#!/usr/bin/env python3 """ LinkedIn Profile Enhancer - Gradio Interface (app2.py) A beautiful web interface for the LinkedIn Profile Enhancer using Gradio """ import sys import os import time import json from typing import Dict, Any, Tuple, Optional import gradio as gr from PIL import Image import requests from io import BytesIO # Add project root to path sys.path.append(os.path.dirname(os.path.abspath(__file__))) from agents.orchestrator import ProfileOrchestrator from agents.scraper_agent import ScraperAgent from agents.analyzer_agent import AnalyzerAgent from agents.content_agent import ContentAgent class LinkedInEnhancerGradio: """Gradio # Action Buttons - Single Enhanced Button with gr.Row(): enhance_btn = gr.Button("🚀 Enhance LinkedIn Profile", variant="primary", size="lg") # Action Buttons - Single Enhanced Button with Download with gr.Row(): enhance_btn = gr.Button("🚀 Enhance LinkedIn Profile", variant="primary", size="lg")rface for LinkedIn Profile Enhancer""" def __init__(self): self.orchestrator = ProfileOrchestrator() self.current_profile_data = None self.current_analysis = None self.current_suggestions = None def test_api_connections(self) -> Tuple[str, str]: """Test API connections and return status""" apify_status = "❌ Failed" openai_status = "❌ Failed" try: scraper = ScraperAgent() if scraper.test_apify_connection(): apify_status = "✅ Connected" except Exception as e: apify_status = f"❌ Error: {str(e)[:50]}..." try: content_agent = ContentAgent() if content_agent.test_openai_connection(): openai_status = "✅ Connected" except Exception as e: openai_status = f"❌ Error: {str(e)[:50]}..." return apify_status, openai_status def load_profile_image(self, image_url: str) -> Optional[Image.Image]: """Load profile image from URL""" try: if image_url: response = requests.get(image_url, timeout=10) if response.status_code == 200: return Image.open(BytesIO(response.content)) except Exception as e: print(f"Error loading image: {e}") return None def enhance_linkedin_profile(self, linkedin_url: str, job_description: str = "") -> Tuple[str, str, str, str, str, str, str, str, Optional[Image.Image]]: """Complete LinkedIn profile enhancement with extraction, analysis, and suggestions""" if not linkedin_url.strip(): return "❌ Error", "Please enter a LinkedIn profile URL", "", "", "", "", "", "", None if not any(pattern in linkedin_url.lower() for pattern in ['linkedin.com/in/', 'www.linkedin.com/in/']): return "❌ Error", "Please enter a valid LinkedIn profile URL", "", "", "", "", "", "", None try: # Step 1: Extract profile data self.orchestrator.memory.session_data.clear() profile_data = self.orchestrator.scraper.extract_profile_data(linkedin_url) self.current_profile_data = profile_data # Format basic info basic_info = f""" **Name:** {profile_data.get('name', 'N/A')} **Headline:** {profile_data.get('headline', 'N/A')} **Location:** {profile_data.get('location', 'N/A')} **Connections:** {profile_data.get('connections', 'N/A')} **Followers:** {profile_data.get('followers', 'N/A')} **Email:** {profile_data.get('email', 'N/A')} **Current Job:** {profile_data.get('job_title', 'N/A')} at {profile_data.get('company_name', 'N/A')} """ # Format about section about_section = profile_data.get('about', 'No about section available') # Format experience experience_text = "" for i, exp in enumerate(profile_data.get('experience', [])[:5], 1): experience_text += f""" **{i}. {exp.get('title', 'Position')}** - Company: {exp.get('company', 'N/A')} - Duration: {exp.get('duration', 'N/A')} - Location: {exp.get('location', 'N/A')} - Current: {'Yes' if exp.get('is_current') else 'No'} """ if exp.get('description'): experience_text += f"- Description: {exp.get('description')[:200]}...