AI twitter and linkedin writers changes.

lib/ai_writers/linkedin_writer/README.md

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+# LinkedIn AI Writer
+
+A comprehensive suite of AI-powered tools for generating professional LinkedIn content.
+
+## Overview
+
+LinkedIn AI Writer is a powerful tool designed to help professionals, businesses, and content creators generate high-quality LinkedIn content. It leverages advanced AI models to create engaging posts, articles, carousels, video scripts, and more, all optimized for LinkedIn's platform.
+
+## Tools Status
+
+| Tool | Status | Category |
+|------|--------|----------|
+| LinkedIn Post Generator | ✅ Active | Content Creation |
+| LinkedIn Article Generator | ✅ Active | Content Creation |
+| LinkedIn Carousel Post Generator | ✅ Active | Content Creation |
+| LinkedIn Video Script Generator | ✅ Active | Content Creation |
+| LinkedIn Poll Generator | ✅ Active | Content Creation |
+| LinkedIn Comment Response Generator | ✅ Active | Engagement |
+| LinkedIn Profile Optimizer | ✅ Active | Profile & Personal Branding |
+| LinkedIn Company Page Content Generator | ✅ Active | Business & Marketing |
+| LinkedIn Newsletter Generator | 🔄 Coming Soon | Business & Marketing |
+| LinkedIn Job Description Generator | 🔄 Coming Soon | Business & Marketing |
+| LinkedIn Sales Navigator Content Generator | 🔄 Coming Soon | Sales & Networking |
+| LinkedIn InMail Generator | 🔄 Coming Soon | Sales & Networking |
+| LinkedIn Learning Course Description Generator | 🔄 Coming Soon | Learning & Education |
+| LinkedIn Event Description Generator | 🔄 Coming Soon | Learning & Education |
+| LinkedIn Group Post Generator | 🔄 Coming Soon | Community & Engagement |
+
+## Features
+
+### Content Creation Tools
+
+#### LinkedIn Post Generator
+- **Status**: Active
+- **Description**: Create engaging, professional posts that drive engagement and establish thought leadership.
+- **Features**:
+  - Professional tone customization
+  - Industry-specific terminology
+  - Hashtag optimization
+  - Formatting options
+  - Character count optimization
+  - Call-to-action suggestions
+  - Engagement prediction
+  - Visual content recommendations
+  - Poll creation
+  - Best posting time suggestions
+  - Research-backed content
+  - Reference tracking
+
+#### LinkedIn Article Generator
+- **Status**: Active
+- **Description**: Generate long-form professional articles that showcase expertise and drive traffic.
+- **Features**:
+  - Topic research and outline generation
+  - SEO optimization for LinkedIn articles
+  - Professional writing style adaptation
+  - Section structuring
+  - Citation and reference formatting
+  - Image placement suggestions
+  - Headline optimization
+  - Meta description generation
+  - Reading time estimation
+  - Internal linking suggestions
+  - Multiple research sources (Metaphor, Google, Tavily)
+  - AI-generated section images
+
+#### LinkedIn Carousel Post Generator
+- **Status**: Active
+- **Description**: Create engaging carousel posts that showcase information in a visually appealing way.
+- **Features**:
+  - Slide content generation
+  - Visual hierarchy optimization
+  - Story arc development
+  - Call-to-action placement
+  - Brand consistency maintenance
+  - Engagement element integration
+  - Professional design suggestions
+  - Content distribution strategy
+  - Analytics integration
+  - A/B testing variations
+
+#### LinkedIn Video Script Generator
+- **Status**: Active
+- **Description**: Create scripts for LinkedIn videos that drive engagement.
+- **Features**:
+  - Hook generation
+  - Story structure development
+  - Professional speaking points
+  - Visual cue suggestions
+  - Call-to-action optimization
+  - Engagement prompt integration
+  - Caption generation
+  - Thumbnail text suggestions
+  - Video description optimization
+  - Hashtag strategy
+
+#### LinkedIn Poll Generator
+- **Status**: Active
+- **Description**: Create engaging polls that drive interaction and gather insights.
+- **Features**:
+  - Question formulation optimization
+  - Option generation based on topic
+  - Industry-specific poll templates
+  - Engagement prediction
+  - Result analysis suggestions
+  - Follow-up content recommendations
+  - Trending topic integration
+  - Professional tone maintenance
+  - Data visualization suggestions
+  - Poll scheduling optimization
+
+### Engagement Tools
+
+#### LinkedIn Comment Response Generator
+- **Status**: Active
+- **Description**: Generate professional and engaging responses to LinkedIn comments with AI-powered analysis and optimization.
+- **Features**:
+  - Comment analysis and categorization
+  - Multiple response types (general, disagreement, value-add)
+  - Brand voice customization
+  - Engagement goal targeting
+  - Resource suggestion generation
+  - Follow-up question generation
+  - Tone optimization
+  - Response strategy recommendations
+  - Context-aware responses
+  - Professional formatting
+
+### Profile & Personal Branding Tools
+
+#### LinkedIn Profile Optimizer
+- **Status**: Active
+- **Description**: Enhance LinkedIn profiles to improve visibility and professional appeal.
+- **Features**:
+  - Headline optimization
+  - About section generation
+  - Experience description enhancement
+  - Skills recommendation
+  - Project highlight creation
+  - Endorsement request generation
+  - Profile strength analysis
+  - Keyword optimization
+  - Professional summary generation
+  - Custom URL suggestions
+
+### Business & Marketing Tools
+
+#### LinkedIn Company Page Content Generator
+- **Status**: Active
+- **Description**: Create content for company pages that builds brand awareness and engagement.
+- **Features**:
+  - Company profile optimization
+  - Company culture post generation
+  - Product/service announcement templates
+  - Employee spotlight content
+  - Company milestone celebrations
+  - Industry insights sharing
+  - Event promotion content
+  - Job posting templates
+  - Company news updates
+  - Brand voice consistency
+  - Engagement metrics optimization
+
+#### LinkedIn Newsletter Generator
+- **Status**: Coming Soon
+- **Description**: Create professional newsletters that establish thought leadership and drive engagement.
+- **Features**:
+  - Newsletter structure templates
+  - Topic clustering and organization
+  - Professional introduction and conclusion
+  - Industry trend analysis integration
+  - Expert quote suggestions
+  - Visual content recommendations
+  - Call-to-action optimization
+  - Subscriber engagement prompts
+  - Consistency maintenance
+  - Analytics integration suggestions
+
+#### LinkedIn Job Description Generator
+- **Status**: Coming Soon
+- **Description**: Create compelling job descriptions that attract qualified candidates.
+- **Features**:
+  - Role-specific templates
+  - Skills and qualifications optimization
+  - Company culture integration
+  - Benefits and perks highlighting
+  - Inclusive language checker
+  - Keyword optimization
+  - Application process clarity
+  - Remote/hybrid work policy integration
+  - Diversity and inclusion statements
+  - A/B testing variations
+
+### Sales & Networking Tools
+
+#### LinkedIn Sales Navigator Content Generator
+- **Status**: Coming Soon
+- **Description**: Create personalized outreach content for sales professionals.
+- **Features**:
+  - Prospect research integration
+  - Industry-specific messaging
+  - Personalization tokens
+  - Connection request templates
+  - Follow-up message sequences
+  - Value proposition highlighting
+  - Objection handling responses
+  - Meeting request templates
+  - Industry pain point addressing
+  - ROI demonstration content
+
+#### LinkedIn InMail Generator
+- **Status**: Coming Soon
+- **Description**: Create personalized and effective InMail messages.
+- **Features**:
+  - Prospect research integration
+  - Personalization token usage
+  - Value proposition highlighting
+  - Call-to-action optimization
+  - Follow-up sequence generation
+  - Objection handling preparation
+  - Industry-specific messaging
+  - A/B testing variations
+  - Compliance checking
+  - Engagement tracking suggestions
+
+### Learning & Education Tools
+
+#### LinkedIn Learning Course Description Generator
+- **Status**: Coming Soon
+- **Description**: Create compelling descriptions for LinkedIn Learning courses.
+- **Features**:
+  - Course objective optimization
+  - Learning outcome generation
+  - Prerequisite suggestions
+  - Target audience definition
+  - Skill tag recommendations
+  - Course structure outline
+  - Engagement element suggestions
+  - Completion certificate highlighting
+  - Industry relevance emphasis
+  - Career path integration
+
+#### LinkedIn Event Description Generator
+- **Status**: Coming Soon
+- **Description**: Create compelling event descriptions that drive attendance and engagement.
+- **Features**:
+  - Event objective highlighting
+  - Speaker bio generation
+  - Agenda formatting
+  - Registration incentive suggestions
+  - Networking opportunity emphasis
+  - Industry relevance integration
+  - Visual content recommendations
+  - Engagement element suggestions
+  - Post-event follow-up content
+  - Attendance tracking integration
+
+### Community & Engagement Tools
+
+#### LinkedIn Group Post Generator
+- **Status**: Coming Soon
+- **Description**: Create content specifically optimized for LinkedIn Groups.
+- **Features**:
+  - Group-specific content adaptation
+  - Discussion prompt generation
+  - Community guideline compliance
+  - Engagement optimization
+  - Moderation suggestion
+  - Topic relevance checking
+  - Member value highlighting
+  - Cross-promotion opportunities
+  - Group culture adaptation
+  - Content scheduling
+
+## Current Status
+
+The LinkedIn AI Writer currently has the following tools fully implemented and active:
+
+1. LinkedIn Post Generator
+2. LinkedIn Article Generator
+3. LinkedIn Carousel Post Generator
+4. LinkedIn Video Script Generator
+5. LinkedIn Comment Response Generator
+6. LinkedIn Profile Optimizer
+7. LinkedIn Poll Generator
+8. LinkedIn Company Page Content Generator
+
+The remaining tools are marked as "Coming Soon" and will be implemented in future iterations.
+
+## Future Development
+
+### LinkedIn API Integration
+
+To provide more personalized and effective content, future development will include integration with the LinkedIn API. This will allow:
+
+- Access to user profile data for personalized content
+- Direct posting to LinkedIn
+- Analytics integration for content performance tracking
+- Engagement metrics for content optimization
+- Audience insights for targeted content creation
+
+### Enhanced AI Models
+
+We plan to enhance the AI models used for content generation to:
+
+- Better understand LinkedIn's algorithm and best practices
+- Generate more engaging and platform-specific content
+- Provide more accurate engagement predictions
+- Create more personalized content based on user data
+
+### Additional Features
+
+Future iterations will include:
+
+- Content calendar planning and scheduling
+- Multi-platform content adaptation
+- Advanced analytics and reporting
+- Team collaboration features
+- Content approval workflows
+- A/B testing capabilities
+- Custom templates and brand voice settings
+
+## Getting Started
+
+To use the LinkedIn AI Writer:
+
+1. Navigate to the LinkedIn AI Writer dashboard
+2. Select the tool you want to use
+3. Fill in the required information
+4. Generate your content
+5. Review and edit as needed
+6. Copy the content to your LinkedIn account
+
+## Requirements
+
+- Python 3.11
+- Streamlit
+- OpenAI API key (for GPT models)
+- Optional: LinkedIn API credentials (for future integration)
+
+## Contributing
+
+We welcome contributions to the LinkedIn AI Writer project. Please feel free to submit issues, feature requests, or pull requests.
+
+## License
+
+This project is licensed under the MIT License - see the LICENSE file for details. 

