Update app.py

app.py

@@ -1,8 +1,5 @@
 import streamlit as st
 import json
-from transformers import AutoTokenizer, AutoModelForCausalLM
-import torch
-import time
 
 # Page configuration
 st.set_page_config(
@@ -54,23 +51,6 @@ def get_context(query: str, knowledge_base: dict) -> str:
     
     return "\n".join(contexts)
 
-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
-
 def main():
     st.title("Portfolio Chatbot Testing Interface")
     st.write("Test the chatbot's responses and interaction patterns")
@@ -96,8 +76,8 @@ def main():
             # 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..."
+            # For testing, just echo back the context
+            response = f"TEST RESPONSE: Here's what I know about this:\n\n{context}"
             
             # Display assistant response in chat message container
             with st.chat_message("assistant"):