\n" experience_text += "\n" # Format education and skills education_text = "" for i, edu in enumerate(profile_data.get('education', []), 1): education_text += f""" **{i}. {edu.get('school', 'School')}** - Degree: {edu.get('degree', 'N/A')} - Field: {edu.get('field', 'N/A')} - Year: {edu.get('year', 'N/A')} - Grade: {edu.get('grade', 'N/A')} """ skills_text = ", ".join(profile_data.get('skills', [])[:20]) if len(profile_data.get('skills', [])) > 20: skills_text += f" ... and {len(profile_data.get('skills', [])) - 20} more" details_text = f""" ## 🎓 Education {education_text if education_text else "No education information available"} ## 🛠️ Skills {skills_text if skills_text else "No skills information available"} ## 🏆 Certifications {len(profile_data.get('certifications', []))} certifications found ## 📊 Additional Data - Projects: {len(profile_data.get('projects', []))} - Publications: {len(profile_data.get('publications', []))} - Recommendations: {len(profile_data.get('recommendations', []))} """ # Load profile image profile_image = self.load_profile_image(profile_data.get('profile_image_hq') or profile_data.get('profile_image')) # Step 2: Analyze profile automatically try: analysis = self.orchestrator.analyzer.analyze_profile( self.current_profile_data, job_description ) self.current_analysis = analysis # Format analysis results analysis_text = f""" ## 📊 Analysis Results **Overall Rating:** {analysis.get('overall_rating', 'Unknown')} **Completeness Score:** {analysis.get('completeness_score', 0):.1f}% **Job Match Score:** {analysis.get('job_match_score', 0):.1f}% ### 🌟 Strengths """ for strength in analysis.get('strengths', []): analysis_text += f"- {strength}\n" analysis_text += "\n### ⚠️ Areas for Improvement\n" for weakness in analysis.get('weaknesses', []): analysis_text += f"- {weakness}\n" # Keyword analysis keyword_analysis = analysis.get('keyword_analysis', {}) keywords_text = "" if keyword_analysis: found_keywords = keyword_analysis.get('found_keywords', []) missing_keywords = keyword_analysis.get('missing_keywords', []) keywords_text = f""" ## 🔍 Keyword Analysis **Found Keywords:** {', '.join(found_keywords[:10])} {"..." if len(found_keywords) > 10 else ""} **Missing Keywords:** {', '.join(missing_keywords[:5])} {"..." if len(missing_keywords) > 5 else ""} """ except Exception as e: analysis_text = f"⚠️ Analysis failed: {str(e)}" keywords_text = "" # Step 3: Generate suggestions automatically try: suggestions = self.orchestrator.content_generator.generate_suggestions( self.current_analysis, job_description ) self.current_suggestions = suggestions suggestions_text = "" for category, items in suggestions.items(): if category == 'ai_generated_content': ai_content = items if isinstance(items, dict) else {} # AI Headlines if 'ai_headlines' in ai_content and ai_content['ai_headlines']: suggestions_text += "## ✨ Professional Headlines\n\n" for i, headline in enumerate(ai_content['ai_headlines'], 1): cleaned_headline = headline.strip('"').replace('\\"', '"') if cleaned_headline.startswith(('1.', '2.', '3.', '4.', '5.')): cleaned_headline = cleaned_headline[2:].strip() suggestions_text += f"{i}. {cleaned_headline}\n\n" # AI About Section if 'ai_about_section' in ai_content and ai_content['ai_about_section']: suggestions_text += "## 📄 Enhanced About Section\n\n" suggestions_text += f"```\n{ai_content['ai_about_section']}\n```\n\n" # AI Experience Descriptions if 'ai_experience_descriptions' in ai_content and ai_content['ai_experience_descriptions']: suggestions_text += "## 💼 Experience Description Ideas\n\n" for desc in ai_content['ai_experience_descriptions']: suggestions_text += f"- {desc}\n" suggestions_text += "\n" else: # Standard categories category_name = category.replace('_', ' ').title() suggestions_text += f"## 📋 {category_name}\n\n" if isinstance(items, list): for item in items: suggestions_text += f"- {item}\n" else: suggestions_text += f"- {items}\n" suggestions_text += "\n" except Exception as e: suggestions_text = f"⚠️ Suggestions generation failed: {str(e)}" return "✅ Profile Enhanced Successfully", basic_info, about_section, experience_text, details_text, analysis_text, keywords_text, suggestions_text, profile_image except Exception as e: return "❌ Error", f"Failed to enhance profile: {str(e)}", "", "", "", "", "", "", None def analyze_profile(self, job_description: str = "") -> Tuple[str, str, str]: """Analyze the extracted profile data""" if not self.current_profile_data: return "❌ Error", "Please extract profile data first", "" try: # Analyze profile analysis = self.orchestrator.analyzer.analyze_profile( self.current_profile_data, job_description ) self.current_analysis = analysis # Format analysis results analysis_text = f""" ## 📊 Analysis Results **Overall Rating:** {analysis.get('overall_rating', 'Unknown')} **Completeness