Update app.py

app.py

@@ -1,328 +1,334 @@
-import streamlit as st
-import json
-from typing import Dict, List, Any
-import re
+    import streamlit as st
+    import json
+    from typing import Dict, List, Any
+    import re
 
-def format_project_response(project: dict, indent_level: int = 0) -> str:
-    """Format project details with proper indentation and spacing"""
-    indent = "  " * indent_level
-    
-    response = [f"{indent}• {project['name']}"]
-    response.append(f"{indent}  {project['description']}")
-    
-    if 'skills_used' in project:
-        response.append(f"{indent}  Technologies: {', '.join(project['skills_used'])}")
-    
-    if 'status' in project:
-        status = project['status']
-        if 'development' in status.lower() or 'progress' in status.lower():
-            response.append(f"{indent}  Status: {status}")
-            if 'confidentiality_note' in project:
-                response.append(f"{indent}  Note: {project['confidentiality_note']}")
-    
-    return '\n'.join(response) + '\n'
+    def format_project_response(project: dict, indent_level: int = 0) -> str:
+        """Format project details with proper indentation and spacing"""
+        indent = "  " * indent_level
+        
+        response = [f"{indent}• {project['name']}"]
+        response.append(f"{indent}  {project['description']}")
+        
+        if 'skills_used' in project:
+            response.append(f"{indent}  Technologies: {', '.join(project['skills_used'])}")
+        
+        if 'status' in project:
+            status = project['status']
+            if 'development' in status.lower() or 'progress' in status.lower():
+                response.append(f"{indent}  Status: {status}")
+                if 'confidentiality_note' in project:
+                    response.append(f"{indent}  Note: {project['confidentiality_note']}")
+        
+        return '\n'.join(response) + '\n'
 
-def format_skills_response(skills: dict) -> str:
-    """Format skills with proper hierarchy and spacing"""
-    response = ["My Technical Expertise:\n"]
-    
-    categories = {
-        'Machine Learning & AI': ['core', 'frameworks', 'focus_areas'],
-        'Programming': ['primary', 'libraries', 'tools'],
-        'Data & Analytics': ['databases', 'visualization', 'processing']
-    }
-    
-    for category, subcategories in categories.items():
-        response.append(f"• {category}")
-        for subcat in subcategories:
-            if subcat in skills['machine_learning']:
-                items = skills['machine_learning'][subcat]
-                response.append(f"  - {subcat.title()}: {', '.join(items)}")
-        response.append("")  # Add spacing between categories
-    
-    return '\n'.join(response)
+    def format_skills_response(skills: dict) -> str:
+        """Format skills with proper hierarchy and spacing"""
+        response = ["My Technical Expertise:\n"]
+        
+        categories = {
+            'Machine Learning & AI': ['core', 'frameworks', 'focus_areas'],
+            'Programming': ['primary', 'libraries', 'tools'],
+            'Data & Analytics': ['databases', 'visualization', 'processing']
+        }
+        
+        for category, subcategories in categories.items():
+            response.append(f"• {category}")
+            for subcat in subcategories:
+                if subcat in skills['machine_learning']:
+                    items = skills['machine_learning'][subcat]
+                    response.append(f"  - {subcat.title()}: {', '.join(items)}")
+            response.append("")  # Add spacing between categories
+        
+        return '\n'.join(response)
 
