Update app.py

app.py

@@ -1,51 +1,131 @@
 import streamlit as st
 import json
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+import time
 
-# Page config
-st.set_page_config(page_title="Chatbot Test", layout="wide")
+# Page configuration
+st.set_page_config(
+    page_title="Portfolio Chatbot Test",
+    page_icon="🤖",
+    layout="wide"
+)
 
-# Initialize chat history
-if "messages" not in st.session_state:
+# Initialize session state
+if 'messages' not in st.session_state:
     st.session_state.messages = []
 
-# Load knowledge base
 def load_knowledge_base():
+    """Load the knowledge base from JSON file"""
     try:
-        with open("knowledge_base.json", "r", encoding="utf-8") as f:
+        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)}")
         return {}
 
-kb = load_knowledge_base()
-
-# Main UI
-st.title("Portfolio Chat Test")
+def get_context(query: str, knowledge_base: dict) -> str:
+    """Get relevant context from knowledge base based on query"""
+    query_lower = query.lower()
+    contexts = []
+    
+    # Project context
+    if "project" in query_lower:
+        if "projects" in knowledge_base:
+            contexts.extend([
+                f"{name}: {desc}"
+                for name, desc in knowledge_base["projects"].items()
+            ])
+            
+    # Skills context
+    elif any(keyword in query_lower for keyword in ["skill", "experience", "capability"]):
+        if "personal_details" in knowledge_base and "skills" in knowledge_base["personal_details"]:
+            contexts.extend([
+                f"{skill}: {desc}"
+                for skill, desc in knowledge_base["personal_details"]["skills"].items()
+            ])
+            
+    # Default context
+    else:
+        contexts = [
+            f"Name: {knowledge_base.get('personal_details', {}).get('full_name', 'Manyue')}",
+            "Summary: I am an aspiring AI/ML engineer with experience in Python, Machine Learning, and Data Analysis."
+        ]
+    
+    return "\n".join(contexts)
 
-# Chat interface
-for message in st.session_state.messages:
-    with st.chat_message(message["role"]):
-        st.write(message["content"])
+def initialize_model():
+    """Initialize the model and tokenizer"""
+    try:
+        # For testing, use a smaller model
+        model_name = "meta-llama/Llama-2-7b-chat-hf"  # You'll need to adjust this
+        
+        tokenizer = AutoTokenizer.from_pretrained(model_name)
+        model = AutoModelForCausalLM.from_pretrained(
+            model_name,
+            torch_dtype=torch.float16,
+            device_map="auto"
+        )
+        return tokenizer, model
+    except Exception as e:
+        st.error(f"Error initializing model: {str(e)}")
+        return None, None
 
-# Chat input
-if prompt := st.chat_input("Ask me anything..."):
-    # Add user message
-    st.session_state.messages.append({"role": "user", "content": prompt})
+def main():
+    st.title("Portfolio Chatbot Testing Interface")
+    st.write("Test the chatbot's responses and interaction patterns")
     
-    # Generate simple response from knowledge base
-    if "projects" in kb and "project" in prompt.lower():
-        response = "Here are my projects: " + ", ".join(kb["projects"].keys())
-    elif "skills" in prompt.lower():
-        response = "I have experience in Python, ML, and Data Analysis."
-    else:
-        response = "I understand you're asking about: " + prompt
+    # Load knowledge base
+    knowledge_base = load_knowledge_base()
+    
+    # Create two columns for layout
+    col1, col2 = st.columns([2, 1])
     
-    # Add assistant response
-    with st.chat_message("assistant"):
-        st.write(response)
-    st.session_state.messages.append({"role": "assistant", "content": response})
+    with col1:
+        st.subheader("Chat Interface")
+        # Display chat messages from history
+        for message in st.session_state.messages:
+            with st.chat_message(message["role"]):
+                st.markdown(message["content"])
+        
+        # Accept user input
+        if prompt := st.chat_input("What would you like to know?"):
+            # Add user message to chat history
+            st.session_state.messages.append({"role": "user", "content": prompt})
+            
+            # Get context for the query
+            context = get_context(prompt, knowledge_base)
+            
+            # For now, just echo back a response (replace with actual model response later)
+            response = f"Test Response: Let me tell you about that based on my experience..."
+            
+            # Display assistant response in chat message container
+            with st.chat_message("assistant"):
+                st.markdown(response)
+            
+            # Add assistant response to chat history
+            st.session_state.messages.append({"role": "assistant", "content": response})
+    
+    with col2:
+        st.subheader("Testing Tools")
+        if st.button("Clear Chat History"):
+            st.session_state.messages = []
+            st.experimental_rerun()
+        
+        st.subheader("Sample Questions")
+        if st.button("Tell me about your ML projects"):
+            st.session_state.messages.append({
+                "role": "user",
+                "content": "Tell me about your ML projects"
+            })
+            st.experimental_rerun()
+        
+        if st.button("What are your Python skills?"):
+            st.session_state.messages.append({
+                "role": "user",
+                "content": "What are your Python skills?"
+            })
+            st.experimental_rerun()
 
-# Clear chat button
-if st.sidebar.button("Clear Chat"):
-    st.session_state.messages = []
-    st.rerun()
+if __name__ == "__main__":
+    main()