Update app.py

app.py

@@ -6,27 +6,36 @@ from langchain.prompts import ChatPromptTemplate
 from langchain_huggingface import HuggingFaceEndpoint
 
 def create_prompt(name1: str, name2: str, persona_style: str):
+    """Create the chat prompt template for a two-person conversation."""
     prompt_template_str = f"""
-    You are simulating a conversation between two people: {name1} and {name2}. Both are human individuals. The conversation should embody the style and characteristics defined as {persona_style}. They are talking to each other naturally, taking turns speaking.
+    You are simulating a conversation between two people: {name1} and {name2}. 
+    Both are human individuals. The conversation should embody the style and characteristics defined as {persona_style}.
+    They are talking to each other naturally. There are no 'Human' or 'AI' roles here, just {name1} and {name2} speaking alternately. 
+    {name1} starts the conversation. Each message should be in the format:
+    {name1}: <message>
+    {name2}: <message>
 
     Characteristics and style:
     - Both {name1} and {name2} communicate in a friendly, human-like manner.
     - They can be curious, ask questions, share opinions.
-    - Their responses should be brief and natural, like texting a friend.
+    - Responses should be brief and natural, like texting a friend.
     - Use everyday language, show feelings and opinions.
     - Keep each response to about 1-2 short sentences.
-    - Use emojis sparingly and naturally if it fits the persona_style.
+    - Use emojis sparingly and only if it fits the persona_style.
     - Avoid overusing emojis (1-2 max per message).
 
-    The conversation should last exactly 15 messages total, starting with {name1} and then alternating. After the 15th message, stop.
-
-    Current partial conversation (if any):
-    {{chat_history}}
-    Next message:
+    Make sure that each turn is clearly designated as {name1} or {name2}. 
+    The conversation should continue for a total of 15 messages. Start with {name1} speaking first. Alternate between {name1} and {name2}.
+    Once the 15th message (by {name1}) is given, the conversation ends.
+    Do not continue the conversation after 15 messages.
     """
     return ChatPromptTemplate.from_template(prompt_template_str)
 
 def simulate_conversation(chain: LLMChain, name1: str, name2: str, total_messages: int = 15):
+    """
+    Simulate a conversation of exactly total_messages turns.
+    name1 starts the conversation (message 1), then name2 (message 2), etc., alternating.
+    """
     conversation_lines = []
     st.write("**Starting conversation simulation...**")
     print("Starting conversation simulation...")
@@ -34,10 +43,12 @@ def simulate_conversation(chain: LLMChain, name1: str, name2: str, total_message
     try:
         for i in range(total_messages):
             truncated_history = "\n".join(conversation_lines)
+            # Determine whose turn it is:
             current_speaker = name1 if i % 2 == 0 else name2
             st.write(f"**[Message {i+1}/{total_messages}] {current_speaker} is speaking...**")
             print(f"[Message {i+1}/{total_messages}] {current_speaker} is speaking...")
 
+            # Prompt the chain with a generic "continue" instruction, the chain uses the template + history to produce the next message
             response = chain.run(chat_history=truncated_history, input="Continue the conversation.")
             response = response.strip()
 
@@ -49,8 +60,10 @@ def simulate_conversation(chain: LLMChain, name1: str, name2: str, total_message
                 if line.startswith(f"{current_speaker}:"):
                     chosen_line = line
                     break
+
             if not chosen_line:
-                chosen_line = lines[0] if lines else f"{current_speaker}: (No response)"
+                # Fallback: If the model didn't format properly, try first non-empty line
+                chosen_line = next((l for l in lines if l.strip()), f"{current_speaker}: (No response)")
 
             st.write(chosen_line)
             print(chosen_line)
@@ -69,20 +82,20 @@ def summarize_conversation(chain: LLMChain, conversation: str, name1: str, name2
     st.write("**Summarizing the conversation...**")
     print("Summarizing the conversation...")
 
-    # Updated prompt to clearly request a title and summary in the desired format
+    # Updated prompt: no continuation instructions, just a direct request.
     summary_prompt = f"""
-    The following is a conversation between {name1} and {name2}:
+    Below is a completed conversation between {name1} and {name2}:
     {conversation}
 
-    Please provide:
-    - A short and descriptive title of the conversation on one line, starting with "Title:".
-    - A summary of the conversation on the next line, starting with "Summary:".
+    Now that the conversation has ended, please provide:
+    1. A short and descriptive title of the conversation on one line, starting with "Title:".
+    2. A summary of the conversation on the next line, starting with "Summary:".
     The summary should be a few short sentences highlighting the main points, tone, and conclusion.
+    Do not continue the conversation. Do not add extra lines beyond the title and summary.
     """
 
     try:
         response = chain.run(chat_history="", input=summary_prompt)
-        # Just return the response as-is. The model should follow the instructions given.
         return response.strip()
     except Exception as e:
         st.error(f"Error summarizing conversation: {e}")
@@ -118,7 +131,7 @@ def main():
                     huggingfacehub_api_token=os.environ.get("HUGGINGFACEHUB_API_TOKEN"),
                     task="text-generation",
                     temperature=0.7,
-                    max_new_tokens=256  # Reduced for speed
+                    max_new_tokens=256 # Reduced tokens for faster response
                 )
                 st.write("**Model loaded successfully!**")
                 print("Model loaded successfully!")