-def analyze_job_description(text: str, knowledge_base: dict) -> str:
-    """Analyze job description and provide detailed alignment"""
-    # Extract key requirements
-    requirements = {
-        'technical_tools': set(),
-        'soft_skills': set(),
-        'responsibilities': set()
-    }
-    
-    # Common technical tools and skills
-    tech_keywords = {
-        'data science', 'analytics', 'visualization', 'tableau', 'python', 
-        'machine learning', 'modeling', 'automation', 'sql', 'data analysis'
-    }
-    
-    # Common soft skills
-    soft_keywords = {
-        'collaborate', 'communicate', 'analyze', 'design', 'implement',
-        'produce insights', 'improve', 'support'
-    }
-    
-    text_lower = text.lower()
-    
-    # Extract company name if present
-    companies = ['rbc', 'shopify', 'google', 'microsoft', 'amazon']
-    company_name = next((company.upper() for company in companies if company in text_lower), None)
-    
-    # Extract requirements
-    for word in tech_keywords:
-        if word in text_lower:
-            requirements['technical_tools'].add(word)
-    
-    for word in soft_keywords:
-        if word in text_lower:
-            requirements['soft_skills'].add(word)
-            
-    # Build response
-    response_parts = []
-    
-    # Company-specific introduction if applicable
-    if company_name:
-        response_parts.append(f"Here's how I align with {company_name}'s requirements:\n")
-    else:
-        response_parts.append("Based on the job requirements, here's how I align:\n")
+    def analyze_job_description(text: str, knowledge_base: dict) -> str:
+        """Analyze job description and provide detailed alignment"""
+        # Extract key requirements
+        requirements = {
+            'technical_tools': set(),
+            'soft_skills': set(),
+            'responsibilities': set()
+        }
+        
+        # Common technical tools and skills
+        tech_keywords = {
+            'data science', 'analytics', 'visualization', 'tableau', 'python', 
+            'machine learning', 'modeling', 'automation', 'sql', 'data analysis'
+        }
+        
+        # Common soft skills
+        soft_keywords = {
+            'collaborate', 'communicate', 'analyze', 'design', 'implement',
+            'produce insights', 'improve', 'support'
+        }
+        
+        text_lower = text.lower()
+        
+        # Extract company name if present
+        companies = ['rbc', 'shopify', 'google', 'microsoft', 'amazon']
+        company_name = next((company.upper() for company in companies if company in text_lower), None)
+        
+        # Extract requirements
+        for word in tech_keywords:
+            if word in text_lower:
+                requirements['technical_tools'].add(word)
+        
+        for word in soft_keywords:
+            if word in text_lower:
+                requirements['soft_skills'].add(word)
+                
+        # Build response
+        response_parts = []
+        
+        # Company-specific introduction if applicable
+        if company_name:
+            response_parts.append(f"Here's how I align with {company_name}'s requirements:\n")
+        else:
+            response_parts.append("Based on the job requirements, here's how I align:\n")
 