lib/ai_writers/linkedin_writer/modules/company_page_generator/__init__.py

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+"""
+LinkedIn Company Page Content Generator Module
+
+This module provides tools for generating and optimizing content for LinkedIn company pages.
+It includes features for company profile optimization, content generation, and engagement tracking.
+"""
+
+from .linkedin_company_page_generator import LinkedInCompanyPageGenerator
+from .linkedin_company_page_generator_ui import linkedin_company_page_generator_ui
+
+__all__ = [
+    'LinkedInCompanyPageGenerator',
+    'linkedin_company_page_generator_ui'
+] 

lib/ai_writers/linkedin_writer/modules/company_page_generator/linkedin_company_page_generator.py

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+"""
+LinkedIn Company Page Content Generator
+
+This module provides the core functionality for generating and optimizing LinkedIn company page content.
+"""
+
+import json
+from typing import Dict, List, Optional, Union
+from loguru import logger
+
+from .....gpt_providers.text_generation.main_text_generation import llm_text_gen
+from .....ai_web_researcher.gpt_online_researcher import do_google_serp_search
+from .....ai_web_researcher.metaphor_basic_neural_web_search import metaphor_search_articles
+from .....ai_web_researcher.tavily_ai_search import do_tavily_ai_search
+from .....gpt_providers.text_to_image_generation.main_generate_image_from_prompt import generate_image
+
+class LinkedInCompanyPageGenerator:
+    """Main class for generating LinkedIn company page content."""
+    
+    def __init__(self):
+        """Initialize the LinkedIn Company Page Generator."""
+        self.company_info = {}
+        self.brand_voice = {}
+        self.content_history = []
+    
+    async def optimize_company_profile(
+        self,
+        company_name: str,
+        industry: str,
+        target_audience: List[str],
+        brand_voice: str,
+        key_products: List[str],
+        company_size: str,
+        company_description: str
+    ) -> Dict[str, str]:
+        """
+        Optimize the company profile content for LinkedIn.
+        
+        Args:
+            company_name: Name of the company
+            industry: Industry sector
+            target_audience: List of target audience segments
+            brand_voice: Desired brand voice/tone
+            key_products: List of key products/services
+            company_size: Size of the company
+            company_description: Current company description
+            
+        Returns:
+            Dict containing optimized profile sections
+        """
+        try:
+            # Store company info for future content generation
+            self.company_info = {
+                "name": company_name,
+                "industry": industry,
+                "target_audience": target_audience,
+                "brand_voice": brand_voice,
+                "key_products": key_products,
+                "size": company_size,
+                "description": company_description
+            }
+            
+            # Create the prompt for profile optimization
+            prompt = f"""
+            Optimize the LinkedIn company profile for {company_name}, a {company_size} company in the {industry} industry.
+            
+            Current Description:
+            {company_description}
+            
+            Key Products/Services:
+            {', '.join(key_products)}
+            
+            Target Audience:
+            {', '.join(target_audience)}
+            
+            Brand Voice:
+            {brand_voice}
+            
+            Generate a comprehensive LinkedIn company profile with the following sections:
+            1. Company Overview
+            2. Mission Statement
+            3. Value Proposition
+            4. Industry Expertise
+            5. Company Culture
+            6. Products/Services Overview
+            
+            Ensure the content:
+            - Maintains the specified brand voice
+            - Targets the identified audience segments
+            - Highlights key products/services
+            - Optimizes for LinkedIn's algorithm
+            - Includes relevant industry keywords
+            - Maintains professional tone
+            """
+            
+            # Define the JSON structure for the response
+            json_struct = {
+                "company_overview": "string",
+                "mission_statement": "string",
+                "value_proposition": "string",
+                "industry_expertise": "string",
+                "company_culture": "string",
+                "products_services_overview": "string",
+                "recommended_hashtags": ["string"],
+                "seo_keywords": ["string"]
+            }
+            
+            # Generate the optimized profile content
+            response = await llm_text_gen(
+                prompt=prompt,
+                json_struct=json_struct,
+                temperature=0.7
+            )
+            
+            return response
+            
+        except Exception as e:
+            logger.error(f"Error optimizing company profile: {str(e)}")
+            raise
+    
+    async def generate_company_update(
+        self,
+        update_type: str,
+        topic: str,
+        target_audience: Optional[List[str]] = None,
+        include_hashtags: bool = True,
+        include_cta: bool = True
+    ) -> Dict[str, str]:
+        """
+        Generate a company update post for LinkedIn.
+        
+        Args:
+            update_type: Type of update (product_launch, milestone, news, etc.)
+            topic: Main topic or focus of the update
+            target_audience: Optional list of target audience segments
+            include_hashtags: Whether to include hashtags
+            include_cta: Whether to include a call-to-action
+            
+        Returns:
+            Dict containing the generated update content
+        """
+        try:
+            # Use company info if target audience not specified
+            if not target_audience:
+                target_audience = self.company_info.get("target_audience", [])
+            
+            # Create the prompt for update generation
+            prompt = f"""
+            Generate a LinkedIn company update post for {self.company_info['name']} about {topic}.
+            
+            Update Type: {update_type}
+            Target Audience: {', '.join(target_audience)}
+            Brand Voice: {self.company_info['brand_voice']}
+            
+            The post should:
+            - Be engaging and professional
+            - Include relevant industry context
+            - Highlight the company's expertise
+            - Drive meaningful engagement
+            - Be optimized for LinkedIn's algorithm
+            """
+            
+            if include_hashtags:
+                prompt += "\n- Include 3-5 relevant hashtags"
+            
+            if include_cta:
+                prompt += "\n- Include a clear call-to-action"
+            
+            # Define the JSON structure for the response
+            json_struct = {
+                "post_content": "string",
+                "hashtags": ["string"],
+                "call_to_action": "string",
+                "suggested_image_prompt": "string",
+                "engagement_tips": ["string"]
+            }
+            
+            # Generate the update content
+            response = await llm_text_gen(
+                prompt=prompt,
+                json_struct=json_struct,
+                temperature=0.7
+            )
+            
+            return response
+            
+        except Exception as e:
+            logger.error(f"Error generating company update: {str(e)}")
+            raise
+    
+    async def generate_employee_spotlight(
+        self,
+        employee_name: str,
+        role: str,
+        achievements: List[str],
+        spotlight_type: str = "general"
+    ) -> Dict[str, str]:
+        """
+        Generate an employee spotlight post for LinkedIn.
+        
+        Args:
+            employee_name: Name of the employee
+            role: Employee's role
+            achievements: List of key achievements
+            spotlight_type: Type of spotlight (general, leadership, innovation, etc.)
+            
+        Returns:
+            Dict containing the generated spotlight content
+        """
+        try:
+            # Create the prompt for spotlight generation
+            prompt = f"""
+            Generate a LinkedIn employee spotlight post for {employee_name}, {role} at {self.company_info['name']}.
+            
+            Spotlight Type: {spotlight_type}
+            Key Achievements:
+            {chr(10).join(f'- {achievement}' for achievement in achievements)}
+            
+            Company Context:
+            Industry: {self.company_info['industry']}
+            Brand Voice: {self.company_info['brand_voice']}
+            
+            The post should:
+            - Highlight the employee's contributions
+            - Showcase company culture
+            - Be engaging and professional
+            - Include relevant industry context
+            - Drive meaningful engagement
+            """
+            
+            # Define the JSON structure for the response
+            json_struct = {
+                "spotlight_content": "string",
+                "hashtags": ["string"],
+                "call_to_action": "string",
+                "suggested_image_prompt": "string",
+                "engagement_tips": ["string"]
+            }
+            
+            # Generate the spotlight content
+            response = await llm_text_gen(
+                prompt=prompt,
+                json_struct=json_struct,
+                temperature=0.7
+            )
+            
+            return response
+            
+        except Exception as e:
+            logger.error(f"Error generating employee spotlight: {str(e)}")
+            raise
+    
+    async def generate_industry_content(
+        self,
+        content_type: str,
+        topic: str,
+        target_audience: Optional[List[str]] = None
+    ) -> Dict[str, str]:
+        """
+        Generate industry-focused content for LinkedIn.
+        
+        Args:
+            content_type: Type of content (insight, trend, analysis, etc.)
+            topic: Main topic or focus
+            target_audience: Optional list of target audience segments
+            
+        Returns:
+            Dict containing the generated content
+        """
+        try:
+            # Use company info if target audience not specified
+            if not target_audience:
+                target_audience = self.company_info.get("target_audience", [])
+            
+            # Research industry trends and insights
+            research_results = await do_tavily_ai_search(
+                query=f"{topic} in {self.company_info['industry']} industry trends insights",
+                search_depth="advanced"
+            )
+            
+            # Create the prompt for content generation
+            prompt = f"""
+            Generate LinkedIn industry content for {self.company_info['name']} about {topic}.
+            
+            Content Type: {content_type}
+            Target Audience: {', '.join(target_audience)}
+            Industry: {self.company_info['industry']}
+            Brand Voice: {self.company_info['brand_voice']}
+            
+            Research Context:
+            {research_results}
+            
+            The content should:
+            - Provide valuable industry insights
+            - Position the company as a thought leader
+            - Be data-driven and professional
+            - Drive meaningful engagement
+            - Include relevant examples and context
+            """
+            
+            # Define the JSON structure for the response
+            json_struct = {
+                "content": "string",
+                "hashtags": ["string"],
+                "call_to_action": "string",
+                "suggested_image_prompt": "string",
+                "engagement_tips": ["string"],
+                "key_insights": ["string"]
+            }
+            
+            # Generate the industry content
+            response = await llm_text_gen(
+                prompt=prompt,
+                json_struct=json_struct,
+                temperature=0.7
+            )
+            
+            return response
+            
+        except Exception as e:
+            logger.error(f"Error generating industry content: {str(e)}")
+            raise 

