Update app.py

- add more fixes

app.py

@@ -1,99 +1,83 @@
 import gradio as gr
 from huggingface_hub import InferenceClient
 
-"""
-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
-"""
-
 def respond(message, history, token, model, system_message, max_tokens, temperature, top_p):
     """
     Handle chat responses using the Hugging Face Inference API.
-    
-    Parameters:
-    - message: The user's current message.
-    - history: List of previous user-assistant message pairs.
-    - token: HF API token for authentication.
-    - model: Model name to use for inference.
-    - system_message: System prompt to initialize the chat.
-    - max_tokens: Maximum number of tokens to generate.
-    - temperature: Sampling temperature.
-    - top_p: Top-p (nucleus) sampling parameter.
-    
-    Yields:
-    - Incremental responses for streaming in the chat interface.
     """
-    # Check for missing token
+    # Handle token and model defaults
+    token = token.strip()
+    model = model.strip()
+    
+    # Default model selection logic
     if not token:
-        yield "Please provide an HF API Token."
-        return
-
-    # Use default model if none provided
-    if not model:
-        model = "meta-llama/Llama-3.1-8B-Instruct"
-
-    # Initialize the InferenceClient
-    try:
-        client = InferenceClient(model=model, token=token)
-    except Exception as e:
-        yield f"Error initializing client: {str(e)}"
-        return
+        model = "gpt2"  # Default public model that doesn't require token
+        try:
+            client = InferenceClient(model=model)
+        except Exception as e:
+            yield f"Error initializing client: {str(e)}"
+            return
+    else:
+        model = model or "meta-llama/Llama-3.1-8B-Instruct"  # Default private model
+        try:
+            client = InferenceClient(model=model, token=token)
+        except Exception as e:
+            yield f"Error initializing client: {str(e)}"
+            return
 
-    # Build the message history, starting with the system message
+    # Build message history
     messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:  # User message
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:  # Assistant message
-            messages.append({"role": "assistant", "content": val[1]})
-
+    for user_msg, assistant_msg in history:
+        if user_msg:
+            messages.append({"role": "user", "content": user_msg})
+        if assistant_msg:
+            messages.append({"role": "assistant", "content": assistant_msg})
     messages.append({"role": "user", "content": message})
 
+    # Generate response
     response = ""
-
-    # Make the API call with streaming
     try:
-        for message in client.chat_completion(
+        for chunk in client.chat_completion(
             messages,
             max_tokens=max_tokens,
             stream=True,
             temperature=temperature,
             top_p=top_p,
         ):
-            # Check for non-empty content in the delta
-            if message.choices and message.choices[0].delta.content is not None:
-                token = message.choices[0].delta.content
-                response += token
+            if chunk.choices and chunk.choices[0].delta.content:
+                response += chunk.choices[0].delta.content
                 yield response
     except Exception as e:
-        yield f"Error during API call: {str(e)}"
+        yield f"API Error: {str(e)}"
 
-# Define input components
-token_input = gr.Textbox(type="password", label="HF API Token")
-model_input = gr.Textbox(label="Model Name", value="HuggingFaceH4/zephyr-7b-beta")
+# Input components
+token_input = gr.Textbox(
+    type="password", 
+    label="HF API Token (leave empty for public models)",
+    placeholder="hf_XXXXXXXXXXXX"
+)
+model_input = gr.Dropdown(
+    label="Model Name",
+    choices=[
+        "gpt2",
+        "HuggingFaceH4/zephyr-7b-beta",
+        "meta-llama/Llama-3.1-8B-Instruct"
+    ],
+    value="gpt2"
+)
 
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs:
-https://www.gradio.app/docs/chatinterface
-"""
+# Chat interface
 demo = gr.ChatInterface(
-    title="Chat Interface to test HF Models with a HF TOKEN",
-    description="Enter your HF Token To Acces the Models",
-    respond,
+    fn=respond,
+    title="HF Model Chat Interface",
+    description="Enter token for private models or use public models without token",
     additional_inputs=[
         token_input,
         model_input,
-        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)",
-        ),
+        gr.Textbox(value="You are helpful AI.", label="System Message"),
+        gr.Slider(1, 2048, value=512, label="Max Tokens"),
+        gr.Slider(0.1, 4.0, value=0.7, label="Temperature"),
+        gr.Slider(0.1, 1.0, value=0.95, label="Top-p"),
     ],
 )