-    # Technical Skills Alignment
-    response_parts.append("• Technical Skills Match:")
-    my_relevant_skills = []
-    if 'visualization' in requirements['technical_tools'] or 'tableau' in requirements['technical_tools']:
-        my_relevant_skills.append("  - Proficient in Tableau and data visualization (used in multiple projects)")
-    if 'data analysis' in requirements['technical_tools']:
-        my_relevant_skills.append("  - Strong data analysis skills demonstrated in projects like LoanTap Credit Assessment")
-    if 'machine learning' in requirements['technical_tools'] or 'modeling' in requirements['technical_tools']:
-        my_relevant_skills.append("  - Experienced in building ML models from scratch (demonstrated in algorithm practice projects)")
-    
-    response_parts.extend(my_relevant_skills)
-    response_parts.append("")
-    
-    # Business Understanding
-    response_parts.append("• Business Acumen:")
-    response_parts.append("  - Commerce background provides strong understanding of business requirements")
-    response_parts.append("  - Experience in translating business needs into technical solutions")
-    response_parts.append("  - Proven ability to communicate technical findings to business stakeholders")
-    response_parts.append("")
-    
-    # Project Experience
-    response_parts.append("• Relevant Project Experience:")
-    relevant_projects = []
-    if 'automation' in requirements['technical_tools']:
-        relevant_projects.append("  - Developed AI-powered POS system with automated operations")
-    if 'data analysis' in requirements['technical_tools']:
-        relevant_projects.append("  - Built credit assessment model for LoanTap using comprehensive data analysis")
-    if 'machine learning' in requirements['technical_tools']:
-        relevant_projects.append("  - Created multiple ML models from scratch, including predictive analytics for Ola")
-    
-    response_parts.extend(relevant_projects)
-    response_parts.append("")
-    
-    # Education and Additional Qualifications
-    response_parts.append("• Additional Strengths:")
-    response_parts.append("  - Currently pursuing advanced AI/ML education in Canada")
-    response_parts.append("  - Strong foundation in both technical implementation and business analysis")
-    response_parts.append("  - Experience in end-to-end project delivery and deployment")
-    
-    return '\n'.join(response_parts)
+        # Technical Skills Alignment
+        response_parts.append("• Technical Skills Match:")
+        my_relevant_skills = []
+        if 'visualization' in requirements['technical_tools'] or 'tableau' in requirements['technical_tools']:
+            my_relevant_skills.append("  - Proficient in Tableau and data visualization (used in multiple projects)")
+        if 'data analysis' in requirements['technical_tools']:
+            my_relevant_skills.append("  - Strong data analysis skills demonstrated in projects like LoanTap Credit Assessment")
+        if 'machine learning' in requirements['technical_tools'] or 'modeling' in requirements['technical_tools']:
+            my_relevant_skills.append("  - Experienced in building ML models from scratch (demonstrated in algorithm practice projects)")
+        
+        response_parts.extend(my_relevant_skills)
+        response_parts.append("")
+        
+        # Business Understanding
+        response_parts.append("• Business Acumen:")
+        response_parts.append("  - Commerce background provides strong understanding of business requirements")
+        response_parts.append("  - Experience in translating business needs into technical solutions")
+        response_parts.append("  - Proven ability to communicate technical findings to business stakeholders")
+        response_parts.append("")
+        
+        # Project Experience
+        response_parts.append("• Relevant Project Experience:")
+        relevant_projects = []
+        if 'automation' in requirements['technical_tools']:
+            relevant_projects.append("  - Developed AI-powered POS system with automated operations")
+        if 'data analysis' in requirements['technical_tools']:
+            relevant_projects.append("  - Built credit assessment model for LoanTap using comprehensive data analysis")
+        if 'machine learning' in requirements['technical_tools']:
+            relevant_projects.append("  - Created multiple ML models from scratch, including predictive analytics for Ola")
+        
+        response_parts.extend(relevant_projects)
+        response_parts.append("")
+        
+        # Education and Additional Qualifications
+        response_parts.append("• Additional Strengths:")
+        response_parts.append("  - Currently pursuing advanced AI/ML education in Canada")
+        response_parts.append("  - Strong foundation in both technical implementation and business analysis")
+        response_parts.append("  - Experience in end-to-end project delivery and deployment")
+        
+        return '\n'.join(response_parts)
 