Score:** {analysis.get('completeness_score', 0):.1f}% **Job Match Score:** {analysis.get('job_match_score', 0):.1f}% ### 🌟 Strengths """ for strength in analysis.get('strengths', []): analysis_text += f"- {strength}\n" analysis_text += "\n### � Areas for Improvement\n" for weakness in analysis.get('weaknesses', []): analysis_text += f"- {weakness}\n" # Keyword analysis keyword_analysis = analysis.get('keyword_analysis', {}) keywords_text = "" if keyword_analysis: found_keywords = keyword_analysis.get('found_keywords', []) missing_keywords = keyword_analysis.get('missing_keywords', []) keywords_text = f""" ## 🔍 Keyword Analysis **Found Keywords:** {', '.join(found_keywords[:10])} {"..." if len(found_keywords) > 10 else ""} **Missing Keywords:** {', '.join(missing_keywords[:5])} {"..." if len(missing_keywords) > 5 else ""} """ return "✅ Success", analysis_text, keywords_text except Exception as e: return "❌ Error", f"Failed to analyze profile: {str(e)}", "" def generate_suggestions(self, job_description: str = "") -> Tuple[str, str]: """Generate enhancement suggestions""" if not self.current_analysis: return "❌ Error", "Please analyze profile first" try: # Generate suggestions suggestions = self.orchestrator.content_generator.generate_suggestions( self.current_analysis, job_description ) self.current_suggestions = suggestions suggestions_text = "" ai_content_text = "" for category, items in suggestions.items(): if category == 'ai_generated_content': ai_content = items if isinstance(items, dict) else {} # AI Headlines if 'ai_headlines' in ai_content and ai_content['ai_headlines']: ai_content_text += "## ✨ Professional Headlines\n\n" for i, headline in enumerate(ai_content['ai_headlines'], 1): cleaned_headline = headline.strip('"').replace('\\"', '"') if cleaned_headline.startswith(('1.', '2.', '3.', '4.', '5.')): cleaned_headline = cleaned_headline[2:].strip() ai_content_text += f"{i}. {cleaned_headline}\n\n" # AI About Section if 'ai_about_section' in ai_content and ai_content['ai_about_section']: ai_content_text += "## � Enhanced About Section\n\n" ai_content_text += f"```\n{ai_content['ai_about_section']}\n```\n\n" # AI Experience Descriptions if 'ai_experience_descriptions' in ai_content and ai_content['ai_experience_descriptions']: ai_content_text += "## 💼 Experience Description Ideas\n\n" for desc in ai_content['ai_experience_descriptions']: ai_content_text += f"- {desc}\n" ai_content_text += "\n" else: # Standard categories category_name = category.replace('_', ' ').title() suggestions_text += f"## 📋 {category_name}\n\n" if isinstance(items, list): for item in items: suggestions_text += f"- {item}\n" else: suggestions_text += f"- {items}\n" suggestions_text += "\n" return "✅ Success", suggestions_text + ai_content_text except Exception as e: return "❌ Error", f"Failed to generate suggestions: {str(e)}" def export_results(self, linkedin_url: str) -> str: """Export all results to a comprehensive downloadable file""" if not self.current_profile_data: return None # Return None if no data try: # Create filename with timestamp profile_name = linkedin_url.split('/in/')[-1].split('/')[0] if linkedin_url else 'profile' timestamp = time.strftime('%Y%m%d_%H%M%S') filename = f"LinkedIn_Profile_Enhancement_{profile_name}_{timestamp}.md" # Create full file path file_path = os.path.join(os.getcwd(), filename) # Compile comprehensive report content = f"""# 🚀 LinkedIn Profile Enhancement Report **Generated:** {time.strftime('%B %d, %Y at %I:%M %p')} **Profile URL:** [{linkedin_url}]({linkedin_url}) **Enhancement Date:** {time.strftime('%Y-%m-%d')} --- ## 📊 Executive Summary This comprehensive report provides a detailed analysis of your LinkedIn profile along with AI-powered enhancement suggestions to improve your professional visibility and job match potential. --- ## 👤 Basic Profile Information | Field | Current Value | |-------|---------------| | **Name** | {self.current_profile_data.get('name', 'N/A')} | | **Professional Headline** | {self.current_profile_data.get('headline', 'N/A')} | | **Location** | {self.current_profile_data.get('location', 'N/A')} | | **Connections** | {self.current_profile_data.get('connections', 'N/A')} | | **Followers** | {self.current_profile_data.get('followers', 