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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.

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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'
]

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

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lib/ai_writers/linkedin_writer/modules/company_page_generator/linkedin_company_page_generator_ui.py Normal file
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"""
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
""")

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# 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

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"""
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']

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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*
---

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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()

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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()