-def format_story_response(knowledge_base: dict) -> str:
-    """Format background story with proper structure"""
-    response_parts = ["My Journey from Commerce to ML/AI:\n"]
-    
-    # Education Background
-    response_parts.append("• Education Background:")
-    response_parts.append(f"  - Commerce degree from {knowledge_base['education']['undergraduate']['institution']}")
-    response_parts.append(f"  - Currently at {knowledge_base['education']['postgraduate'][0]['institution']}")
-    response_parts.append(f"  - Also enrolled at {knowledge_base['education']['postgraduate'][1]['institution']}")
-    response_parts.append("")
-    
-    # Career Transition
-    response_parts.append("• Career Transition:")
-    transition = next((qa['answer'] for qa in knowledge_base['frequently_asked_questions'] 
-                      if 'transition' in qa['question'].lower()), '')
-    response_parts.append(f"  - {transition[:200]}...")
-    response_parts.append("")
-    
-    # Current Focus
-    response_parts.append("• Current Focus:")
-    response_parts.append("  - Building practical ML projects")
-    response_parts.append("  - Advancing AI/ML education in Canada")
-    response_parts.append("")
-    
-    # Goals
-    response_parts.append("• Future Goals:")
-    response_parts.append("  - Secure ML Engineering role in Canada")
-    response_parts.append("  - Develop innovative AI solutions")
-    response_parts.append("  - Contribute to cutting-edge ML projects")
-    
-    return '\n'.join(response_parts)
+    def format_story_response(knowledge_base: dict) -> str:
+        """Format background story with proper structure"""
+        response_parts = ["My Journey from Commerce to ML/AI:\n"]
+        
+        # Education Background
+        response_parts.append("• Education Background:")
+        response_parts.append(f"  - Commerce degree from {knowledge_base['education']['undergraduate']['institution']}")
+        response_parts.append(f"  - Currently at {knowledge_base['education']['postgraduate'][0]['institution']}")
+        response_parts.append(f"  - Also enrolled at {knowledge_base['education']['postgraduate'][1]['institution']}")
+        response_parts.append("")
+        
+        # Career Transition
+        response_parts.append("• Career Transition:")
+        transition = next((qa['answer'] for qa in knowledge_base['frequently_asked_questions'] 
+                        if 'transition' in qa['question'].lower()), '')
+        response_parts.append(f"  - {transition[:200]}...")
+        response_parts.append("")
+        
+        # Current Focus
+        response_parts.append("• Current Focus:")
+        response_parts.append("  - Building practical ML projects")
+        response_parts.append("  - Advancing AI/ML education in Canada")
+        response_parts.append("")
+        
+        # Goals
+        response_parts.append("• Future Goals:")
+        response_parts.append("  - Secure ML Engineering role in Canada")
+        response_parts.append("  - Develop innovative AI solutions")
+        response_parts.append("  - Contribute to cutting-edge ML projects")
+        
+        return '\n'.join(response_parts)
 
-def format_standout_response() -> str:
-    """Format response about standout qualities"""
-    response_parts = ["What Makes Me Stand Out:\n"]
-    response_parts.append("• Unique Background:")
-    response_parts.append("  - Successfully transitioned from commerce to tech")
-    response_parts.append("  - Blend of business acumen and technical expertise")
-    response_parts.append("")
-    
-    response_parts.append("• Practical Experience:")
-    response_parts.append("  - Built multiple ML projects from scratch")
-    response_parts.append("  - Focus on real-world applications")
-    response_parts.append("")
-    
-    response_parts.append("• Technical Depth:")
-    response_parts.append("  - Strong foundation in ML/AI principles")
-    response_parts.append("  - Experience with end-to-end project implementation")
-    response_parts.append("")
-    
-    response_parts.append("• Innovation Focus:")
-    response_parts.append("  - Developing novel solutions in ML/AI")
-    response_parts.append("  - Emphasis on practical impact")
-    
-    return '\n'.join(response_parts)
+    def format_standout_response() -> str:
+        """Format response about standout qualities"""
+        response_parts = ["What Makes Me Stand Out:\n"]
+        response_parts.append("• Unique Background:")
+        response_parts.append("  - Successfully transitioned from commerce to tech")
+        response_parts.append("  - Blend of business acumen and technical expertise")
+        response_parts.append("")
+        
+        response_parts.append("• Practical Experience:")
+        response_parts.append("  - Built multiple ML projects from scratch")
+        response_parts.append("  - Focus on real-world applications")
+        response_parts.append("")
+        
+        response_parts.append("• Technical Depth:")
+        response_parts.append("  - Strong foundation in ML/AI principles")
+        response_parts.append("  - Experience with end-to-end project implementation")
+        response_parts.append("")
+        
+        response_parts.append("• Innovation Focus:")
+        response_parts.append("  - Developing novel solutions in ML/AI")
+        response_parts.append("  - Emphasis on practical impact")
+        
+        return '\n'.join(response_parts)
 
