Update app.py

app.py

@@ -3,8 +3,6 @@ import streamlit as st
 import torch
 from langchain.chains import LLMChain
 from langchain.prompts import ChatPromptTemplate
-
-# Use the new package for HuggingFaceEndpoint
 from langchain_huggingface import HuggingFaceEndpoint
 
 def create_prompt(name: str, persona_style: str):
@@ -55,6 +53,9 @@ def simulate_conversation(chain: LLMChain, turns: int = 15, max_history_rounds=3
         "So what’s your conclusion?"
     ]
 
+    st.write("**Starting conversation simulation...**")
+    print("Starting conversation simulation...")
+
     try:
         for i in range(turns):
             human_input = human_messages[i % len(human_messages)]
@@ -62,8 +63,15 @@ def simulate_conversation(chain: LLMChain, turns: int = 15, max_history_rounds=3
             # Build truncated chat_history for prompt
             truncated_history_lines = chat_history_list[-(max_history_rounds*2):]
             truncated_history = "\n".join(truncated_history_lines)
+            
+            st.write(f"**[Turn {i+1}/{turns}] Human:** {human_input}")
+            print(f"[Turn {i+1}/{turns}] Human: {human_input}")
 
             response = chain.run(chat_history=truncated_history, input=human_input)
+
+            st.write(f"**AI:** {response}")
+            print(f"AI: {response}")
+
             chat_history_list.append(f"Human: {human_input}")
             chat_history_list.append(f"AI: {response}")
 
@@ -71,16 +79,21 @@ def simulate_conversation(chain: LLMChain, turns: int = 15, max_history_rounds=3
         return final_conversation
     except Exception as e:
         st.error(f"Error during conversation simulation: {e}")
+        print(f"Error during conversation simulation: {e}")
         return None
 
 def summarize_conversation(chain: LLMChain, conversation: str):
     """Use the LLM to summarize the completed conversation."""
     summary_prompt = f"Summarize the following conversation in a few short sentences highlighting the main points, tone, and conclusion:\n\n{conversation}\nSummary:"
+    st.write("**Summarizing the conversation...**")
+    print("Summarizing the conversation...")
+
     try:
         response = chain.run(chat_history="", input=summary_prompt)
         return response.strip()
     except Exception as e:
         st.error(f"Error summarizing conversation: {e}")
+        print(f"Error summarizing conversation: {e}")
         return "No summary available due to error."
 
 def main():
@@ -98,13 +111,14 @@ def main():
                                  value="friendly, curious, and a bit sarcastic")
 
     if st.button("Start Conversation Simulation"):
+        st.write("**Loading model...**")
+        print("Loading model...")
+
         with st.spinner("Starting simulation..."):
             # Construct the endpoint URL for the selected model
             endpoint_url = f"https://api-inference.huggingface.co/models/{selected_model}"
 
             try:
-                # Use HuggingFaceEndpoint instead of HuggingFaceHub
-                # Specify temperature and max_new_tokens as top-level arguments
                 llm = HuggingFaceEndpoint(
                     endpoint_url=endpoint_url,
                     huggingfacehub_api_token=os.environ.get("HUGGINGFACEHUB_API_TOKEN"),
@@ -112,21 +126,31 @@ def main():
                     temperature=0.7,
                     max_new_tokens=512
                 )
+                st.write("**Model loaded successfully!**")
+                print("Model loaded successfully!")
             except Exception as e:
                 st.error(f"Error initializing HuggingFaceEndpoint: {e}")
+                print(f"Error initializing HuggingFaceEndpoint: {e}")
                 return
 
             prompt = create_prompt(name, persona_style)
             chain = LLMChain(llm=llm, prompt=prompt)
 
+            st.write("**Simulating the conversation...**")
+            print("Simulating the conversation...")
+
             conversation = simulate_conversation(chain, turns=15, max_history_rounds=3)
             if conversation:
                 st.subheader("Conversation:")
                 st.text(conversation)
+                print("Conversation Simulation Complete.\n")
+                print("Full Conversation:\n", conversation)
 
+                # Summarize conversation
                 st.subheader("Summary:")
                 summary = summarize_conversation(chain, conversation)
                 st.write(summary)
+                print("Summary:\n", summary)
 
 if __name__ == "__main__":
     main()