'N/A')} | | **Email** | {self.current_profile_data.get('email', 'N/A')} | | **Current Position** | {self.current_profile_data.get('job_title', 'N/A')} at {self.current_profile_data.get('company_name', 'N/A')} | --- ## 📝 Current About Section ``` {self.current_profile_data.get('about', 'No about section available')} ``` --- ## 💼 Professional Experience """ # Add experience details for i, exp in enumerate(self.current_profile_data.get('experience', []), 1): content += f""" ### {i}. {exp.get('title', 'Position')} **Company:** {exp.get('company', 'N/A')} **Duration:** {exp.get('duration', 'N/A')} **Location:** {exp.get('location', 'N/A')} **Current Role:** {'Yes' if exp.get('is_current') else 'No'} """ if exp.get('description'): content += f"**Description:**\n```\n{exp.get('description')}\n```\n\n" # Add education content += "---\n\n## 🎓 Education\n\n" for i, edu in enumerate(self.current_profile_data.get('education', []), 1): content += f""" ### {i}. {edu.get('school', 'School')} - **Degree:** {edu.get('degree', 'N/A')} - **Field of Study:** {edu.get('field', 'N/A')} - **Year:** {edu.get('year', 'N/A')} - **Grade:** {edu.get('grade', 'N/A')} """ # Add skills skills = self.current_profile_data.get('skills', []) content += f"""--- ## 🛠️ Skills & Expertise **Total Skills Listed:** {len(skills)} """ if skills: # Group skills for better readability skills_per_line = 5 for i in range(0, len(skills), skills_per_line): skill_group = skills[i:i+skills_per_line] content += f"- {' • '.join(skill_group)}\n" # Add certifications and additional data content += f""" --- ## 🏆 Additional Profile Data | Category | Count | |----------|-------| | **Certifications** | {len(self.current_profile_data.get('certifications', []))} | | **Projects** | {len(self.current_profile_data.get('projects', []))} | | **Publications** | {len(self.current_profile_data.get('publications', []))} | | **Recommendations** | {len(self.current_profile_data.get('recommendations', []))} | """ # Add analysis results if available if self.current_analysis: content += f"""--- ## 📈 AI Analysis Results ### Overall Assessment - **Overall Rating:** {self.current_analysis.get('overall_rating', 'Unknown')} - **Profile Completeness:** {self.current_analysis.get('completeness_score', 0):.1f}% - **Job Match Score:** {self.current_analysis.get('job_match_score', 0):.1f}% ### 🌟 Identified Strengths """ for strength in self.current_analysis.get('strengths', []): content += f"- {strength}\n" content += "\n### ⚠️ Areas for Improvement\n" for weakness in self.current_analysis.get('weaknesses', []): content += f"- {weakness}\n" # Add keyword analysis keyword_analysis = self.current_analysis.get('keyword_analysis', {}) if keyword_analysis: found_keywords = keyword_analysis.get('found_keywords', []) missing_keywords = keyword_analysis.get('missing_keywords', []) content += f""" ### 🔍 Keyword Analysis **Found Keywords ({len(found_keywords)}):** {', '.join(found_keywords[:15])} {"..." if len(found_keywords) > 15 else ""} **Missing Keywords ({len(missing_keywords)}):** {', '.join(missing_keywords[:10])} {"..." if len(missing_keywords) > 10 else ""} """ # Add enhancement suggestions if available if self.current_suggestions: content += "\n---\n\n## 💡 AI-Powered Enhancement Suggestions\n\n" for category, items in self.current_suggestions.items(): if category == 'ai_generated_content': ai_content = items if isinstance(items, dict) else {} # AI Headlines if 'ai_headlines' in ai_content and ai_content['ai_headlines']: content += "### ✨ Professional Headlines (Choose Your Favorite)\n\n" for i, headline in enumerate(ai_content['ai_headlines'], 1): cleaned_headline = headline.strip('"').replace('\\"', '"') if cleaned_headline.startswith(('1.', '2.', '3.', '4.', '5.')): cleaned_headline = cleaned_headline[2:].strip() content += f"{i}. {cleaned_headline}\n\n" # AI About Section if 'ai_about_section' in ai_content and ai_content['ai_about_section']: content += "### 📄 Enhanced About Section\n\n" content += f"```\n{ai_content['ai_about_section']}\n```\n\n" # AI Experience Descriptions if 'ai_experience_descriptions' in ai_content and ai_content['ai_experience_descriptions']: content += "### 💼 Experience Description Enhancements\n\n" for j, desc in enumerate(ai_content['ai_experience_descriptions'], 