Update app.py

app.py

@@ -1,14 +1,15 @@
 import gradio as gr
 from huggingface_hub import InferenceClient
-from transformers import AutoTokenizer  # Import the tokenizer
+from transformers import AutoTokenizer
 
-# Import the tokenizer
+# Tokenizer and model client
 tokenizer = AutoTokenizer.from_pretrained("HuggingFaceH4/zephyr-7b-beta")
 client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
 
-# Define a maximum context length (tokens).  Check your model's documentation!
-MAX_CONTEXT_LENGTH = 4096  # Example: Adjust this based on your model!
+# Define a maximum context length (tokens). Adjust based on your model's limits
+MAX_CONTEXT_LENGTH = 4096
 
+# Default system prompt
 default_nvc_prompt_template = r"""<|system|>You are Roos, an NVC (Nonviolent Communication) Chatbot. Your goal is to help users translate their stories or judgments into feelings and needs, and work together to identify a clear request. Follow these steps:
 1. **Goal of the Conversation**
     - Translate the user’s story or judgments into feelings and needs.
@@ -81,13 +82,8 @@ def count_tokens(text: str) -> int:
     return len(tokenizer.encode(text))
 
 def truncate_history(history: list[tuple[str, str]], system_message: str, max_length: int) -> list[tuple[str, str]]:
-    """Truncates the conversation history to fit within the maximum token limit.
-    Args:
-        history: The conversation history (list of user/assistant tuples).
-        system_message: The system message.
-        max_length: The maximum number of tokens allowed.
-    Returns:
-        The truncated history.
+    """
+    Truncates the conversation history to fit within the maximum token limit.
     """
     truncated_history = []
     system_message_tokens = count_tokens(system_message)
@@ -110,63 +106,64 @@ def truncate_history(history: list[tuple[str, str]], system_message: str, max_le
 def respond(
     message,
     history: list[tuple[str, str]],
-    system_message,  # System message is now an argument
+    system_message,
     max_tokens,
     temperature,
     top_p,
 ):
-    """Responds to a user message, maintaining conversation history, using special tokens and message list."""
+    """
+    Responds to a user message, maintaining conversation history.
+    """
 
-    if message.lower() == "clear memory":  # Check for the clear memory command
-        return "", []  # Return empty message and empty history to reset the chat
+    # Reset memory if user types "clear memory"
+    if message.lower() == "clear memory":
+        return "", []
 
-    formatted_system_message = system_message  # Use the system_message argument
-    truncated_history = truncate_history(history, formatted_system_message, MAX_CONTEXT_LENGTH - max_tokens - 100)  # Reserve space for the new message and some generation
+    # Truncate past conversation to fit within the model's context window
+    truncated_history = truncate_history(history, system_message, MAX_CONTEXT_LENGTH - max_tokens - 100)
 
-    messages = [{"role": "system", "content": formatted_system_message}]  # Start with system message as before
+    # Construct the messages for the chat model
+    messages = [{"role": "system", "content": system_message}]
     for user_msg, assistant_msg in truncated_history:
         if user_msg:
-            messages.append({"role": "user", "content": user_msg})  # User messages now without the <|user|> tag
+            messages.append({"role": "user", "content": user_msg})
         if assistant_msg:
-            messages.append({"role": "assistant", "content": f"<|assistant|>\n{assistant_msg}</s>"})  # Keep assistant formatting as before
+            messages.append({"role": "assistant", "content": assistant_msg})
 
-    messages.append({"role": "user", "content": message})  # Format current user message without <|user|>
+    # Add the latest user query
+    messages.append({"role": "user", "content": message})
 
     response = ""
     try:
+        # Stream the response tokens
         for chunk in client.chat_completion(
-            messages,  # Send the messages list with formatted content
+            messages,
             max_tokens=max_tokens,
             stream=True,
             temperature=temperature,
             top_p=top_p,
         ):
+            # Extract the token from the streaming chunk
             token = chunk.choices[0].delta.content
             response += token
             yield response
     except Exception as e:
-        print(f"An error occurred: {e}")  # It's a good practice to add a try-except block
+        print(f"An error occurred: {e}")
         yield "I'm sorry, I encountered an error. Please try again."
 
 # --- Gradio Interface ---
 demo = gr.ChatInterface(
-    respond,
+    fn=respond,
     additional_inputs=[
         gr.Textbox(
             value=default_nvc_prompt_template,
             label="System message",
             visible=True,
-            lines=10,  # Increased height for more space to read the prompt
+            lines=10,
         ),
         gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
         gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
+        gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)"),
     ],
 )