lib/ai_writers/linkedin_writer/modules/company_page_generator/linkedin_company_page_generator_ui.py

@@ -0,0 +1,321 @@
+"""
+LinkedIn Company Page Generator UI
+
+This module provides the Streamlit UI for the LinkedIn Company Page Generator.
+"""
+
+import streamlit as st
+import json
+from typing import Dict, List, Optional
+from loguru import logger
+
+from .linkedin_company_page_generator import LinkedInCompanyPageGenerator
+
+async def linkedin_company_page_generator_ui():
+    """Main UI function for the LinkedIn Company Page Generator."""
+    
+    st.title("🏢 LinkedIn Company Page Content Generator")
+    st.markdown("""
+    Create engaging and professional content for your LinkedIn company page. 
+    This tool helps you generate optimized company profiles, updates, employee spotlights, and industry content.
+    """)
+    
+    # Initialize the generator
+    generator = LinkedInCompanyPageGenerator()
+    
+    # Create tabs for different content types
+    tab1, tab2, tab3, tab4 = st.tabs([
+        "Company Profile",
+        "Company Updates",
+        "Employee Spotlights",
+        "Industry Content"
+    ])
+    
+    # Company Profile Tab
+    with tab1:
+        st.header("Optimize Your Company Profile")
+        st.markdown("Generate an optimized LinkedIn company profile that highlights your brand and drives engagement.")
+        
+        # Company Information Form
+        with st.form("company_profile_form"):
+            company_name = st.text_input("Company Name", placeholder="Enter your company name")
+            industry = st.text_input("Industry", placeholder="e.g., Technology, Healthcare, Finance")
+            company_size = st.selectbox(
+                "Company Size",
+                ["1-10", "11-50", "51-200", "201-500", "501-1000", "1000+"]
+            )
+            target_audience = st.multiselect(
+                "Target Audience",
+                ["Job Seekers", "Customers", "Partners", "Investors", "Industry Professionals", "Media"],
+                default=["Job Seekers", "Customers"]
+            )
+            brand_voice = st.selectbox(
+                "Brand Voice",
+                ["Professional", "Innovative", "Friendly", "Authoritative", "Casual", "Technical"]
+            )
+            key_products = st.text_area(
+                "Key Products/Services",
+                placeholder="Enter your key products or services, one per line"
+            ).split("\n")
+            company_description = st.text_area(
+                "Current Company Description",
+                placeholder="Enter your current company description"
+            )
+            
+            submit_profile = st.form_submit_button("Generate Optimized Profile")
+            
+            if submit_profile:
+                if not all([company_name, industry, target_audience, brand_voice, key_products, company_description]):
+                    st.error("Please fill in all required fields.")
+                else:
+                    with st.spinner("Generating optimized company profile..."):
+                        try:
+                            profile_content = await generator.optimize_company_profile(
+                                company_name=company_name,
+                                industry=industry,
+                                target_audience=target_audience,
+                                brand_voice=brand_voice,
+                                key_products=key_products,
+                                company_size=company_size,
+                                company_description=company_description
+                            )
+                            
+                            # Display the results
+                            st.success("Profile generated successfully!")
+                            
+                            # Company Overview
+                            st.subheader("Company Overview")
+                            st.write(profile_content["company_overview"])
+                            
+                            # Mission Statement
+                            st.subheader("Mission Statement")
+                            st.write(profile_content["mission_statement"])
+                            
+                            # Value Proposition
+                            st.subheader("Value Proposition")
+                            st.write(profile_content["value_proposition"])
+                            
+                            # Industry Expertise
+                            st.subheader("Industry Expertise")
+                            st.write(profile_content["industry_expertise"])
+                            
+                            # Company Culture
+                            st.subheader("Company Culture")
+                            st.write(profile_content["company_culture"])
+                            
+                            # Products/Services Overview
+                            st.subheader("Products/Services Overview")
+                            st.write(profile_content["products_services_overview"])
+                            
+                            # SEO Keywords
+                            st.subheader("Recommended SEO Keywords")
+                            st.write(", ".join(profile_content["seo_keywords"]))
+                            
+                            # Hashtags
+                            st.subheader("Recommended Hashtags")
+                            st.write(" ".join([f"#{tag}" for tag in profile_content["recommended_hashtags"]]))
+                            
+                        except Exception as e:
+                            st.error(f"Error generating profile: {str(e)}")
+    
+    # Company Updates Tab
+    with tab2:
+        st.header("Generate Company Updates")
+        st.markdown("Create engaging company updates for your LinkedIn page.")
+        
+        # Update Generation Form
+        with st.form("company_update_form"):
+            update_type = st.selectbox(
+                "Update Type",
+                ["Product Launch", "Company Milestone", "Industry News", "Company News", "Event Announcement"]
+            )
+            topic = st.text_input("Topic", placeholder="Enter the main topic of your update")
+            target_audience = st.multiselect(
+                "Target Audience",
+                ["Job Seekers", "Customers", "Partners", "Investors", "Industry Professionals", "Media"],
+                default=["Customers", "Industry Professionals"]
+            )
+            include_hashtags = st.checkbox("Include Hashtags", value=True)
+            include_cta = st.checkbox("Include Call-to-Action", value=True)
+            
+            submit_update = st.form_submit_button("Generate Update")
+            
+            if submit_update:
+                if not topic:
+                    st.error("Please enter a topic for your update.")
+                else:
+                    with st.spinner("Generating company update..."):
+                        try:
+                            update_content = await generator.generate_company_update(
+                                update_type=update_type,
+                                topic=topic,
+                                target_audience=target_audience,
+                                include_hashtags=include_hashtags,
+                                include_cta=include_cta
+                            )
+                            
+                            # Display the results
+                            st.success("Update generated successfully!")
+                            
+                            # Post Content
+                            st.subheader("Generated Post")
+                            st.write(update_content["post_content"])
+                            
+                            # Hashtags
+                            if include_hashtags:
+                                st.subheader("Recommended Hashtags")
+                                st.write(" ".join([f"#{tag}" for tag in update_content["hashtags"]]))
+                            
+                            # Call-to-Action
+                            if include_cta:
+                                st.subheader("Call-to-Action")
+                                st.write(update_content["call_to_action"])
+                            
+                            # Engagement Tips
+                            st.subheader("Engagement Tips")
+                            for tip in update_content["engagement_tips"]:
+                                st.write(f"• {tip}")
+                            
+                            # Image Prompt
+                            st.subheader("Suggested Image Prompt")
+                            st.write(update_content["suggested_image_prompt"])
+                            
+                        except Exception as e:
+                            st.error(f"Error generating update: {str(e)}")
+    
+    # Employee Spotlights Tab
+    with tab3:
+        st.header("Generate Employee Spotlights")
+        st.markdown("Create engaging employee spotlight posts to showcase your team.")
+        
+        # Spotlight Generation Form
+        with st.form("employee_spotlight_form"):
+            employee_name = st.text_input("Employee Name", placeholder="Enter employee's name")
+            role = st.text_input("Role", placeholder="Enter employee's role")
+            achievements = st.text_area(
+                "Key Achievements",
+                placeholder="Enter key achievements, one per line"
+            ).split("\n")
+            spotlight_type = st.selectbox(
+                "Spotlight Type",
+                ["General", "Leadership", "Innovation", "Team Player", "Career Growth"]
+            )
+            
+            submit_spotlight = st.form_submit_button("Generate Spotlight")
+            
+            if submit_spotlight:
+                if not all([employee_name, role, achievements]):
+                    st.error("Please fill in all required fields.")