1): content += f"{j}. {desc}\n\n" else: # Standard categories category_name = category.replace('_', ' ').title() content += f"### 📋 {category_name}\n\n" if isinstance(items, list): for item in items: content += f"- {item}\n" else: content += f"- {items}\n" content += "\n" # Add action items and next steps content += """--- ## 🎯 Recommended Action Items ### Immediate Actions (This Week) 1. **Update Headline:** Choose one of the AI-generated headlines that best reflects your goals 2. **Enhance About Section:** Implement the suggested about section improvements 3. **Add Missing Keywords:** Incorporate relevant missing keywords naturally into your content 4. **Complete Profile Sections:** Fill in any incomplete sections identified in the analysis ### Medium-term Goals (This Month) 1. **Experience Descriptions:** Update job descriptions using the AI-generated suggestions 2. **Skills Optimization:** Add relevant skills identified in the keyword analysis 3. **Network Growth:** Aim to increase connections in your industry 4. **Content Strategy:** Start sharing relevant professional content ### Long-term Strategy (Next 3 Months) 1. **Regular Updates:** Keep your profile current with new achievements and skills 2. **Engagement:** Actively engage with your network's content 3. **Personal Branding:** Develop a consistent professional brand across all sections 4. **Performance Monitoring:** Track profile views and connection requests --- ## 📞 Additional Resources - **LinkedIn Profile Optimization Guide:** [LinkedIn Help Center](https://www.linkedin.com/help/linkedin) - **Professional Photography:** Consider professional headshots for profile picture - **Skill Assessments:** Take LinkedIn skill assessments to verify your expertise - **Industry Groups:** Join relevant professional groups in your field *This is an automated analysis. Results may vary based on individual goals and industry standards.* """ # Save to file (this will be downloaded by the browser) with open(file_path, 'w', encoding='utf-8') as f: f.write(content) return file_path # Return file path for download except Exception as e: print(f"Export error: {str(e)}") return None def create_gradio_interface(): """Create and return the Gradio interface""" app = LinkedInEnhancerGradio() # Custom CSS for beautiful styling custom_css = """ .gradio-container { font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif; max-width: 1200px; margin: 0 auto; } .header-text { text-align: center; background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); color: white; padding: 2rem; border-radius: 10px; margin-bottom: 2rem; } .status-box { padding: 1rem; border-radius: 8px; margin: 0.5rem 0; } .success { background-color: #d4edda; border: 1px solid #c3e6cb; color: #155724; } .error { background-color: #f8d7da; border: 1px solid #f5c6cb; color: #721c24; } .info { background-color: #e7f3ff; border: 1px solid #b3d7ff; color: #0c5460; } """ with gr.Blocks(css=custom_css, title="🚀 LinkedIn Profile Enhancer", theme=gr.themes.Soft()) as demo: # Header gr.HTML("""

🚀 LinkedIn Profile Enhancer

AI-powered LinkedIn profile analysis and enhancement suggestions

🔍
Real Scraping
🤖
AI Analysis
🎯
Smart Suggestions
📊
Rich Data
""") # API Status Section with gr.Row(): with gr.Column(scale=1): gr.Markdown("## 🔌 API Status") with gr.Row(): apify_status = gr.Textbox(label="📡 Apify API", interactive=False, value="Testing...") openai_status = gr.Textbox(label="🤖 OpenAI API", interactive=False, value="Testing...") test_btn = gr.Button("🔄 Test Connections", variant="secondary") # Main Input Section with gr.Row(): with gr.Column(scale=2): linkedin_url = gr.Textbox( label="🔗 LinkedIn Profile URL", placeholder="https://www.linkedin.com/in/your-profile", lines=1 ) job_description = gr.Textbox( label="🎯 Target Job Description (Optional)", placeholder="Paste the job description here for tailored suggestions...", lines=5 ) with gr.Column(scale=1): profile_image = gr.Image( label="📸 Profile Picture", height=200, width=200 ) # Action Buttons - Single Enhanced Button with gr.Row(): enhance_btn = gr.Button("� Enhance LinkedIn Profile", variant="primary", size="lg") export_btn = gr.Button("📁 Export Results", variant="secondary") # Results Section with Tabs with gr.Tabs(): with gr.TabItem("📊 Basic