Update app.py

app.py

@@ -1,190 +1,190 @@
-    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'
+import streamlit as st
+import json
+from typing import Dict, List, Any
+import re
 
-    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")
-
-        # 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_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_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_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")
+
+    # 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_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:
+def add_relevant_links(response: str, query: str, knowledge_base: dict) -> str:
         """Add relevant links based on query context"""
         query_lower = query.lower()
         links = []
@@ -205,133 +205,138 @@
         
         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']
-        
-        # 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)
+import streamlit as st
+import json
+from typing import Dict, List, Any
+import re
 
-    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 handle_market_conditions(knowledge_base: dict) -> str:
+    """Handle market condition related queries with perspective"""
+    market_outlook = knowledge_base['personal_details']['perspectives']['market_outlook']
 
-    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 
+    # 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:
+    """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 phrase 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 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..."):
+        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..."):
             # Add user message
             st.session_state.messages.append({"role": "user", "content": prompt})
-            
+
             try:
                 # Generate and display response
                 with st.chat_message("assistant"):
@@ -340,9 +345,9 @@
                     st.session_state.messages.append({"role": "assistant", "content": response})
             except Exception as e:
                 st.error(f"An error occurred: {str(e)}")
-            
+
             st.rerun()
-    
+
     with col2:
         st.markdown("### Quick Questions")
         example_questions = [
@@ -352,7 +357,7 @@
             "What's your journey into ML?",
             "Paste a job description to see how I match!"
         ]
-        
+
         for question in example_questions:
             if st.button(question, key=f"btn_{question}", use_container_width=True):
                 st.session_state.messages.append({"role": "user", "content": question})
@@ -362,11 +367,11 @@
                 except Exception as e:
                     st.error(f"An error occurred: {str(e)}")
                 st.rerun()
-        
+
         st.markdown("---")
         if st.button("Clear Chat", use_container_width=True):
             st.session_state.messages = []
             st.rerun()
 
 if __name__ == "__main__":
-    main()
+    main()