Update app.py

app.py

@@ -1,253 +1,86 @@
 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']}")
+def format_project_response(project: dict) -> str:
+    """Format project details with clear separation"""
+    response = [f"\n• {project['name']}:"]
+    response.append(f"  Description: {project['description']}")
     
     if 'skills_used' in project:
-        response.append(f"{indent}  Technologies: {', '.join(project['skills_used'])}")
+        response.append(f"\n  Technologies Used:")
+        response.append(f"  {', '.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']}")
+        response.append(f"\n  Current Status: {project['status']}")
+        if 'confidentiality_note' in project:
+            response.append(f"  Note: {project['confidentiality_note']}")
     
-    return '\n'.join(response) + '\n'  # Add extra newline for spacing
+    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
+def get_philosophical_response(query: str, knowledge_base: dict) -> str:
+    """Handle philosophical or market-related queries"""
+    query_lower = query.lower()
     
-    return '\n'.join(response)
+    # Market-related response
+    if any(word in query_lower for word in ['market', 'job', 'opportunity', 'down']):
+        return """I believe success in any market comes down to quality of effort and preparation. While the market may have cycles, I focus on:
 
-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")
+• Continuous Skill Development:
+  - Building practical projects that solve real problems
+  - Staying updated with latest ML/AI trends
+  - Enhancing my technical portfolio
 
-    # 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("")  # Add spacing
-    
-    # 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("")  # Add spacing
-    
-    # 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("")  # Add spacing
-    
-    # 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)
+• Value Proposition:
+  - Unique combination of business and technical skills
+  - Focus on practical implementation
+  - Strong problem-solving approach
 
-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("")  # Add spacing
-    
-    # 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]}...")  # Truncate for readability
-    response_parts.append("")  # Add spacing
-    
-    # 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("")  # Add spacing
-    
-    # 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)
+I see this period as an opportunity to strengthen my skills and build more impactful projects."""
 
-def add_relevant_links(response: str, query: str, knowledge_base: dict) -> str:
-    """Add relevant links based on query context"""
-    query_lower = query.lower()
-    links = []
-    
-    # Add links strategically based on context
-    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
+    # Off-topic response
+    if any(word in query_lower for word in ['weather', 'temperature', 'climate']):
+        return """I'm focused on discussing my ML/AI journey and projects. For weather information, I'd recommend checking local weather services.
+
+Would you like to know about:
+• My ML projects and technical skills?
+• My journey from commerce to tech?
+• My approach to the current job market?"""
+
+    return None  # Return None if not a philosophical query
 
 def generate_response(query: str, knowledge_base: dict) -> str:
-    """Generate enhanced responses using the knowledge base"""
+    """Enhanced response generation with better handling of various queries"""
     query_lower = query.lower()
     
+    # First check for philosophical/off-topic queries
+    philosophical_response = get_philosophical_response(query, knowledge_base)
+    if philosophical_response:
+        return philosophical_response
+    
     # 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"]
+        response_parts = ["Here are my key projects:"]
         
-        # Major Projects (under development)
-        response_parts.append("Major Projects (In Development):")
+        # Major Projects
+        response_parts.append("\nMajor Projects (In Development):")
         for project in knowledge_base['projects']['major_projects']:
-            response_parts.append(format_project_response(project, indent_level=1))
+            response_parts.append(format_project_response(project))
         
-        # Algorithm Implementation Projects
-        response_parts.append("Completed Algorithm Implementation Projects:")
+        # Algorithm Projects
+        response_parts.append("\nCompleted Algorithm Implementation Projects:")
         for project in knowledge_base['projects']['algorithm_practice_projects']:
-            response_parts.append(format_project_response(project, indent_level=1))
+            response_parts.append(format_project_response(project))
         
         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']):
-        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("")
+        # Add relevant links
+        if 'online_presence' in knowledge_base.get('personal_details', {}):
+            response += f"\n\nView my complete portfolio: {knowledge_base['personal_details']['online_presence']['portfolio']}"
         
-        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)
+        return response
     
-    # 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!")
+    # [Rest of your existing response handlers]
 
 def main():
     st.title("💬 Chat with Manyue's Portfolio")
@@ -257,7 +90,7 @@ def main():
         st.session_state.messages = []
     if "knowledge_base" not in st.session_state:
         try:
-            with open('knowledge_base.json', 'r', encoding='utf-8') as f:
+            with open('manny_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.")
@@ -273,31 +106,33 @@ def main():
         - You can also paste job descriptions to see how my profile matches!
         """)
         st.session_state.displayed_welcome = True
-    
-    # Create two columns
+
+    # Create two columns with proper sizing
     col1, col2 = st.columns([3, 1])
     
     with col1:
-        # Display chat messages
-        for message in st.session_state.messages:
-            with st.chat_message(message["role"]):
-                st.markdown(message["content"])
+        # Chat container for better scrolling
+        chat_container = st.container()
+        with chat_container:
+            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 prompt := st.chat_input("Ask me anything or paste a job description...", key="chat_input"):
             # Add user message
+            with st.chat_message("user"):
+                st.markdown(prompt)
             st.session_state.messages.append({"role": "user", "content": prompt})
             
-            try:
-                # Generate and display response
-                with st.chat_message("assistant"):
+            # Generate and display response
+            with st.chat_message("assistant"):
+                try:
                     response = generate_response(prompt, st.session_state.knowledge_base)
                     st.markdown(response)
                     st.session_state.messages.append({"role": "assistant", "content": response})
-            except Exception as e:
-                st.error(f"An error occurred: {str(e)}")
-            
-            st.rerun()
+                except Exception as e:
+                    st.error(f"An error occurred: {str(e)}")
     
     with col2:
         st.subheader("Quick Questions")
@@ -306,16 +141,27 @@ def main():
             "What are your technical skills?",
             "What makes you stand out?",
             "What's your journey into ML?",
-            "Paste a job description to see how I match!"
+            "Your view on the current market?"
         ]
         
-        for question in example_questions:
-            if st.button(question):
-                st.session_state.messages.append({"role": "user", "content": question})
+        # Handle quick questions with proper keys
+        for i, question in enumerate(example_questions):
+            if st.button(question, key=f"btn_{i}"):
+                with st.chat_message("user"):
+                    st.markdown(question)
+                    st.session_state.messages.append({"role": "user", "content": question})
+                
+                with st.chat_message("assistant"):
+                    try:
+                        response = generate_response(question, st.session_state.knowledge_base)
+                        st.markdown(response)
+                        st.session_state.messages.append({"role": "assistant", "content": response})
+                    except Exception as e:
+                        st.error(f"An error occurred: {str(e)}")
                 st.rerun()
         
         st.markdown("---")
-        if st.button("Clear Chat"):
+        if st.button("Clear Chat", key="clear_chat"):
             st.session_state.messages = []
             st.rerun()