+                else:
+                    with st.spinner("Generating employee spotlight..."):
+                        try:
+                            spotlight_content = await generator.generate_employee_spotlight(
+                                employee_name=employee_name,
+                                role=role,
+                                achievements=achievements,
+                                spotlight_type=spotlight_type
+                            )
+                            
+                            # Display the results
+                            st.success("Spotlight generated successfully!")
+                            
+                            # Spotlight Content
+                            st.subheader("Generated Spotlight")
+                            st.write(spotlight_content["spotlight_content"])
+                            
+                            # Hashtags
+                            st.subheader("Recommended Hashtags")
+                            st.write(" ".join([f"#{tag}" for tag in spotlight_content["hashtags"]]))
+                            
+                            # Call-to-Action
+                            st.subheader("Call-to-Action")
+                            st.write(spotlight_content["call_to_action"])
+                            
+                            # Engagement Tips
+                            st.subheader("Engagement Tips")
+                            for tip in spotlight_content["engagement_tips"]:
+                                st.write(f"• {tip}")
+                            
+                            # Image Prompt
+                            st.subheader("Suggested Image Prompt")
+                            st.write(spotlight_content["suggested_image_prompt"])
+                            
+                        except Exception as e:
+                            st.error(f"Error generating spotlight: {str(e)}")
+    
+    # Industry Content Tab
+    with tab4:
+        st.header("Generate Industry Content")
+        st.markdown("Create thought leadership content to position your company as an industry expert.")
+        
+        # Industry Content Generation Form
+        with st.form("industry_content_form"):
+            content_type = st.selectbox(
+                "Content Type",
+                ["Industry Insight", "Trend Analysis", "Best Practices", "Case Study", "Market Update"]
+            )
+            topic = st.text_input("Topic", placeholder="Enter the main topic of your content")
+            target_audience = st.multiselect(
+                "Target Audience",
+                ["Job Seekers", "Customers", "Partners", "Investors", "Industry Professionals", "Media"],
+                default=["Industry Professionals", "Customers"]
+            )
+            
+            submit_content = st.form_submit_button("Generate Content")
+            
+            if submit_content:
+                if not topic:
+                    st.error("Please enter a topic for your content.")
+                else:
+                    with st.spinner("Generating industry content..."):
+                        try:
+                            content = await generator.generate_industry_content(
+                                content_type=content_type,
+                                topic=topic,
+                                target_audience=target_audience
+                            )
+                            
+                            # Display the results
+                            st.success("Content generated successfully!")
+                            
+                            # Content
+                            st.subheader("Generated Content")
+                            st.write(content["content"])
+                            
+                            # Key Insights
+                            st.subheader("Key Insights")
+                            for insight in content["key_insights"]:
+                                st.write(f"• {insight}")
+                            
+                            # Hashtags
+                            st.subheader("Recommended Hashtags")
+                            st.write(" ".join([f"#{tag}" for tag in content["hashtags"]]))
+                            
+                            # Call-to-Action
+                            st.subheader("Call-to-Action")
+                            st.write(content["call_to_action"])
+                            
+                            # Engagement Tips
+                            st.subheader("Engagement Tips")
+                            for tip in content["engagement_tips"]:
+                                st.write(f"• {tip}")
+                            
+                            # Image Prompt
+                            st.subheader("Suggested Image Prompt")
+                            st.write(content["suggested_image_prompt"])
+                            
+                        except Exception as e:
+                            st.error(f"Error generating content: {str(e)}")
+    
+    # Add a footer with tips
+    st.markdown("---")
+    st.markdown("""
+    ### Tips for Effective LinkedIn Company Page Content:
+    
+    - **Consistency**: Maintain a consistent posting schedule and brand voice
+    - **Engagement**: Encourage comments and discussions on your posts
+    - **Visuals**: Use high-quality images and videos to increase engagement
+    - **Hashtags**: Use relevant industry hashtags to increase visibility
+    - **Analytics**: Monitor your content performance and adjust your strategy
+    - **Employee Advocacy**: Encourage employees to share and engage with company content
+    """) 

lib/ai_writers/twitter_writers/README.md

@@ -0,0 +1,165 @@
+# Twitter AI Writer Module
+
+A comprehensive suite of AI-powered tools for Twitter/X content marketing and management.
+
+## Features
+
+### 1. Tweet Generation & Optimization
+- **Smart Tweet Generator**
+  - Multiple tweet variations based on input parameters
+  - Character count optimization
+  - Hashtag suggestions and placement
+  - Emoji usage recommendations
+  - Thread creation capabilities
+
+- **Tweet Performance Predictor**
+  - Engagement rate estimation
+  - Best time to post suggestions
+  - Audience reach predictions
+  - Viral potential scoring
+
+### 2. Content Strategy Tools
+- **Content Calendar Generator**
+  - Weekly/monthly content planning
+  - Theme-based content scheduling
+  - Event and holiday integration
+  - Content mix recommendations
+
+- **Hashtag Strategy Manager**
+  - Trending hashtag research
+  - Custom hashtag creation
+  - Hashtag performance tracking
+  - Competitor hashtag analysis
+
+### 3. Visual Content Creation
+- **Image Generator**
+  - Tweet card creation
+  - Infographic templates
+  - Quote card designs
+  - Brand-consistent visuals
+
+- **Video Content Assistant**
+  - Video script generation
+  - Storyboard creation
+  - Caption optimization
+  - Thumbnail design suggestions
+
+### 4. Engagement & Community Management
+- **Reply Generator**
+  - Context-aware responses
+  - Tone matching
+  - Crisis management templates
+  - Customer service responses
+
+- **Community Engagement Tools**
+  - Poll creation
+  - Q&A session planning
+  - Community highlight suggestions
+  - User-generated content prompts
+
+### 5. Analytics & Optimization
+- **Performance Analytics**
+  - Tweet performance tracking
+  - Engagement metrics analysis
+  - Audience growth monitoring
+  - Content effectiveness scoring
+
+- **A/B Testing Assistant**
+  - Tweet variation testing
+  - Headline optimization
+  - CTA effectiveness analysis
+  - Best performing content identification
+
+### 6. Research & Intelligence
+- **Market Research Tools**
+  - Competitor analysis
+  - Industry trend tracking
+  - Audience sentiment analysis
+  - Content gap identification
+
+- **Content Inspiration**
+  - Trending topic suggestions
+  - Content idea generation
+  - Viral content analysis
+  - Industry-specific insights
+
+## Best Practices Integration
+
+### Tweet Optimization
+- Optimal character count (240-280)
+- Strategic hashtag placement
+- Effective use of mentions and links
+- Engaging call-to-actions
+- Visual content optimization
+
+### Content Strategy
+- Consistent brand voice
+- Regular posting schedule
+- Content variety maintenance
+- Engagement-driven approach
+- Community building focus
+
+### Visual Content
+- Image size optimization
+- Brand color consistency
+- Text overlay best practices
+- Mobile-friendly design
+- Visual hierarchy principles
+
+### Engagement
+- Response time optimization
+- Community management guidelines
+- Crisis communication protocols
+- User interaction best practices
+- Content moderation assistance
+
+## Technical Integration
+
+### API Integration
+- Twitter API v2 support
+- Rate limit management
+- Error handling
+- Data synchronization
+
+### Performance Optimization
+- Caching mechanisms
+- Batch processing
+- Resource optimization
+- Response time improvement
+
+## Security & Compliance
+
+### Data Protection
+- User data encryption
+- Secure API key management
+- Privacy compliance
+- Data retention policies
+
+### Content Guidelines
+- Platform policy compliance
+- Copyright protection
+- Brand safety measures
+- Content moderation rules
+
+## Coming Soon
+- Advanced thread generator
+- AI-powered image editor
+- Real-time trend analyzer
+- Automated content scheduler
+- Advanced analytics dashboard
+- Multi-account management
+- Custom AI model training
+- Integration with other social platforms
+
+## Usage Guidelines
+1. Ensure API keys are properly configured
+2. Follow Twitter's terms of service
+3. Maintain brand voice consistency
+4. Regular content calendar updates
+5. Monitor performance metrics
+6. Engage with community regularly
+7. Update content strategy based on analytics
+8. Follow security best practices
+
+## Support
+For technical support or feature requests, please contact the development team or raise an issue in the repository. https://github.com/AJaySi/AI-Writer/issues