Information"): enhance_status = gr.Textbox(label="Status", interactive=False) basic_info = gr.Markdown(label="Basic Information") with gr.TabItem("📝 About Section"): about_section = gr.Markdown(label="About Section") with gr.TabItem("💼 Experience"): experience_info = gr.Markdown(label="Work Experience") with gr.TabItem("🎓 Education & Skills"): education_skills = gr.Markdown(label="Education & Skills") with gr.TabItem("📈 Analysis Results"): analysis_results = gr.Markdown(label="Analysis Results") keyword_analysis = gr.Markdown(label="Keyword Analysis") with gr.TabItem("💡 Enhancement Suggestions"): suggestions_content = gr.Markdown(label="Enhancement Suggestions") with gr.TabItem("� Download Report"): download_status = gr.Textbox(label="Download Status", interactive=False) download_btn = gr.DownloadButton("📥 Download Report", variant="secondary", visible=False) gr.Markdown(""" ### � **Download Complete Report** After enhancing your profile, click the **Download Report** button to get a comprehensive markdown file containing: #### 📊 **Complete Profile Analysis** - Basic profile information and current content - Detailed experience and education sections - Skills analysis and completeness scoring #### 🤖 **AI Enhancement Suggestions** - Professional headline options - Enhanced about section recommendations - Experience description improvements - Keyword optimization suggestions #### 🎯 **Action Plan** - Immediate action items (this week) - Medium-term goals (this month) - Long-term strategy (next 3 months) - Additional resources and tips **File Format:** Markdown (.md) - Compatible with GitHub, Notion, and most text editors """) # Event Handlers def on_test_connections(): apify, openai = app.test_api_connections() return apify, openai def on_enhance_profile(url, job_desc): status, basic, about, exp, details, analysis, keywords, suggestions, image = app.enhance_linkedin_profile(url, job_desc) # Show download button if enhancement was successful download_visible = status.startswith("✅") download_message = "✅ Profile enhanced! You can now download the report." if download_visible else "❌ Please enhance profile first to download report." return ( status, basic, about, exp, details, analysis, keywords, suggestions, image, gr.DownloadButton("📥 Download Report", variant="secondary", visible=download_visible), download_message ) def on_download_report(url): file_path = app.export_results(url) if file_path and os.path.exists(file_path): return file_path return None # Connect events test_btn.click( fn=on_test_connections, outputs=[apify_status, openai_status] ) enhance_btn.click( fn=on_enhance_profile, inputs=[linkedin_url, job_description], outputs=[enhance_status, basic_info, about_section, experience_info, education_skills, analysis_results, keyword_analysis, suggestions_content, profile_image, download_btn, download_status] ) download_btn.click( fn=on_download_report, inputs=[linkedin_url], outputs=[download_btn] ) # Auto-test connections on load demo.load( fn=on_test_connections, outputs=[apify_status, openai_status] ) # Footer gr.HTML("""

🚀 LinkedIn Profile Enhancer | Powered by AI | Built with ❤️ using Gradio

Data scraped with respect to LinkedIn's ToS | Uses OpenAI GPT-4o-mini and Apify

""") return demo def main(): """Main function""" # Check if running with command line arguments (for backward compatibility) if len(sys.argv) > 1: if sys.argv[1] == '--help': print(""" LinkedIn Profile Enhancer - Gradio Interface Usage: python app.py # Launch Gradio web interface python app.py --help # Show this help Web Interface Features: - Beautiful modern UI - Real-time profile extraction - AI-powered analysis - Enhancement suggestions - Export functionality - Profile image display """) return else: print("❌ Unknown argument. Use --help for usage information.") return # Launch Gradio interface print("🚀 Starting LinkedIn Profile Enhancer...") print("📱 Launching Gradio interface...") # FIX: Create the demo by calling the function demo = create_gradio_interface() # FIX: Use proper launch settings for Hugging Face Spaces demo.launch( share=False, # Remove share=True for HF Spaces server_name="0.0.0.0", # Important for HF Spaces server_port=7860, # Default port for HF Spaces show_error=True ) if __name__ == "__main__": main()