-def add_relevant_links(response: str, query: str, knowledge_base: dict) -> str:
-    """Add relevant links based on query context"""
-    query_lower = query.lower()
-    links = []
-    
-    if any(word in query_lower for word in ['project', 'portfolio', 'work']):
-        links.append(f"\nView my complete portfolio: {knowledge_base['personal_details']['online_presence']['portfolio']}")
-    
-    if any(word in query_lower for word in ['background', 'experience', 'work']):
-        links.append(f"\nConnect with me: {knowledge_base['personal_details']['online_presence']['linkedin']}")
-    
-    for post in knowledge_base['personal_details']['online_presence']['blog_posts']:
-        if 'link' in post and any(word in query_lower for word in post['title'].lower().split()):
-            links.append(f"\nRelated blog post: {post['link']}")
-            break
-    
-    if links:
-        response += '\n' + '\n'.join(links)
-    
-    return response
+    def add_relevant_links(response: str, query: str, knowledge_base: dict) -> str:
+        """Add relevant links based on query context"""
+        query_lower = query.lower()
+        links = []
+        
+        if any(word in query_lower for word in ['project', 'portfolio', 'work']):
+            links.append(f"\nView my complete portfolio: {knowledge_base['personal_details']['online_presence']['portfolio']}")
+        
+        if any(word in query_lower for word in ['background', 'experience', 'work']):
+            links.append(f"\nConnect with me: {knowledge_base['personal_details']['online_presence']['linkedin']}")
+        
+        for post in knowledge_base['personal_details']['online_presence']['blog_posts']:
+            if 'link' in post and any(word in query_lower for word in post['title'].lower().split()):
+                links.append(f"\nRelated blog post: {post['link']}")
+                break
+        
+        if links:
+            response += '\n' + '\n'.join(links)
+        
+        return response
 
-def handle_market_conditions(knowledge_base: dict) -> str:
-    """Handle market condition related queries with perspective"""
-    market_outlook = knowledge_base['personal_details']['perspectives']['market_outlook']
-    
-    response = [
-        market_outlook['job_market'],
-        market_outlook['value_proposition'],
-        market_outlook['strategy']
-    ]
-    
-    return '\n\n'.join(response)
+    def handle_market_conditions(knowledge_base: dict) -> str:
+        """Handle market condition related queries with perspective"""
+        market_outlook = knowledge_base['personal_details']['perspectives']['market_outlook']
+        
+        # Enhanced formatting for better readability
+        response_parts = [
+            "Here's my perspective on the current market situation:\n",
+            f"• {market_outlook['job_market']}",
+            f"\n• {market_outlook['value_proposition']}",
+            f"\n• {market_outlook['strategy']}"
+        ]
+        
+        return '\n'.join(response_parts)
 
-def handle_general_query(query: str, knowledge_base: dict) -> str:
-    """Handle general queries using common_queries section"""
-    query_lower = query.lower()
-    
-    # Check for weather-related queries
-    if any(word in query_lower for word in ['weather', 'temperature', 'climate']):
-        return knowledge_base['personal_details']['common_queries']['weather']
-    
-    # Check for market-related queries
-    if any(word in query_lower for word in ['market', 'job market', 'opportunities']):
-        return handle_market_conditions(knowledge_base)
-    
-    # Default to personal summary for truly general queries
-    return knowledge_base['personal_details']['professional_summary']
+    def handle_general_query(query: str, knowledge_base: dict) -> str:
+        """Enhanced handling of general queries"""
+        query_lower = query.lower()
+        
+        # Improved weather-related query detection
+        if any(word in query_lower for word in ['weather', 'temperature', 'climate', 'cold', 'hot', 'warm']):
+            return knowledge_base['personal_details']['common_queries']['weather']
+        
+        # Enhanced market-related query detection
+        if any(phrase in query_lower for word in ['market', 'job market', 'jobs', 'opportunities', 'hiring']):
+            return handle_market_conditions(knowledge_base)
+        
+        # More specific job fit query detection
+        if any(phrase in query_lower for phrase in ['job description', 'job posting', 'job requirement', 'good fit']):
+            return ("Please paste the job description you'd like me to analyze. I'll evaluate how my skills and experience align with the requirements.")
+        
+        # Default to personal summary
+        return knowledge_base['personal_details']['professional_summary']
 
