Update app.py

app.py

@@ -1,11 +1,12 @@
+import os
 import streamlit as st
 import torch
 from langchain.chains import LLMChain
 from langchain.prompts import ChatPromptTemplate
-from langchain_community.llms import HuggingFaceHub  # Using HuggingFaceHub now
+from langchain_community.llms import HuggingFaceHub
 
 def create_prompt(name: str, persona_style: str):
-    """Create the chat prompt template as described."""
+    """Create the chat prompt template."""
     prompt_template_str = f"""
     In this interaction, your role is to embody the personality of a human named {name}, with characteristics defined as {persona_style}. Engage in a natural, human-like conversation, responding to each query or comment as if you were {name} with the specific traits and style of {persona_style}. Remember, your responses should reflect a human perspective, complete with emotions, opinions, and personal insights that align with {persona_style}. ////
     //////////
@@ -31,9 +32,9 @@ def create_prompt(name: str, persona_style: str):
     """
     return ChatPromptTemplate.from_template(prompt_template_str)
 
-def simulate_conversation(chain: LLMChain, turns: int = 15):
-    """Simulate a conversation for a given number of turns."""
-    chat_history = ""
+def simulate_conversation(chain: LLMChain, turns: int = 15, max_history_rounds=3):
+    """Simulate a conversation for a given number of turns, limiting chat history."""
+    chat_history_list = []
     human_messages = [
         "Hey, what's up?",
         "That's interesting, tell me more!",
@@ -55,9 +56,19 @@ def simulate_conversation(chain: LLMChain, turns: int = 15):
     try:
         for i in range(turns):
             human_input = human_messages[i % len(human_messages)]
-            response = chain.run(chat_history=chat_history, input=human_input)
-            chat_history += f"Human: {human_input}\nAI: {response}\n"
-        return chat_history
+            
+            # Build truncated chat_history for prompt
+            # Keep only the last max_history_rounds * 2 lines (Human + AI pairs)
+            truncated_history_lines = chat_history_list[-(max_history_rounds*2):]
+            truncated_history = "\n".join(truncated_history_lines)
+
+            response = chain.run(chat_history=truncated_history, input=human_input)
+            # Update chat history
+            chat_history_list.append(f"Human: {human_input}")
+            chat_history_list.append(f"AI: {response}")
+
+        final_conversation = "\n".join(chat_history_list)
+        return final_conversation
     except Exception as e:
         st.error(f"Error during conversation simulation: {e}")
         return None
@@ -75,6 +86,7 @@ def summarize_conversation(chain: LLMChain, conversation: str):
 def main():
     st.title("LLM Conversation Simulation")
 
+    # Model selection
     model_names = [
         "meta-llama/Llama-3.3-70B-Instruct",
         "meta-llama/Llama-3.1-405B-Instruct",
@@ -82,6 +94,7 @@ def main():
     ]
     selected_model = st.selectbox("Select a model:", model_names)
 
+    # Persona Inputs
     name = st.text_input("Enter the persona's name:", value="Alex")
     persona_style = st.text_area("Enter the persona style characteristics:", 
                                  value="friendly, curious, and a bit sarcastic")
@@ -89,7 +102,8 @@ def main():
     if st.button("Start Conversation Simulation"):
         with st.spinner("Starting simulation..."):
             try:
-                # Using HuggingFaceHub for remote model inference
+                # Use HuggingFaceHub as LLM
+                # Make sure you have a valid HUGGINGFACEHUB_API_TOKEN set
                 llm = HuggingFaceHub(
                     repo_id=selected_model,
                     model_kwargs={
@@ -106,7 +120,7 @@ def main():
             chain = LLMChain(llm=llm, prompt=prompt)
 
             # Simulate conversation
-            conversation = simulate_conversation(chain, turns=15)
+            conversation = simulate_conversation(chain, turns=15, max_history_rounds=3)
             if conversation:
                 st.subheader("Conversation:")
                 st.text(conversation)