Update app.py

app.py

@@ -6,30 +6,19 @@ 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."""
-    # We'll define that name1 (e.g., Alice) starts the conversation.
-    # The conversation is recorded as:
-    # Alice: {input or response}
-    # Bob: {response}
-    # and so on...
-
     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. 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>
+    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.
 
     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.
-    - They can use everyday language, show feelings and opinions.
+    - 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.
     - Avoid overusing emojis (1-2 max per 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 is given (by {name1}, since the conversation starts with {name1}), the conversation ends. After that, produce a summary and a title of the conversation separately.
+    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}}
@@ -38,33 +27,21 @@ def create_prompt(name1: str, name2: str, persona_style: str):
     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...")
 
     try:
         for i in range(total_messages):
-            # Build truncated conversation (if needed, though we may not need truncation with only 15 messages)
             truncated_history = "\n".join(conversation_lines)
-
-            # Determine whose turn it is:
-            # i=0 (first message), i even => name1 speaks, i odd => name2 speaks
             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...")
 
-            # We ask the model for the next line in the conversation
-            # The model should produce something like: "Alice: ...message..."
             response = chain.run(chat_history=truncated_history, input="Continue the conversation.")
             response = response.strip()
 
-            # We only keep the line that pertains to the current message
-            # If the model generates both speakers, we may need to parse carefully.
-            # Ideally, the model will produce only one line. If multiple lines appear, we'll take the first line that starts with current_speaker.
+            # Extract the line for the current speaker
             lines = response.split("\n")
             chosen_line = None
             for line in lines:
@@ -72,9 +49,7 @@ 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:
-                # Fallback: If not found, just use the first line
                 chosen_line = lines[0] if lines else f"{current_speaker}: (No response)"
 
             st.write(chosen_line)
@@ -90,7 +65,6 @@ def simulate_conversation(chain: LLMChain, name1: str, name2: str, total_message
         return None
 
 def summarize_conversation(chain: LLMChain, conversation: str, name1: str, name2: str):
-    """Use the LLM to summarize the completed conversation and provide a title."""
     st.write("**Summarizing the conversation...**")
     print("Summarizing the conversation...")
 
@@ -99,10 +73,12 @@ def summarize_conversation(chain: LLMChain, conversation: str, name1: str, name2
     {conversation}
 
     Provide a short descriptive title for their conversation and then summarize it in a few short sentences highlighting the main points, tone, and conclusion.
+    
     Format your answer as:
     Title: <your conversation title>
     Summary: <your summary here>
     """
+
     try:
         response = chain.run(chat_history="", input=summary_prompt)
         return response.strip()
@@ -140,7 +116,7 @@ def main():
                     huggingfacehub_api_token=os.environ.get("HUGGINGFACEHUB_API_TOKEN"),
                     task="text-generation",
                     temperature=0.7,
-                    max_new_tokens=512
+                    max_new_tokens=256  # Reduced for speed
                 )
                 st.write("**Model loaded successfully!**")
                 print("Model loaded successfully!")
@@ -155,7 +131,6 @@ def main():
             st.write("**Simulating the conversation...**")
             print("Simulating the conversation...")
 
-            # Total messages = 15
             conversation = simulate_conversation(chain, name1, name2, total_messages=15)
             if conversation:
                 st.subheader("Final Conversation:")