-def generate_response(query: str, knowledge_base: dict) -> str:
-    """Enhanced response generation with better query handling"""
-    query_lower = query.lower()
-    
-    # Handle project listing requests
-    if any(word in query_lower for word in ['list', 'project', 'portfolio', 'built', 'created', 'developed']):
-        response_parts = ["Here are my key projects:\n"]
-        
-        # Major Projects (under development)
-        response_parts.append("Major Projects (In Development):")
-        for project in knowledge_base['projects']['major_projects']:
-            response_parts.append(format_project_response(project, indent_level=1))
-        
-        # Algorithm Implementation Projects
-        response_parts.append("Completed Algorithm Implementation Projects:")
-        for project in knowledge_base['projects']['algorithm_practice_projects']:
-            response_parts.append(format_project_response(project, indent_level=1))
-        
-        response = '\n'.join(response_parts)
-        return add_relevant_links(response, query, knowledge_base)
-    
-    # Handle job description analysis
-    elif len(query.split()) > 20 and any(phrase in query_lower for phrase in 
-        ['requirements', 'qualifications', 'looking for', 'job description']):
-        return analyze_job_description(query, knowledge_base)
-    
-    # Handle background/story queries
-    elif any(word in query_lower for word in ['background', 'journey', 'story', 'transition']):
-        return format_story_response(knowledge_base)
-    
-    # Handle skill-specific queries
-    elif any(word in query_lower for word in ['skill', 'know', 'technology', 'stack']):
-        return format_skills_response(knowledge_base['skills']['technical_skills'])
-    
-    # Handle standout/unique qualities queries
-    elif any(word in query_lower for word in ['stand out', 'unique', 'different', 'special']):
-        return format_standout_response()
-    
-    # Handle market condition queries
-    elif any(phrase in query_lower for phrase in ['market down', 'job market', 'market conditions']):
-        return handle_market_conditions(knowledge_base)
-    
-    # Handle general queries
-    elif len(query.split()) < 5 or any(word in query_lower for word in ['weather', 'temperature']):
-        return handle_general_query(query, knowledge_base)
-    
-    # Default response
-    return (f"I'm {knowledge_base['personal_details']['professional_summary']}\n\n"
-            "You can ask me about:\n"
-            "• My projects and portfolio\n"
-            "• My journey from commerce to ML/AI\n"
-            "• My technical skills and experience\n"
-            "• My fit for ML/AI roles\n"
-            "Or paste a job description to see how my profile matches!")
+    def generate_response(query: str, knowledge_base: dict) -> str:
+        """Enhanced response generation with improved pattern matching"""
+        query_lower = query.lower()
+        
+        # Enhanced market conditions detection
+        if any(word in query_lower for word in ['market', 'job market', 'hiring']) or \
+        any(phrase in query_lower for phrase in ['market down', 'market conditions', 'current situation']):
+            return handle_market_conditions(knowledge_base)
+        
+        # Enhanced job description analysis detection
+        if ('job description' in query_lower or 'job posting' in query_lower) or \
+        (len(query.split()) > 20 and any(word in query_lower for word in 
+            ['requirements', 'qualifications', 'looking for', 'responsibilities', 'skills needed'])):
+            if len(query.split()) < 20:
+                return "Please paste the complete job description, and I'll analyze how well I match the requirements."
+            return analyze_job_description(query, knowledge_base)
+        
+        # Enhanced weather query detection
+        if any(word in query_lower for word in ['weather', 'temperature', 'climate', 'cold', 'hot', 'warm']):
+            return handle_general_query(query, knowledge_base)
+        
+        # Existing handlers remain unchanged
+        if any(word in query_lower for word in ['list', 'project', 'portfolio', 'built', 'created', 'developed']):
+            response_parts = ["Here are my key projects:\n"]
+            response_parts.append("Major Projects (In Development):")
+            for project in knowledge_base['projects']['major_projects']:
+                response_parts.append(format_project_response(project, indent_level=1))
+            response_parts.append("Completed Algorithm Implementation Projects:")
+            for project in knowledge_base['projects']['algorithm_practice_projects']:
+                response_parts.append(format_project_response(project, indent_level=1))
+            response = '\n'.join(response_parts)
+            return add_relevant_links(response, query, knowledge_base)
+        
+        elif any(word in query_lower for word in ['background', 'journey', 'story', 'transition']):
+            return format_story_response(knowledge_base)
+        
+        elif any(word in query_lower for word in ['skill', 'know', 'technology', 'stack']):
+            return format_skills_response(knowledge_base['skills']['technical_skills'])
+        
+        elif any(word in query_lower for word in ['stand out', 'unique', 'different', 'special']):
+            return format_standout_response()
+        
+        # General query handler for shorter queries
+        elif len(query.split()) < 5:
+            return handle_general_query(query, knowledge_base)
+        
+        # Default response
+        return (f"I'm {knowledge_base['personal_details']['professional_summary']}\n\n"
+                "You can ask me about:\n"
+                "• My projects and portfolio\n"
+                "• My journey from commerce to ML/AI\n"
+                "• My technical skills and experience\n"
+                "• My fit for ML/AI roles\n"
+                "Or paste a job description to see how my profile matches!")
 