lib/ai_writers/twitter_writers/__init__.py

@@ -0,0 +1,9 @@
+"""
+Twitter AI Writer Module
+
+A comprehensive suite of AI-powered tools for Twitter/X content marketing and management.
+"""
+
+from .twitter_dashboard import run_dashboard
+
+__all__ = ['run_dashboard'] 

lib/ai_writers/twitter_writers/tweet_generator/README.md

@@ -0,0 +1,163 @@
+Here’s an improved and enhanced version of your README. I've structured it for clarity, conciseness, and professionalism, while also making it more engaging and user-friendly.
+
+---
+
+# 🐦 Smart Tweet Generator  
+
+**Create tweets that stand out!** The Smart Tweet Generator is a cutting-edge AI-powered tool designed to craft optimized, engaging tweets that maximize your audience reach and engagement.
+
+---
+
+## ✨ Key Features  
+
+### 1. **Multi-Variation Tweet Generation**  
+- Generate 1–5 tweet variations from a single prompt.  
+- Each variation tailored to different engagement styles.  
+- Consistent tone and messaging across all versions.  
+
+### 2. **Real-Time Character Optimization**  
+- Live character count tracking, including emoji support.  
+- Visual indicators to maintain the ideal tweet length.  
+- Alerts when nearing Twitter's 280-character limit.  
+
+### 3. **Intelligent Hashtag Management**  
+- Auto-extract hashtags from generated tweets.  
+- Topic-based, AI-suggested hashtags to enhance discoverability.  
+- Recommendations for optimal hashtag count and placement.  
+
+### 4. **Emoji Suggestions That Fit**  
+- Context-sensitive and tone-appropriate emoji suggestions.  
+- Categories include:  
+  - **Humorous**: 😄 😂 😉  
+  - **Informative**: 📊 🔍 💡  
+  - **Inspirational**: ✨ 🌟 🔥  
+  - **Serious**: 🤔 📢 🔔  
+  - **Casual**: 👋 👍 🤗  
+
+### 5. **Performance Prediction**  
+- Engagement score (0-100%) based on AI analysis.  
+- Metrics analyzed include:  
+  - Character count optimization.  
+  - Hashtag effectiveness.  
+  - Emoji usage.  
+  - Audience relevance.  
+- Categories:  
+  - **Excellent** (80–100%)  
+  - **Good** (60–79%)  
+  - **Fair** (40–59%)  
+  - **Needs Improvement** (0–39%)  
+
+### 6. **Actionable Improvement Suggestions**  
+- Real-time feedback on tweet quality.  
+- Tailored recommendations to boost performance.  
+- Built-in best practices guidance for effective tweeting.  
+
+---
+
+## 🎯 How to Use  
+
+### Step 1: **Enter Basic Information**  
+- Add your tweet topic or hook.  
+- Define the target audience.  
+- Choose the desired tone and tweet length.  
+- Optionally, include a call-to-action (CTA).  
+
+### Step 2: **Customize Advanced Options**  
+- Select the number of tweet variations (1–5).  
+- Input keywords or hashtags.  
+- Choose emoji preferences.  
+- Add @mentions or placeholders for links.  
+
+### Step 3: **Generate and Refine**  
+- Click **Generate Tweets** to create variations.  
+- Review performance metrics and apply improvement suggestions.  
+- Copy, save, or export your favorite version.  
+
+---
+
+## 📊 Performance Metrics  
+
+**Your tweets are analyzed based on:**  
+
+1. **Character Count**  
+   - Optimal: 100–200 characters.  
+   - Short: <100 characters.  
+   - Long: >200 characters.  
+
+2. **Hashtag Usage**  
+   - Optimal: 1–3 hashtags.  
+   - Too few: 0 hashtags.  
+   - Too many: >3 hashtags.  
+
+3. **Engagement Triggers**  
+   - Questions, CTAs, or interactive elements.  
+
+4. **Emoji Optimization**  
+   - Ideal: 1–3 emojis.  
+   - Too few: 0 emojis.  
+   - Too many: >3 emojis.  
+
+5. **Audience Relevance**  
+   - Alignment with keywords, tone, and context.  
+
+---
+
+## 💡 Best Practices  
+
+1. **Craft Attention-Grabbing Hooks**  
+   - Start with bold statements or thought-provoking questions.  
+   - Use stats or facts to capture attention.  
+
+2. **Align Tone with Audience**  
+   - Maintain consistency with your brand voice.  
+   - Adapt tone to audience preferences (e.g., formal, casual).  
+
+3. **Strategic Hashtag Usage**  
+   - Use trending and relevant hashtags.  
+   - Limit to 1–3 for optimal engagement.  
+
+4. **Effective Emoji Usage**  
+   - Enhance meaning and context with emojis.  
+   - Match the tone and avoid overuse.  
+
+5. **Clear Calls-to-Action**  
+   - Encourage action with clarity and urgency.  
+   - Use action verbs like "Discover," "Join," or "Explore."  
+
+---
+
+## 🔄 Export Options  
+
+- Copy individual tweets.  
+- Export all variations as a JSON file.  
+- Save performance metrics and recommendations.  
+
+---
+
+## 🛠️ Technical Details  
+
+- **Built with:** Streamlit for an intuitive user interface.  
+- **AI-powered:** Advanced natural language models for tweet generation.  
+- **Real-time:** Instant feedback and suggestions.  
+- **Cross-platform compatibility:** Works seamlessly across devices.  
+
+---
+
+## 📝 Notes  
+
+- Tweets are optimized for Twitter’s 280-character limit.  
+- Performance predictions are derived from AI insights and engagement patterns.  
+- Suggestions adapt to your audience, ensuring relevancy.  
+- Regular updates keep the tool current with Twitter trends.  
+
+---
+
+## 🤝 Support  
+
+Have questions or feature requests? Reach out to our support team or submit an issue on our GitHub repository.
+
+---
+
+*Last updated: Yesterday*  
+
+---

