Update app.py

Let test changes made on top of a working version

app.py

@@ -20,7 +20,7 @@ def format_project_response(project: dict, indent_level: int = 0) -> str:
             if 'confidentiality_note' in project:
                 response.append(f"{indent}  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"""
@@ -98,14 +98,14 @@ def analyze_job_description(text: str, knowledge_base: dict) -> str:
         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
+    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("")  # Add spacing
+    response_parts.append("")
     
     # Project Experience
     response_parts.append("• Relevant Project Experience:")
@@ -118,7 +118,7 @@ def analyze_job_description(text: str, knowledge_base: dict) -> str:
         relevant_projects.append("  - Created multiple ML models from scratch, including predictive analytics for Ola")
     
     response_parts.extend(relevant_projects)
-    response_parts.append("")  # Add spacing
+    response_parts.append("")
     
     # Education and Additional Qualifications
     response_parts.append("• Additional Strengths:")
@@ -137,20 +137,20 @@ def format_story_response(knowledge_base: dict) -> str:
     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
+    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]}...")  # Truncate for readability
-    response_parts.append("")  # Add spacing
+    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("")  # Add spacing
+    response_parts.append("")
     
     # Goals
     response_parts.append("• Future Goals:")
@@ -160,12 +160,35 @@ def format_story_response(knowledge_base: dict) -> str:
     
     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 = []
     
-    # 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']}")
     
@@ -182,8 +205,35 @@ def add_relevant_links(response: str, query: str, knowledge_base: dict) -> str:
     
     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_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 generate_response(query: str, knowledge_base: dict) -> str:
-    """Generate enhanced responses using the knowledge base"""
+    """Enhanced response generation with better query handling"""
     query_lower = query.lower()
     
     # Handle project listing requests
@@ -218,27 +268,15 @@ def generate_response(query: str, knowledge_base: dict) -> str:
     
     # 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("")
-        
-        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 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"
@@ -274,8 +312,8 @@ def main():
         """)
         st.session_state.displayed_welcome = True
     
-    # Create two columns
-    col1, col2 = st.columns([3, 1])
+    # Create two columns with adjusted ratios
+    col1, col2 = st.columns([4, 1])
     
     with col1:
         # Display chat messages
@@ -300,7 +338,7 @@ def main():
             st.rerun()
     
     with col2:
-        st.subheader("Quick Questions")
+        st.markdown("### Quick Questions")
         example_questions = [
             "Tell me about your ML projects",
             "What are your technical skills?",
@@ -310,12 +348,17 @@ def main():
         ]
         
         for question in example_questions:
-            if st.button(question):
+            if st.button(question, key=f"btn_{question}", use_container_width=True):
                 st.session_state.messages.append({"role": "user", "content": question})
+                try:
+                    response = generate_response(question, st.session_state.knowledge_base)
+                    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", use_container_width=True):
             st.session_state.messages = []
             st.rerun()