-def main():
-    st.title("💬 Chat with Manyue's Portfolio")
-    
-    # Initialize session state
-    if "messages" not in st.session_state:
-        st.session_state.messages = []
-    if "knowledge_base" not in st.session_state:
-        try:
-            with open('knowledge_base.json', 'r', encoding='utf-8') as f:
-                st.session_state.knowledge_base = json.load(f)
-        except FileNotFoundError:
-            st.error("Knowledge base file not found.")
-            return
-    
-    # Display welcome message
-    if "displayed_welcome" not in st.session_state:
-        st.write("""
-        Hi! I'm Manyue's AI assistant. I can tell you about:
-        - My journey from commerce to ML/AI
-        - My technical skills and projects
-        - My fit for ML/AI roles
-        - You can also paste job descriptions to see how my profile matches!
-        """)
-        st.session_state.displayed_welcome = True
-    
-    # Create two columns with adjusted ratios
-    col1, col2 = st.columns([4, 1])
-    
-    with col1:
-        # Display chat messages
-        for message in st.session_state.messages:
-            with st.chat_message(message["role"]):
-                st.markdown(message["content"])
-        
-        # Chat input
-        if prompt := st.chat_input("Ask me anything or paste a job description..."):
+    def main():
+        st.title("💬 Chat with Manyue's Portfolio")
+        
+        # Initialize session state
+        if "messages" not in st.session_state:
+            st.session_state.messages = []
+        if "knowledge_base" not in st.session_state:
+            try:
+                with open('knowledge_base.json', 'r', encoding='utf-8') as f:
+                    st.session_state.knowledge_base = json.load(f)
+            except FileNotFoundError:
+                st.error("Knowledge base file not found.")
+                return
+        
+        # Display welcome message
+        if "displayed_welcome" not in st.session_state:
+            st.write("""
+            Hi! I'm Manyue's AI assistant. I can tell you about:
+            - My journey from commerce to ML/AI
+            - My technical skills and projects
+            - My fit for ML/AI roles
+            - You can also paste job descriptions to see how my profile matches!
+            """)
+            st.session_state.displayed_welcome = True
+        
+        # Create two columns with adjusted ratios
+        col1, col2 = st.columns([4, 1])
+        
+        with col1:
+            # Display chat messages
+            for message in st.session_state.messages:
+                with st.chat_message(message["role"]):
+                    st.markdown(message["content"])
+            
+            # Chat input
+            if prompt := st.chat_input("Ask me anything or paste a job description..."):
             # Add user message
             st.session_state.messages.append({"role": "user", "content": prompt})