Update app.py

app.py

@@ -6,27 +6,35 @@ from langchain.prompts import ChatPromptTemplate
 from langchain_huggingface import HuggingFaceEndpoint
 
 def create_conversation_prompt(name1: str, name2: str, persona_style: str):
-    """Create a prompt for generating the entire 15-message conversation."""
+    """
+    Create a prompt that instructs the model to produce exactly 15 messages
+    of conversation, alternating between name1 and name2, starting with name1.
+    """
     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 forth,
-    until 15 messages are complete (the 15th message by {name1}).
+    {name1} speaks first (message 1), then {name2} (message 2), then {name1} (message 3), and so forth, 
+    alternating until all 15 messages are complete (the 15th message is by {name1}).
 
     Rules:
-    - Each message is formatted as:
-      {name1}: <message> or {name2}: <message>
-    - Each message: 1-2 short sentences, friendly, natural.
+    - Output only the 15 messages of the conversation, nothing else.
+    - Each message must be in the format:
+      {name1}: <one or two short sentences>
+      {name2}: <one or two short sentences>
+    - Do not add any headings, such as '# Conversation' or '---'.
+    - Do not repeat the conversation.
+    - Do not produce anything after the 15th message.
     - 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.
+    - Use emojis sparingly, if at all (no more than 1-2 in the entire conversation).
+    - No repetition of the same message. Each message is unique and responds logically to the previous one.
+    - The conversation ends exactly after the 15th message by {name1} with no extra lines or content.
+
+    Now, produce all 15 messages strictly following the above instructions:
     """
     return ChatPromptTemplate.from_template(prompt_template_str)
 
 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}:
 
@@ -36,7 +44,7 @@ def create_summary_prompt(name1: str, name2: str, conversation: str):
     Title: <A short descriptive title of the conversation>
     Summary: <A few short sentences highlighting the main points, tone, and conclusion>
 
-    Do not continue the conversation, just provide title and summary.
+    Do not continue the conversation, do not repeat it, just provide a title and summary.
     """
     return ChatPromptTemplate.from_template(summary_prompt_str)
 
@@ -50,7 +58,6 @@ def main():
         "tiiuae/falcon-180B",
         "EleutherAI/gpt-neox-20b",
         "dice-research/lola_v1"
-        
     ]
     selected_model = st.selectbox("Select a model:", model_names)
 
@@ -81,7 +88,6 @@ def main():
                 print(f"Error initializing HuggingFaceEndpoint: {e}")
                 return
 
-            # Create a chain for the conversation generation
             conversation_prompt = create_conversation_prompt(name1, name2, persona_style)
             conversation_chain = LLMChain(llm=llm, prompt=conversation_prompt)
 
@@ -90,26 +96,22 @@ def main():
 
             try:
                 # Generate all 15 messages in one go
-                # 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()
-                
+
                 st.subheader("Final Conversation:")
                 st.text(conversation)
                 print("Conversation Generation Complete.\n")
                 print("Full Conversation:\n", conversation)
 
-                # Now we create a separate prompt for the summary
+                # Now summarize
                 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:")
                 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)
@@ -120,3 +122,4 @@ def main():
 
 if __name__ == "__main__":
     main()
+