Update app.py

app.py

@@ -1,7 +1,6 @@
 import streamlit as st
 import json
 import openai
-import time
 
 # Initialize Streamlit page configuration
 st.set_page_config(
@@ -12,10 +11,13 @@ st.set_page_config(
 
 # Secret management
 def get_openai_api_token():
-    # In production, use Streamlit secrets
-    return st.secrets["OPENAI_API_KEY"]
+    try:
+        return st.secrets["OPENAI_API_KEY"]
+    except KeyError:
+        st.error("OpenAI API key not found in secrets.")
+        return None
 
-# Initialize OpenAI client
+# Load and cache OpenAI client
 @st.cache_resource
 def get_openai_client():
     openai.api_key = get_openai_api_token()
@@ -27,21 +29,21 @@ def load_knowledge_base():
     try:
         with open('knowledge_base.json', 'r', encoding='utf-8') as f:
             return json.load(f)
-    except Exception as e:
-        st.error(f"Error loading knowledge base: {str(e)}")
+    except FileNotFoundError:
+        st.error("Knowledge base file not found.")
         return {}
 
 def get_context(query: str, knowledge_base: dict) -> str:
     """Get relevant context for the query"""
     query_lower = query.lower()
     contexts = []
-    
+
     # Project-related queries
     if any(word in query_lower for word in ["project", "build", "develop", "create"]):
         if "projects" in knowledge_base:
             for name, details in knowledge_base["projects"].items():
                 contexts.append(f"Project - {name}: {details.get('description', '')}")
-    
+
     # Skills and expertise
     elif any(word in query_lower for word in ["skill", "know", "experience", "expert"]):
         if "skills" in knowledge_base.get("personal_details", {}):
@@ -49,26 +51,26 @@ def get_context(query: str, knowledge_base: dict) -> str:
                 f"Skill - {skill}: {desc}"
                 for skill, desc in knowledge_base["personal_details"]["skills"].items()
             ])
-    
+
     # Role fit and career
     elif any(word in query_lower for word in ["role", "fit", "job", "position", "career"]):
         contexts.append(knowledge_base.get("professional_journey", {}).get("mindset", ""))
         contexts.extend(knowledge_base.get("goals_and_aspirations", {}).get("short_term", []))
-        
+
     # Background and journey
     elif any(word in query_lower for word in ["background", "journey", "story"]):
         faq = knowledge_base.get("frequently_asked_questions", [])
         for qa in faq:
             if "background" in qa["question"].lower():
                 contexts.append(qa["answer"])
-    
+
     # Default context
     if not contexts:
         contexts = [
             f"I am {knowledge_base.get('personal_details', {}).get('full_name', 'Manyue')}",
             knowledge_base.get('personal_details', {}).get('professional_summary', '')
         ]
-    
+
     return "\n".join(contexts)
 
 def generate_openai_prompt(query: str, context: str) -> str:
@@ -93,32 +95,23 @@ def get_chat_response(query: str, knowledge_base: dict) -> str:
     try:
         # Get context
         context = get_context(query, knowledge_base)
-        
+
         # Generate prompt
         prompt = generate_openai_prompt(query, context)
-        
-        # Generate response using the new API
+
+        # Generate response
         response = openai.ChatCompletion.create(
             model="gpt-3.5-turbo",
-            messages=[
-                {"role": "system", "content": "You are Manyue's AI assistant. Answer questions about Manyue."},
-                {"role": "user", "content": query},
-                {"role": "assistant", "content": prompt}
-            ],
+            messages=[{"role": "user", "content": prompt}],
             max_tokens=200,
             temperature=0.7
         )
-        
+
         # Extract and clean response
         response_text = response['choices'][0]['message']['content'].strip()
-        
+
         return response_text
-        
-    except Exception as e:
-        st.error(f"Error generating response: {str(e)}")
-        return "I apologize, but I encountered an error. Please try asking again."
 
-        
     except Exception as e:
         st.error(f"Error generating response: {str(e)}")
         return "I apologize, but I encountered an error. Please try asking again."
@@ -132,7 +125,6 @@ def initialize_session_state():
 
 def display_chat_interface():
     """Display main chat interface"""
-    # Display chat messages
     for message in st.session_state.messages:
         with st.chat_message(message["role"]):
             st.markdown(message["content"])
@@ -140,35 +132,35 @@ def display_chat_interface():
 def main():
     st.title("💬 Chat with Manyue's Portfolio")
     st.write("Ask me about my skills, projects, experience, or career goals!")
-    
+
     # Initialize session state
     initialize_session_state()
-    
+
     # Create two columns
     col1, col2 = st.columns([3, 1])
-    
+
     with col1:
         # Display chat interface
         display_chat_interface()
-        
+
         # Chat input
         if prompt := st.chat_input("What would you like to know?"):
             # Add user message
             st.session_state.messages.append({"role": "user", "content": prompt})
-            
+
             # Display user message
             with st.chat_message("user"):
                 st.markdown(prompt)
-            
+
             # Generate and display response
             with st.chat_message("assistant"):
                 with st.spinner("Thinking..."):
                     response = get_chat_response(prompt, st.session_state.knowledge_base)
                 st.markdown(response)
-            
+
             # Add assistant response to history
             st.session_state.messages.append({"role": "assistant", "content": response})
-    
+
     with col2:
         st.subheader("Quick Questions")
         example_questions = [
@@ -177,16 +169,16 @@ def main():
             "Why are you suitable for ML roles?",
             "What is your educational background?"
         ]
-        
-        for question in example_question:
+
+        for question in example_questions:
             if st.button(question):
                 st.session_state.messages.append({"role": "user", "content": question})
                 st.experimental_rerun()
-        
+
         st.markdown("---")
         if st.button("Clear Chat"):
             st.session_state.messages = []
             st.experimental_rerun()
 
 if __name__ == "__main__":
-    main()
+    main()