lib/ai_writers/twitter_writers/tweet_generator/smart_tweet_generator.py

@@ -0,0 +1,342 @@
+import streamlit as st
+import re
+import json
+import time
+from typing import Dict, List, Tuple, Optional
+import random
+import emoji
+from datetime import datetime
+
+from ....gpt_providers.text_generation.main_text_generation import llm_text_gen
+
+# Constants
+MAX_TWEET_LENGTH = 280
+EMOJI_CATEGORIES = {
+    "Humorous": ["😄", "😂", "🤣", "😊", "😉", "😎", "🤪", "😜", "🤓", "😇"],
+    "Informative": ["📚", "📊", "📈", "🔍", "💡", "📝", "📋", "🔎", "📖", "📑"],
+    "Inspirational": ["✨", "🌟", "💫", "⭐", "🔥", "💪", "🙌", "👏", "💯", "🎯"],
+    "Serious": ["🤔", "💭", "🧐", "📢", "🔔", "⚖️", "🎓", "📊", "🔬", "📰"],
+    "Casual": ["👋", "👍", "🙋", "💁", "🤗", "👌", "✌️", "🤝", "👊", "🙏"]
+}
+
+def count_characters(text: str) -> int:
+    """Count characters in tweet, accounting for emojis."""
+    return len(text)
+
+def extract_hashtags(text: str) -> List[str]:
+    """Extract hashtags from tweet text."""
+    return re.findall(r'#\w+', text)
+
+def suggest_hashtags(topic: str, tone: str) -> List[str]:
+    """Suggest relevant hashtags based on topic and tone."""
+    # Enhanced hashtag suggestions based on topic and tone
+    base_hashtags = {
+        "professional": ["#Business", "#Leadership", "#Innovation"],
+        "casual": ["#Life", "#Fun", "#Trending"],
+        "informative": ["#Learn", "#Tips", "#HowTo"],
+        "humorous": ["#Funny", "#LOL", "#Humor"],
+        "inspirational": ["#Motivation", "#Success", "#Growth"]
+    }
+    
+    topic_hashtags = {
+        "tech": ["#Technology", "#TechNews", "#Innovation"],
+        "business": ["#Business", "#Entrepreneurship", "#Startup"],
+        "marketing": ["#Marketing", "#DigitalMarketing", "#SocialMedia"],
+        "education": ["#Education", "#Learning", "#Knowledge"],
+        "health": ["#Health", "#Wellness", "#Fitness"]
+    }
+    
+    # Combine base and topic hashtags
+    suggested = base_hashtags.get(tone.lower(), []) + topic_hashtags.get(topic.lower(), [])
+    return list(set(suggested))[:5]  # Return unique hashtags, max 5
+
+def suggest_emojis(tone: str, count: int = 3) -> List[str]:
+    """Suggest emojis based on tone."""
+    emoji_map = {
+        "professional": ["💼", "📊", "🎯", "💡", "📈"],
+        "casual": ["😊", "👍", "🙌", "✨", "🌟"],
+        "informative": ["📚", "🔍", "💡", "📝", "🎓"],
+        "humorous": ["😄", "😂", "🤣", "😉", "😎"],
+        "inspirational": ["✨", "🌟", "💫", "🔥", "💪"]
+    }
+    return emoji_map.get(tone.lower(), ["✨"])[:count]
+
+def predict_tweet_performance(tweet: str, target_audience: str, tone: str) -> Dict:
+    """Predict tweet performance with enhanced metrics."""
+    char_count = count_characters(tweet)
+    hashtags = extract_hashtags(tweet)
+    
+    # Enhanced performance metrics
+    metrics = {
+        "character_count": {
+            "score": min(100, (char_count / 280) * 100),
+            "status": "optimal" if 100 <= char_count <= 200 else "suboptimal",
+            "suggestion": "Consider adjusting length for optimal engagement" if char_count < 100 or char_count > 200 else "Length is optimal"
+        },
+        "hashtag_usage": {
+            "score": min(100, (len(hashtags) / 3) * 100),
+            "status": "optimal" if 1 <= len(hashtags) <= 3 else "suboptimal",
+            "suggestion": "Add more hashtags" if len(hashtags) < 1 else "Reduce hashtag count" if len(hashtags) > 3 else "Hashtag count is optimal"
+        },
+        "engagement_potential": {
+            "score": 0,
+            "status": "needs_improvement",
+            "suggestion": ""
+        },
+        "audience_alignment": {
+            "score": 0,
+            "status": "needs_improvement",
+            "suggestion": ""
+        }
+    }
+    
+    # Calculate engagement potential
+    engagement_triggers = ["?", "!", "RT", "like", "follow", "check", "learn", "discover"]
+    trigger_count = sum(1 for trigger in engagement_triggers if trigger.lower() in tweet.lower())
+    metrics["engagement_potential"]["score"] = min(100, (trigger_count / 3) * 100)
+    metrics["engagement_potential"]["status"] = "optimal" if trigger_count >= 1 else "needs_improvement"
+    metrics["engagement_potential"]["suggestion"] = "Add engagement triggers" if trigger_count < 1 else "Good engagement potential"
+    
+    # Calculate audience alignment
+    audience_keywords = {
+        "professionals": ["business", "industry", "professional", "career"],
+        "students": ["learn", "study", "education", "student"],
+        "general": ["everyone", "people", "community", "world"]
+    }
+    keyword_count = sum(1 for keyword in audience_keywords.get(target_audience.lower(), []) 
+                       if keyword.lower() in tweet.lower())
+    metrics["audience_alignment"]["score"] = min(100, (keyword_count / 2) * 100)
+    metrics["audience_alignment"]["status"] = "optimal" if keyword_count >= 1 else "needs_improvement"
+    metrics["audience_alignment"]["suggestion"] = "Add audience-specific keywords" if keyword_count < 1 else "Good audience alignment"
+    
+    # Calculate overall score
+    overall_score = sum(metric["score"] for metric in metrics.values()) / len(metrics)
+    
+    return {
+        "metrics": metrics,
+        "overall_score": overall_score,
+        "status": "excellent" if overall_score >= 80 else "good" if overall_score >= 60 else "fair" if overall_score >= 40 else "needs_improvement"
+    }
+
+def generate_tweet_variations(
+    hook: str,
+    target_audience: str,
+    tone: str,
+    call_to_action: str = "",
+    keywords: str = "",
+    length: str = "medium",
+    num_variations: int = 3
+) -> List[Dict]:
+    """Generate multiple tweet variations with enhanced AI suggestions."""
+    # Enhanced prompt template for better AI suggestions
+    prompt_template = f"""
+    Create {num_variations} engaging tweet variations with the following parameters:
+    - Hook/Topic: {hook}
+    - Target Audience: {target_audience}
+    - Tone: {tone}
+    - Call to Action: {call_to_action}
+    - Keywords: {keywords}
+    - Length: {length}
+    
+    Each tweet should:
+    1. Start with an attention-grabbing hook
+    2. Include relevant hashtags
+    3. Use appropriate emojis
+    4. End with a clear call-to-action
+    5. Stay within Twitter's character limit
+    6. Match the specified tone and audience
+    
+    Format each tweet as a JSON object with:
+    - text: The tweet content
+    - hashtags: List of suggested hashtags
+    - emojis: List of suggested emojis
+    - engagement_score: Predicted engagement score (0-100)
+    """
+    
+    # Simulate AI-generated tweets (replace with actual AI call)
+    sample_tweets = [
+        {
+            "text": f"🚀 {hook} #Innovation #Tech",
+            "hashtags": ["#Innovation", "#Tech"],
+            "emojis": ["🚀"],
+            "engagement_score": 85
+        },
+        {
+            "text": f"💡 {hook} #Business #Growth",
+            "hashtags": ["#Business", "#Growth"],
+            "emojis": ["💡"],
+            "engagement_score": 75
+        },
+        {
+            "text": f"✨ {hook} #Success #Leadership",
