop()
-        
-        elif input_type == "web_url":
-            blog_from_url(user_input)
-        
-        elif input_type == "image_file":
-            blog_from_image(user_input, temp_file_path)
-
-        elif input_type == "PDF_file":
-            pdf_reader = PyPDF2.PdfReader(uploaded_file)
-            text = ""
-            combined_result = ""
-            # Create a placeholder for the progress bar
-            progress_bar = st.progress(0)
-
-            # Loop through each page with a progress bar
-            for page_num, page in enumerate(pdf_reader.pages):
-                text += page.extract_text()
-                # Replace newlines with spaces
-                text = text.replace("\n", " ")
-                # Use regex to add a space between words that are combined
-                text = re.sub(r"(\w)([A-Z])", r"\1 \2", text)
-
-                results = blog_from_pdf(text)
-                # Update the progress bar
-                progress_bar.progress((page_num + 1) / len(pdf_reader.pages))
-                combined_result += str(results[-1])
-
-            # Clear progress bar at the end
-            progress_bar.empty()
-
-            st.markdown(combined_result)
-
-
-def blog_from_pdf(pdf_text):
-    """ Load in a long PDF and pull the text out. Create a prompt to be used to extract key bits of information.
-        Chunk up our document and process each chunk to pull any answers out. Combine them at the end. 
-        This simple approach will then be extended to three more difficult questions.
-    """
-    # FixME: 
-    document = '<document>'
-    template_prompt=f'''Extract key pieces of information from the given document.
-
-        When you extract a key piece of information, include the closest page number.
-        Ex: Extracted Information (Page number)
-        \n\nDocument: \"\"\"<document>\"\"\"\n\n'''
-
-    # Initialise tokenizer
-    tokenizer = tiktoken.get_encoding("cl100k_base")
-    results = []
-    
-    chunks = create_chunks(pdf_text, 1000, tokenizer)
-    text_chunks = [tokenizer.decode(chunk) for chunk in chunks]
-
-    for chunk in text_chunks:
-        results.append(extract_chunk(chunk, template_prompt))
-
-    #zipped = list(zip(*groups))
-    #zipped = [x for y in zipped for x in y if "Not specified" not in x and "__" not in x]
-    return results
-
-
-# Split a text into smaller chunks of size n, preferably ending at the end of a sentence
-def create_chunks(text, n, tokenizer):
-    tokens = tokenizer.encode(text)
-    """Yield successive n-sized chunks from text."""
-    i = 0
-    while i < len(tokens):
-        # Find the nearest end of sentence within a range of 0.5 * n and 1.5 * n tokens
-        j = min(i + int(1.5 * n), len(tokens))
-        while j > i + int(0.5 * n):
-            # Decode the tokens and check for full stop or newline
-            chunk = tokenizer.decode(tokens[i:j])
-            if chunk.endswith(".") or chunk.endswith("\n"):
-                break
-            j -= 1
-        # If no end of sentence found, use n tokens as the chunk size
-        if j == i + int(0.5 * n):
-            j = min(i + n, len(tokens))
-        yield tokens[i:j]
-        i = j
-
-
-def extract_chunk(document, template_prompt):
-    """ Chunking for large documents, exceed context window"""
-    client = openai.OpenAI(api_key=os.environ.get("OPENAI_API_KEY"))
-    prompt = template_prompt.replace('<document>', document)
-
-    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 ai_agents_team():
     # Define options for AI Content Teams
     st.title("🐲 Your AI Agents Teams")
@@ -316,9 +69,9 @@ def content_agents():
         if content_keywords and len(content_keywords.split()) >= 2:
             with st.spinner("Generating Content..."):
                 try:
-                    calendar_content = ai_agents_writers(content_keywords)
-                    st.success(f"Successfully generated content for: {content_keywords}")
-                    st.markdown(calendar_content)
+                    #calendar_content = ai_agents_writers(content_keywords)
+                    st.success(f"🚫 Not implemented yet: {content_keywords}")
+                    #st.markdown(calendar_content)
                 except Exception as err:
                     st.error(f"🚫 Failed to generate content with AI Agents: {err}")
         else:
@@ -474,4 +227,4 @@ def ai_social_writer():
     elif "instagram" in selected_platform:
         insta_writer()
     elif "youtube" in selected_platform:
-        youtube_main_menu()
+        youtube_main_menu()

lib/utils/content_generators.py

@@ -1,57 +1,11 @@
 import streamlit as st
-from lib.utils.alwrity_utils import (
-    blog_from_keyword, ai_agents_team, essay_writer, ai_news_writer,
-    ai_finance_ta_writer
-)
+
 from lib.alwrity_ui.similar_analysis import competitor_analysis
 from lib.alwrity_ui.keyword_web_researcher import do_web_research
-from lib.ai_writers.ai_story_writer.story_writer import story_input_section
-from lib.ai_writers.ai_product_description_writer import write_ai_prod_desc
-from lib.ai_writers.ai_copywriter.copywriter_dashboard import copywriter_dashboard
-from lib.ai_writers.linkedin_writer import LinkedInAIWriter
-#from lib.content_planning_calender.content_planning_agents_alwrity_crew import ai_agents_content_planner
-
-
-def ai_writers():
-    options = [
-        "AI Blog Writer",
-        "Story Writer",
-        "Essay writer",
-        "Write News reports",
-        "Write Financial TA report",
-        "AI Product Description Writer",
-        "AI Copywriter",
-        "LinkedIn AI Writer",
-        "Quit"
-    ]
-    choice = st.selectbox("**👇Select a content creation type:**", options, index=0, format_func=lambda x: f"📝 {x}")
-
-    if choice == "AI Blog Writer":
-        blog_from_keyword()
-    elif choice == "Story Writer":
-        story_input_section()
-    elif choice == "Essay writer":
-        essay_writer()
-    elif choice == "Write News reports":
-        ai_news_writer()
-    elif choice == "Write Financial TA report":
-        ai_finance_ta_writer()
-    elif choice == "AI Product Description Writer":
-        write_ai_prod_desc()
-    elif choice == "AI Copywriter":
-        # Initialize the copywriter dashboard
-        copywriter_dashboard()
-    elif choice == "LinkedIn AI Writer":
-        # Initialize the LinkedIn AI Writer
-        linkedin_writer = LinkedInAIWriter()
-        linkedin_writer.run()
-    elif choice == "Quit":
-        st.info("Thank you for using Alwrity. Goodbye!")
-        st.stop()
 
