Update app.py

app.py

@@ -2,60 +2,80 @@ 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
+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
 """
 
-# Getting the HF Token and Well as the model 
-token_input = gr.Textbox(type="password", label="HF API Token")
-
-if not token:
-    return "Please provide an HF API Token."
-
-model_input = gr.Textbox(label="Model Name", value="HuggingFaceH4/zephyr-7b-beta")
-
-if not model_input:
-    model_input = "meta-llama/Llama-3.1-8B-Instruct"
-
-client = InferenceClient(model_input)
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
+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
+    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
+
+    # Build the message history, starting with the system message
     messages = [{"role": "system", "content": system_message}]
 
     for val in history:
-        if val[0]:
+        if val[0]:  # User message
             messages.append({"role": "user", "content": val[0]})
-        if val[1]:
+        if val[1]:  # Assistant message
             messages.append({"role": "assistant", "content": val[1]})
 
     messages.append({"role": "user", "content": message})
 
     response = ""
 
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-
-
+    # Make the API call with streaming
+    try:
+        for message 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
+                yield response
+    except Exception as e:
+        yield f"Error during API call: {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")
 
 """
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
+For information on how to customize the ChatInterface, peruse the gradio docs:
+https://www.gradio.app/docs/chatinterface
 """
 demo = gr.ChatInterface(
     respond,
@@ -75,6 +95,5 @@ demo = gr.ChatInterface(
     ],
 )
 
-
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()