Update app.py

app.py

@@ -1,134 +1,64 @@
 import gradio as gr
 from huggingface_hub import InferenceClient
-# Import the correct exception class
-from huggingface_hub.utils import HfHubHTTPError
-import os
 
 """
 For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-
-**Note:** You might need to authenticate with Hugging Face for this to work reliably.
-Run `huggingface-cli login` in your terminal or set the HUGGING_FACE_HUB_TOKEN environment variable.
-Alternatively, pass your token directly: InferenceClient(token="hf_YOUR_TOKEN")
 """
-# Initialize the Inference Client
-# It will try to use HUGGING_FACE_HUB_TOKEN environment variable or cached login
-try:
-    # You might need to provide a token if you haven't logged in via CLI
-    # token = os.getenv("HUGGING_FACE_HUB_TOKEN")
-    # client = InferenceClient("HuggingFaceH4/zephyr-7b-beta", token=token)
-    client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-except Exception as e:
-    print(f"Error initializing InferenceClient: {e}")
-    raise ValueError("Could not initialize InferenceClient. Ensure you are logged in or provide a token.") from e
+client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
 
 
 def respond(
-    message: str,
-    history: list[tuple[str | None, str | None]],
-    system_message: str,
-    max_tokens: int,
-    temperature: float,
-    top_p: float,
+    message,
+    history: list[tuple[str, str]],
+    system_message,
+    max_tokens,
+    temperature,
+    top_p,
 ):
-    """
-    Generates a response using the Hugging Face Inference API.
-
-    Args:
-        message: The user's input message.
-        history: A list of tuples representing the conversation history.
-                 Each tuple is (user_message, bot_message).
-        system_message: The system prompt to guide the model.
-        max_tokens: The maximum number of new tokens to generate.
-        temperature: Controls randomness (higher = more random).
-        top_p: Nucleus sampling parameter.
-
-    Yields:
-        The generated response incrementally.
-    """
     messages = [{"role": "system", "content": system_message}]
 
-    # Add conversation history
-    for user_msg, bot_msg in history:
+    for user_msg, assistant_msg in history:
         if user_msg:
             messages.append({"role": "user", "content": user_msg})
-        if bot_msg:
-            messages.append({"role": "assistant", "content": bot_msg})
+        if assistant_msg:
+            messages.append({"role": "assistant", "content": assistant_msg})
 
-    # Add the latest user message
     messages.append({"role": "user", "content": message})
 
     response = ""
-    try:
-        # Start streaming the response
-        for msg_chunk in client.chat_completion(
-            messages=messages,
-            max_tokens=max_tokens,
-            stream=True,
-            temperature=temperature,
-            top_p=top_p,
-        ):
-            # Check if there's content in the delta
-            token = msg_chunk.choices[0].delta.content
-            if token:  # Add check for empty/None token
-                response += token
-                yield response # Yield the accumulated response so far
-
-    # Catch HTTP errors from the Hugging Face Hub API
-    except HfHubHTTPError as e:
-        error_message = f"Inference API Error: {e}"
-        # Try to get more details from the response if available
-        if e.response:
-            try:
-                details = e.response.json()
-                error_message += f"\nDetails: {details.get('error', 'N/A')}"
-            except Exception: # Catch potential JSON decoding errors
-                pass # Keep the original error message
-        print(error_message)
-        yield f"Sorry, I encountered an error communicating with the model service: {e}" # Display a user-friendly message
 
-    # Catch other potential errors
-    except Exception as e:
-        print(f"An unexpected error occurred: {e}")
-        yield f"Sorry, an unexpected error occurred: {e}"
+    for event in client.chat_completion(
+        messages,
+        max_tokens=int(max_tokens),
+        stream=True,
+        temperature=temperature,
+        top_p=top_p,
+    ):
+        if event.token is not None:
+            response += event.token
+            yield response
 
 
 """
 For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
 """
-demo = gr.ChatInterface(
-    respond,
-    chatbot=gr.Chatbot(height=400), # Adjust chatbot height if desired
-    textbox=gr.Textbox(placeholder="Ask me anything...", container=False, scale=7),
-    title="Zephyr 7B Beta Chat",
-    description="Chat with the Zephyr 7B Beta model using the Hugging Face Inference API.",
-    theme="soft", # Optional: Apply a theme
-    examples=[
-        ["Hello!"],
-        ["Explain the concept of Large Language Models in simple terms."],
-        ["Write a short poem about the moon."],
-    ],
-    cache_examples=False, # Set to True to cache example results
-    retry_btn="Retry",
-    undo_btn="Undo",
-    clear_btn="Clear",
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly and helpful chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=1.0, value=0.7, step=0.1, label="Temperature"), # Note: Max temp often capped lower (e.g., 1.0 or 2.0)
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-     additional_inputs_accordion=gr.Accordion(label="Advanced Options", open=False), # Group additional inputs
-)
+with gr.Blocks() as demo:
+    chatbot = gr.ChatInterface(
+        respond,
+        additional_inputs=[
+            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)",
+            ),
+        ],
+    )
 
 
 if __name__ == "__main__":
-    # Ensure huggingface_hub library is up-to-date: pip install --upgrade huggingface_hub
-    print("Launching Gradio Interface...")
     demo.launch()