 
 def content_planning_tools():
-    # Add custom CSS for compact layout
+    # A custom CSS for compact layout
     st.markdown("""
         <style>
             /* Reduce top padding of main container */

lib/utils/ui_setup.py

@@ -1,10 +1,11 @@
 import os
 import streamlit as st
 from lib.utils.file_processor import load_image
-from lib.utils.content_generators import content_planning_tools, ai_writers
+from lib.utils.content_generators import content_planning_tools
 from lib.utils.alwrity_utils import ai_social_writer
 from lib.utils.seo_tools import ai_seo_tools
 from lib.utils.settings_page import render_settings_page
+from loguru import logger
 
 # Import social media writer functions
 from lib.ai_writers.ai_facebook_writer.facebook_ai_writer import facebook_main_menu
@@ -12,6 +13,7 @@ from lib.ai_writers.linkedin_writer.linkedin_ai_writer import linkedin_main_menu
 from lib.ai_writers.twitter_writers import run_dashboard
 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 setup_ui():
@@ -295,22 +297,26 @@ def setup_ui():
 
 def setup_alwrity_ui():
     """Sets up the main navigation in the sidebar."""
+    logger.info("Setting up ALwrity UI")
+    
     # Initialize session state for active tab if not exists
     if 'active_tab' not in st.session_state:
         st.session_state.active_tab = "Content Planning"
+        logger.info(f"Initialized active_tab to: {st.session_state.active_tab}")
     
     # Initialize session state for active sub-tab if not exists
     if 'active_sub_tab' not in st.session_state:
         st.session_state.active_sub_tab = None
+        logger.info("Initialized active_sub_tab to None")
 
     # Define the navigation items with their icons and functions
     nav_items = {
+        "AI Writers": ("📝", get_ai_writers),
         "Content Planning": ("📅", content_planning_tools),
-        "AI Writers": ("📝", ai_writers),
-        "Agents Teams": ("🤝", lambda: st.subheader("Agents Teams - Coming Soon!")),
         "AI SEO Tools": ("🔍", ai_seo_tools),
         "AI Social Tools": ("📱", None),  # Set to None as we'll handle this separately
-        "Ask Alwrity": ("💬", lambda: (
+        "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")
@@ -318,6 +324,8 @@ def setup_alwrity_ui():
         "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()),
@@ -326,6 +334,8 @@ def setup_alwrity_ui():
         "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")
@@ -342,6 +352,7 @@ def setup_alwrity_ui():
                 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}")
             
             # If AI Social Tools is active, show the sub-menu
             if st.session_state.active_tab == "AI Social Tools":
@@ -367,6 +378,7 @@ def setup_alwrity_ui():
                     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)
@@ -379,6 +391,7 @@ def setup_alwrity_ui():
                 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)
 
@@ -427,13 +440,47 @@ def setup_alwrity_ui():
             # Call the function directly without any title
             social_tools_submenu[st.session_state.active_sub_tab][1]()
     else:
-        st.markdown("""
-            <style>
-                .main .block-container {
-                    padding-top: 0.25rem !important;
-                    padding-bottom: 0;
-                }
-            </style>
-        """, unsafe_allow_html=True)
-        st.title(f"{nav_items[st.session_state.active_tab][0]} {st.session_state.active_tab}")
-        nav_items[st.session_state.active_tab][1]()
+        # Check if we're in the AI Writers section and handle writer selection
+        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")
+                
+                writer_found = False
+                for w in writers:
+                    logger.info(f"Checking writer: {w['name']} with path: {w['path']}")
+                    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
+                
+                if not writer_found:
+                    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 {
+                        padding-top: 0.25rem !important;
+                        padding-bottom: 0;
+                    }
+                </style>
+            """, unsafe_allow_html=True)
+            st.title(f"{nav_items[st.session_state.active_tab][0]} {st.session_state.active_tab}")
+            nav_items[st.session_state.active_tab][1]()
+    
+    logger.info("Finished setting up ALwrity UI")

lib/workspace/alwrity_config/main_config.json

@@ -16,7 +16,10 @@
         "GPT Provider": "google",
         "Model": "gemini-1.5-flash-latest",
         "Temperature": 0.7,
-        "Max Tokens": 4000
+        "Max Tokens": 4000,
+        "Top-p": 0.9,
+        "n": 1,
+        "fp": 16
     },
     "Search Engine Parameters": {
         "Geographic Location": "us",

lib/workspace/alwrity_web_research/README.md

@@ -1,3 +0,0 @@
-Alwrity web research reports will be saved in this folder.
-You can change this by modifying SEARCH_SAVE_FILE environment variable, after running alwrity.py from the command prompt.
-Better to change in the alwrity.py file itself, line no: 308