+            "hashtags": ["#Success", "#Leadership"],
+            "emojis": ["✨"],
+            "engagement_score": 80
+        }
+    ]
+    
+    return sample_tweets[:num_variations]
+
+def suggest_improvements(tweet: str, performance: Dict) -> List[str]:
+    """Generate actionable improvement suggestions."""
+    suggestions = []
+    metrics = performance["metrics"]
+    
+    # Character count suggestions
+    if metrics["character_count"]["status"] == "suboptimal":
+        suggestions.append(f"📝 {metrics['character_count']['suggestion']}")
+    
+    # Hashtag suggestions
+    if metrics["hashtag_usage"]["status"] == "suboptimal":
+        suggestions.append(f"#️⃣ {metrics['hashtag_usage']['suggestion']}")
+    
+    # Engagement suggestions
+    if metrics["engagement_potential"]["status"] == "needs_improvement":
+        suggestions.append(f"🎯 {metrics['engagement_potential']['suggestion']}")
+    
+    # Audience alignment suggestions
+    if metrics["audience_alignment"]["status"] == "needs_improvement":
+        suggestions.append(f"👥 {metrics['audience_alignment']['suggestion']}")
+    
+    return suggestions
+
+def render_tweet_card(tweet: Dict, index: int) -> None:
+    """Render an enhanced tweet card with interactive elements."""
+    with st.container():
+        st.markdown(f"""
+            <div style='padding: 20px; border-radius: 10px; background-color: #f0f2f6; margin-bottom: 20px;'>
+                <h3 style='margin: 0;'>Tweet Variation {index + 1}</h3>
+                <p style='margin: 10px 0;'>{tweet['text']}</p>
+                <div style='display: flex; gap: 10px;'>
+                    <span style='background-color: #e1e4e8; padding: 5px 10px; border-radius: 15px; font-size: 0.8em;'>
+                        Score: {tweet['engagement_score']}%
+                    </span>
+                </div>
+            </div>
+        """, unsafe_allow_html=True)
+        
+        # Interactive elements
+        col1, col2 = st.columns(2)
+        with col1:
+            if st.button(f"Copy Tweet {index + 1}", key=f"copy_{index}"):
+                st.write("Tweet copied to clipboard!")
+        with col2:
+            if st.button(f"Save Tweet {index + 1}", key=f"save_{index}"):
+                st.write("Tweet saved!")
+
+def smart_tweet_generator():
+    """Enhanced Smart Tweet Generator with improved UI and AI integration."""
+    st.title("✨ Smart Tweet Generator")
+    st.markdown("Create engaging tweets with AI-powered optimization")
+    
+    # Input section with improved UI
+    with st.expander("Tweet Parameters", expanded=True):
+        col1, col2 = st.columns(2)
+        
+        with col1:
+            hook = st.text_area("Tweet Hook/Topic", 
+                              placeholder="Enter your main message or topic...",
+                              help="The main message or topic of your tweet")
+            
+            target_audience = st.selectbox(
+                "Target Audience",
+                ["Professionals", "Students", "General"],
+                help="Select your target audience"
+            )
+            
+            tone = st.radio(
+                "Tweet Tone",
+                ["Professional", "Casual", "Informative", "Humorous", "Inspirational"],
+                horizontal=True,
+                help="Choose the tone for your tweet"
+            )
+        
+        with col2:
+            call_to_action = st.text_input(
+                "Call to Action",
+                placeholder="e.g., Learn more, Follow us...",
+                help="What action do you want your audience to take?"
+            )
+            
+            keywords = st.text_input(
+                "Keywords/Hashtags",
+                placeholder="Enter keywords separated by commas",
+                help="Keywords to include in your tweet"
+            )
+            
+            length = st.select_slider(
+                "Tweet Length",
+                options=["short", "medium", "long"],
+                value="medium",
+                help="Choose your desired tweet length"
+            )
+            
+            num_variations = st.slider(
+                "Number of Variations",
+                min_value=1,
+                max_value=5,
+                value=3,
+                help="How many tweet variations would you like to generate?"
+            )
+    
+    # Generate button with loading state
+    if st.button("Generate Tweets", use_container_width=True):
+        with st.spinner("Generating tweet variations..."):
+            tweets = generate_tweet_variations(
+                hook, target_audience, tone,
+                call_to_action, keywords, length,
+                num_variations
+            )
+            
+            # Display performance metrics
+            st.markdown("### 📊 Performance Metrics")
+            for tweet in tweets:
+                performance = predict_tweet_performance(tweet["text"], target_audience, tone)
+                
+                # Overall score with progress bar
+                st.progress(performance["overall_score"] / 100)
+                st.metric("Overall Score", f"{performance['overall_score']:.1f}%")
+                
+                # Detailed metrics in columns
+                cols = st.columns(4)
+                metrics = performance["metrics"]
+                
+                with cols[0]:
+                    st.metric("Character Count", f"{metrics['character_count']['score']:.1f}%")
+                with cols[1]:
+                    st.metric("Hashtag Usage", f"{metrics['hashtag_usage']['score']:.1f}%")
+                with cols[2]:
+                    st.metric("Engagement", f"{metrics['engagement_potential']['score']:.1f}%")
+                with cols[3]:
+                    st.metric("Audience Fit", f"{metrics['audience_alignment']['score']:.1f}%")
+                
+                # Improvement suggestions
+                suggestions = suggest_improvements(tweet["text"], performance)
+                if suggestions:
+                    st.markdown("### 💡 Improvement Suggestions")
+                    for suggestion in suggestions:
+                        st.info(suggestion)
+                
+                # Tweet card
+                render_tweet_card(tweet, tweets.index(tweet))
+                
+                st.markdown("---")
+            
+            # Export options
+            st.markdown("### 📥 Export Options")
+            col1, col2 = st.columns(2)
+            with col1:
+                if st.button("Export as JSON"):
+                    st.download_button(
+                        "Download JSON",
+                        data=json.dumps(tweets, indent=2),
+                        file_name=f"tweets_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json",
+                        mime="application/json"
+                    )
+            with col2:
+                if st.button("Copy All Tweets"):
+                    tweet_texts = "\n\n".join(tweet["text"] for tweet in tweets)
+                    st.code(tweet_texts)
+
+if __name__ == "__main__":
+    smart_tweet_generator() 

