Update app.py

app.py

@@ -1,22 +1,9 @@
 import gradio as gr
 from huggingface_hub import InferenceClient
-import logging
 
 # Initialize the InferenceClient with the model ID from Hugging Face
 client = InferenceClient(model="HuggingFaceH4/zephyr-7b-beta")
 
-# Set up logging
-logging.basicConfig(
-    filename='chatbot_log.log',
-    level=logging.INFO,
-    format='%(asctime)s - %(levelname)s - %(message)s',
-)
-
-def log_conversation(user_message, bot_response):
-    """Logs the conversation between the user and the AI."""
-    logging.info(f"User: {user_message}")
-    logging.info(f"Bot: {bot_response}")
-
 def respond(
     message: str,
     history: list[tuple[str, str]],
@@ -24,11 +11,22 @@ def respond(
     max_tokens: int,
     temperature: float,
     top_p: float,
-    stop_sequence: str,
-    stream_response: bool,
 ):
-    """Generates a response from the AI model based on the user's message and chat history."""
+    """
+    Generates a response from the AI model based on the user's message and chat history.
+    
+    Args:
+        message (str): The user's input message.
+        history (list): A list of tuples representing the conversation history (user, assistant).
+        system_message (str): A system-level message guiding the AI's behavior.
+        max_tokens (int): The maximum number of tokens for the output.
+        temperature (float): Sampling temperature for controlling the randomness.
+        top_p (float): Top-p (nucleus sampling) for controlling diversity.
 
+    Yields:
+        str: The AI's response as it is generated.
+    """
+    
     # Prepare the conversation history for the API call
     messages = [{"role": "system", "content": system_message}]
     
@@ -45,56 +43,41 @@ def respond(
     response = ""
 
     try:
-        if stream_response:
-            # Generate a response from the model with streaming
-            for message in client.chat_completion(
-                messages=messages,
-                max_tokens=max_tokens,
-                stream=True,
-                temperature=temperature,
-                top_p=top_p,
-                stop=stop_sequence,
-            ):
-                token = message.choices[0].delta.get("content", "")
-                response += token
-                yield response
-        else:
-            # Generate a complete response without streaming
-            result = client.chat_completion(
-                messages=messages,
-                max_tokens=max_tokens,
-                stream=False,
-                temperature=temperature,
-                top_p=top_p,
-                stop=stop_sequence,
-            )
-            response = result.choices[0].message.get("content", "")
-            log_conversation(message, response)
+        # Generate a response from the model with streaming
+        for message in client.chat_completion(
+            messages=messages,
+            max_tokens=max_tokens,
+            stream=True,
+            temperature=temperature,
+            top_p=top_p,
+        ):
+            token = message.choices[0].delta.content
+            response += token
             yield response
 
     except Exception as e:
-        error_message = f"An error occurred: {str(e})"
-        logging.error(error_message)
-        yield error_message
+        yield f"An error occurred: {str(e)}"
+
 
 # Define the ChatInterface with additional input components for user customization
 demo = gr.ChatInterface(
     fn=respond,
     additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System Message", lines=2),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max New Tokens"),
-        gr.Slider(minimum=0.1, maximum=2.0, 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.Textbox(value="", label="Stop Sequence (optional)", lines=1),
-        gr.Checkbox(label="Stream Response", value=True),
+        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
+        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)",
+        ),
     ],
-    title="AI Chatbot Interface",
-    description="Interact with an AI chatbot powered by Hugging Face's Zephyr-7B model. Customize the chatbot's behavior and response generation settings.",
-    theme="default",
+    title="Chatbot Interface",
+    description="A customizable chatbot interface using Hugging Face's Inference API.",
 )
 
 # Launch the Gradio interface
 if __name__ == "__main__":
-    logging.info("Launching the Gradio interface...")
     demo.launch()
-    logging.info("Gradio interface launched successfully.")