Update app.py

app.py

@@ -5,46 +5,40 @@ from langchain.chains import LLMChain
 from langchain.prompts import ChatPromptTemplate
 from langchain_huggingface import HuggingFaceEndpoint
 
-def create_prompt(name1: str, name2: str, persona_style: str):
-    """Create a prompt that instructs the model to produce all 15 messages at once."""
+def create_conversation_prompt(name1: str, name2: str, persona_style: str):
+    """Create a prompt for generating the entire 15-message conversation."""
     prompt_template_str = f"""
     You are to simulate a conversation of exactly 15 messages total between two people: {name1} and {name2}.
     The conversation should reflect the style: {persona_style}.
-    {name1} speaks first (message 1), {name2} responds (message 2), then {name1} (message 3), and so on, alternating until 15 messages are complete.
+    {name1} speaks first (message 1), {name2} responds (message 2), then {name1} (message 3), and so forth,
+    until 15 messages are complete (the 15th message by {name1}).
+
     Rules:
-    - Each message should be written as:
+    - Each message is formatted as:
       {name1}: <message> or {name2}: <message>
-    - Each message should be 1-2 short sentences, friendly, and natural.
-    - Keep it casual, can ask questions, share opinions.
-    - Use emojis sparingly if it fits the persona (no more than 1-2 per message).
-    - Do not repeat the same line over and over.
-    - The conversation must flow logically and naturally.
-    - After producing exactly 15 messages (the 15th message by {name1}), stop. Do not add anything else.
-    - Do not continue the conversation beyond 15 messages.
-    
-    Produce all 15 messages now:
+    - Each message: 1-2 short sentences, friendly, natural.
+    - Use everyday language, can ask questions, show opinions.
+    - Use emojis sparingly if it fits the style.
+    - Do not repeat the same line.
+    - Produce all 15 messages now and do not continue beyond the 15th message.
     """
     return ChatPromptTemplate.from_template(prompt_template_str)
 
-def summarize_conversation(chain: LLMChain, conversation: str, name1: str, name2: str):
-    """Summarize the completed conversation."""
-    st.write("**Summarizing the conversation...**")
-    print("Summarizing the conversation...")
+def create_summary_prompt(name1: str, name2: str, conversation: str):
+    """Create a prompt specifically for generating a title and summary of the conversation."""
+    # Here we explicitly create a new prompt template for the summary.
+    summary_prompt_str = f"""
+    The following is a completed conversation between {name1} and {name2}:
 
-    summary_prompt = f"""
-    Below is a completed conversation between {name1} and {name2}:
     {conversation}
 
-    Use the conversation above and write a short Title and a summary of above conversation. The summary should be in paragraph which highlights what was the conversation about.
-    """
+    Please provide:
+    Title: <A short descriptive title of the conversation>
+    Summary: <A few short sentences highlighting the main points, tone, and conclusion>
 
-    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 "Title: No Title\nSummary: No summary available due to error."
+    Do not continue the conversation, just provide title and summary.
+    """
+    return ChatPromptTemplate.from_template(summary_prompt_str)
 
 def main():
     st.title("LLM Conversation Simulation")
@@ -83,26 +77,36 @@ def main():
                 print(f"Error initializing HuggingFaceEndpoint: {e}")
                 return
 
-            prompt = create_prompt(name1, name2, persona_style)
-            chain = LLMChain(llm=llm, prompt=prompt)
+            # Create a chain for the conversation generation
+            conversation_prompt = create_conversation_prompt(name1, name2, persona_style)
+            conversation_chain = LLMChain(llm=llm, prompt=conversation_prompt)
 
             st.write("**Generating the full 15-message conversation...**")
             print("Generating the full 15-message conversation...")
 
             try:
                 # Generate all 15 messages in one go
-                conversation = chain.run(chat_history="", input="Produce the full conversation now.")
+                # Here we send the prompt for the conversation to the LLM
+                conversation = conversation_chain.run(chat_history="", input="Produce the full conversation now.")
                 conversation = conversation.strip()
                 
-                # Print and display the conversation
                 st.subheader("Final Conversation:")
                 st.text(conversation)
                 print("Conversation Generation Complete.\n")
                 print("Full Conversation:\n", conversation)
 
-                # Summarize the conversation
+                # Now we create a separate prompt for the summary
+                summary_prompt = create_summary_prompt(name1, name2, conversation)
+                # Create a new chain for the summary using the summary prompt
+                summary_chain = LLMChain(llm=llm, prompt=summary_prompt)
+
                 st.subheader("Summary and Title:")
-                summary = summarize_conversation(chain, conversation, name1, name2)
+                st.write("**Summarizing the conversation...**")
+                print("Summarizing the conversation...")
+
+                # Here we explicitly call the summary chain with the summary prompt
+                # This ensures we are actually sending the summary prompt to the LLM
+                summary = summary_chain.run(chat_history="", input="")
                 st.write(summary)
                 print("Summary:\n", summary)