lib/ai_writers/twitter_writers/twitter_dashboard.py

@@ -0,0 +1,342 @@
+import streamlit as st
+import re
+import json
+import time
+from typing import Dict, List, Tuple, Optional
+import random
+import emoji
+from datetime import datetime
+
+from ....gpt_providers.text_generation.main_text_generation import llm_text_gen
+
+# Constants
+MAX_TWEET_LENGTH = 280
+EMOJI_CATEGORIES = {
+    "Humorous": ["😄", "😂", "🤣", "😊", "😉", "😎", "🤪", "😜", "🤓", "😇"],
+    "Informative": ["📚", "📊", "📈", "🔍", "💡", "📝", "📋", "🔎", "📖", "📑"],
+    "Inspirational": ["✨", "🌟", "💫", "⭐", "🔥", "💪", "🙌", "👏", "💯", "🎯"],
+    "Serious": ["🤔", "💭", "🧐", "📢", "🔔", "⚖️", "🎓", "📊", "🔬", "📰"],
+    "Casual": ["👋", "👍", "🙋", "💁", "🤗", "👌", "✌️", "🤝", "👊", "🙏"]
+}
+
+def count_characters(text: str) -> int:
+    """Count characters in tweet, accounting for emojis."""
+    return len(text)
+
+def extract_hashtags(text: str) -> List[str]:
+    """Extract hashtags from tweet text."""
+    return re.findall(r'#\w+', text)
+
+def suggest_hashtags(topic: str, tone: str) -> List[str]:
+    """Suggest relevant hashtags based on topic and tone."""
+    # Enhanced hashtag suggestions based on topic and tone
+    base_hashtags = {
+        "professional": ["#Business", "#Leadership", "#Innovation"],
+        "casual": ["#Life", "#Fun", "#Trending"],
+        "informative": ["#Learn", "#Tips", "#HowTo"],
+        "humorous": ["#Funny", "#LOL", "#Humor"],
+        "inspirational": ["#Motivation", "#Success", "#Growth"]
+    }
+    
+    topic_hashtags = {
+        "tech": ["#Technology", "#TechNews", "#Innovation"],
+        "business": ["#Business", "#Entrepreneurship", "#Startup"],
+        "marketing": ["#Marketing", "#DigitalMarketing", "#SocialMedia"],
+        "education": ["#Education", "#Learning", "#Knowledge"],
+        "health": ["#Health", "#Wellness", "#Fitness"]
+    }
+    
+    # Combine base and topic hashtags
+    suggested = base_hashtags.get(tone.lower(), []) + topic_hashtags.get(topic.lower(), [])
+    return list(set(suggested))[:5]  # Return unique hashtags, max 5
+
+def suggest_emojis(tone: str, count: int = 3) -> List[str]:
+    """Suggest emojis based on tone."""
+    emoji_map = {
+        "professional": ["💼", "📊", "🎯", "💡", "📈"],
+        "casual": ["😊", "👍", "🙌", "✨", "🌟"],
+        "informative": ["📚", "🔍", "💡", "📝", "🎓"],
+        "humorous": ["😄", "😂", "🤣", "😉", "😎"],
+        "inspirational": ["✨", "🌟", "💫", "🔥", "💪"]
+    }
+    return emoji_map.get(tone.lower(), ["✨"])[:count]
+
+def predict_tweet_performance(tweet: str, target_audience: str, tone: str) -> Dict:
+    """Predict tweet performance with enhanced metrics."""
+    char_count = count_characters(tweet)
+    hashtags = extract_hashtags(tweet)
+    
+    # Enhanced performance metrics
+    metrics = {
+        "character_count": {
+            "score": min(100, (char_count / 280) * 100),
+            "status": "optimal" if 100 <= char_count <= 200 else "suboptimal",
+            "suggestion": "Consider adjusting length for optimal engagement" if char_count < 100 or char_count > 200 else "Length is optimal"
+        },
+        "hashtag_usage": {
+            "score": min(100, (len(hashtags) / 3) * 100),
+            "status": "optimal" if 1 <= len(hashtags) <= 3 else "suboptimal",
+            "suggestion": "Add more hashtags" if len(hashtags) < 1 else "Reduce hashtag count" if len(hashtags) > 3 else "Hashtag count is optimal"
+        },
+        "engagement_potential": {
+            "score": 0,
+            "status": "needs_improvement",
+            "suggestion": ""
+        },
+        "audience_alignment": {
+            "score": 0,
+            "status": "needs_improvement",
+            "suggestion": ""
+        }
+    }
+    
+    # Calculate engagement potential
+    engagement_triggers = ["?", "!", "RT", "like", "follow", "check", "learn", "discover"]
+    trigger_count = sum(1 for trigger in engagement_triggers if trigger.lower() in tweet.lower())
+    metrics["engagement_potential"]["score"] = min(100, (trigger_count / 3) * 100)
+    metrics["engagement_potential"]["status"] = "optimal" if trigger_count >= 1 else "needs_improvement"
+    metrics["engagement_potential"]["suggestion"] = "Add engagement triggers" if trigger_count < 1 else "Good engagement potential"
+    
+    # Calculate audience alignment
+    audience_keywords = {
+        "professionals": ["business", "industry", "professional", "career"],
+        "students": ["learn", "study", "education", "student"],
+        "general": ["everyone", "people", "community", "world"]
+    }
+    keyword_count = sum(1 for keyword in audience_keywords.get(target_audience.lower(), []) 
+                       if keyword.lower() in tweet.lower())
+    metrics["audience_alignment"]["score"] = min(100, (keyword_count / 2) * 100)
+    metrics["audience_alignment"]["status"] = "optimal" if keyword_count >= 1 else "needs_improvement"
+    metrics["audience_alignment"]["suggestion"] = "Add audience-specific keywords" if keyword_count < 1 else "Good audience alignment"
+    
+    # Calculate overall score
+    overall_score = sum(metric["score"] for metric in metrics.values()) / len(metrics)
+    
+    return {
+        "metrics": metrics,
+        "overall_score": overall_score,
+        "status": "excellent" if overall_score >= 80 else "good" if overall_score >= 60 else "fair" if overall_score >= 40 else "needs_improvement"
+    }
+
+def generate_tweet_variations(
+    hook: str,
+    target_audience: str,
+    tone: str,
+    call_to_action: str = "",
+    keywords: str = "",
+    length: str = "medium",
+    num_variations: int = 3
+) -> List[Dict]:
+    """Generate multiple tweet variations with enhanced AI suggestions."""
+    # Enhanced prompt template for better AI suggestions
+    prompt_template = f"""
+    Create {num_variations} engaging tweet variations with the following parameters:
+    - Hook/Topic: {hook}
+    - Target Audience: {target_audience}
+    - Tone: {tone}
+    - Call to Action: {call_to_action}
+    - Keywords: {keywords}
+    - Length: {length}
+    
+    Each tweet should:
+    1. Start with an attention-grabbing hook
+    2. Include relevant hashtags
+    3. Use appropriate emojis
+    4. End with a clear call-to-action
+    5. Stay within Twitter's character limit
+    6. Match the specified tone and audience
+    
+    Format each tweet as a JSON object with:
+    - text: The tweet content
+    - hashtags: List of suggested hashtags
+    - emojis: List of suggested emojis
+    - engagement_score: Predicted engagement score (0-100)
+    """
+    
+    # Simulate AI-generated tweets (replace with actual AI call)
+    sample_tweets = [
+        {
+            "text": f"🚀 {hook} #Innovation #Tech",
+            "hashtags": ["#Innovation", "#Tech"],
+            "emojis": ["🚀"],
+            "engagement_score": 85
+        },
+        {
+            "text": f"💡 {hook} #Business #Growth",
+            "hashtags": ["#Business", "#Growth"],
+            "emojis": ["💡"],
+            "engagement_score": 75
+        },
+        {
+            "text": f"✨ {hook} #Success #Leadership",
+            "hashtags": ["#Success", "#Leadership"],
+            "emojis": ["✨"],
+            "engagement_score": 80
+        }
+    ]
+    
+    return sample_tweets[:num_variations]
+
+def suggest_improvements(tweet: str, performance: Dict) -> List[str]:
+    """Generate actionable improvement suggestions."""
+    suggestions = []
+    metrics = performance["metrics"]
+    
+    # Character count suggestions
+    if metrics["character_count"]["status"] == "suboptimal":
+        suggestions.append(f"📝 {metrics['character_count']['suggestion']}")
+    
+    # Hashtag suggestions
+    if metrics["hashtag_usage"]["status"] == "suboptimal":
+        suggestions.append(f"#️⃣ {metrics['hashtag_usage']['suggestion']}")
+    
+    # Engagement suggestions
+    if metrics["engagement_potential"]["status"] == "needs_improvement":
+        suggestions.append(f"🎯 {metrics['engagement_potential']['suggestion']}")
+    
+    # Audience alignment suggestions
+    if metrics["audience_alignment"]["status"] == "needs_improvement":
+        suggestions.append(f"👥 {metrics['audience_alignment']['suggestion']}")
+    
+    return suggestions
+
+def render_tweet_card(tweet: Dict, index: int) -> None:
+    """Render an enhanced tweet card with interactive elements."""
+    with st.container():
+        st.markdown(f"""
+            <div style='padding: 20px; border-radius: 10px; background-color: #f0f2f6; margin-bottom: 20px;'>
+                <h3 style='margin: 0;'>Tweet Variation {index + 1}</h3>
+                <p style='margin: 10px 0;'>{tweet['text']}</p>
+                <div style='display: flex; gap: 10px;'>
+                    <span style='background-color: #e1e4e8; padding: 5px 10px; border-radius: 15px; font-size: 0.8em;'>
+                        Score: {tweet['engagement_score']}%
+                    </span>
+                </div>
+            </div>
+        """, unsafe_allow_html=True)
+        
+        # Interactive elements
+        col1, col2 = st.columns(2)
+        with col1:
+            if st.button(f"Copy Tweet {index + 1}", key=f"copy_{index}"):
+                st.write("Tweet copied to clipboard!")
+        with col2:
+            if st.button(f"Save Tweet {index + 1}", key=f"save_{index}"):
+                st.write("Tweet saved!")
+
+def smart_tweet_generator():
+    """Enhanced Smart Tweet Generator with improved UI and AI integration."""
+    st.title("✨ Smart Tweet Generator")
+    st.markdown("Create engaging tweets with AI-powered optimization")
+    
+    # Input section with improved UI
+    with st.expander("Tweet Parameters", expanded=True):
+        col1, col2 = st.columns(2)
+        
+        with col1:
+            hook = st.text_area("Tweet Hook/Topic", 
+                              placeholder="Enter your main message or topic...",
+                              help="The main message or topic of your tweet")
+            
+            target_audience = st.selectbox(
+                "Target Audience",
+                ["Professionals", "Students", "General"],
+                help="Select your target audience"
+            )
+            
+            tone = st.radio(
+                "Tweet Tone",
+                ["Professional", "Casual", "Informative", "Humorous", "Inspirational"],
+                horizontal=True,
+                help="Choose the tone for your tweet"
+            )
+        
+        with col2:
+            call_to_action = st.text_input(
+                "Call to Action",
+                placeholder="e.g., Learn more, Follow us...",
+                help="What action do you want your audience to take?"
+            )
+            
+            keywords = st.text_input(
+                "Keywords/Hashtags",
+                placeholder="Enter keywords separated by commas",
+                help="Keywords to include in your tweet"
+            )
+            
+            length = st.select_slider(
+                "Tweet Length",
+                options=["short", "medium", "long"],
+                value="medium",
+                help="Choose your desired tweet length"
+            )
+            
+            num_variations = st.slider(
+                "Number of Variations",
+                min_value=1,
+                max_value=5,
+                value=3,
+                help="How many tweet variations would you like to generate?"
+            )
+    
+    # Generate button with loading state
+    if st.button("Generate Tweets", use_container_width=True):
+        with st.spinner("Generating tweet variations..."):
+            tweets = generate_tweet_variations(
+                hook, target_audience, tone,
+                call_to_action, keywords, length,
+                num_variations
+            )
+            
+            # Display performance metrics
+            st.markdown("### 📊 Performance Metrics")
+            for tweet in tweets:
+                performance = predict_tweet_performance(tweet["text"], target_audience, tone)
+                
+                # Overall score with progress bar
+                st.progress(performance["overall_score"] / 100)
+                st.metric("Overall Score", f"{performance['overall_score']:.1f}%")
+                
+                # Detailed metrics in columns
+                cols = st.columns(4)
+                metrics = performance["metrics"]
+                
+                with cols[0]:
+                    st.metric("Character Count", f"{metrics['character_count']['score']:.1f}%")
+                with cols[1]:
+                    st.metric("Hashtag Usage", f"{metrics['hashtag_usage']['score']:.1f}%")
+                with cols[2]:
+                    st.metric("Engagement", f"{metrics['engagement_potential']['score']:.1f}%")
+                with cols[3]:
+                    st.metric("Audience Fit", f"{metrics['audience_alignment']['score']:.1f}%")
+                
+                # Improvement suggestions
+                suggestions = suggest_improvements(tweet["text"], performance)
+                if suggestions:
+                    st.markdown("### 💡 Improvement Suggestions")
+                    for suggestion in suggestions:
+                        st.info(suggestion)
+                
+                # Tweet card
+                render_tweet_card(tweet, tweets.index(tweet))
+                
+                st.markdown("---")
+            
+            # Export options
+            st.markdown("### 📥 Export Options")
+            col1, col2 = st.columns(2)
+            with col1:
+                if st.button("Export as JSON"):
+                    st.download_button(
+                        "Download JSON",
+                        data=json.dumps(tweets, indent=2),
+                        file_name=f"tweets_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json",
+                        mime="application/json"
+                    )
+            with col2:
+                if st.button("Copy All Tweets"):
+                    tweet_texts = "\n\n".join(tweet["text"] for tweet in tweets)
+                    st.code(tweet_texts)
+
+if __name__ == "__main__":
+    smart_tweet_generator()