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| 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 | |
| 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]: # User message | |
| messages.append({"role": "user", "content": val[0]}) | |
| if val[1]: # Assistant message | |
| messages.append({"role": "assistant", "content": val[1]}) | |
| messages.append({"role": "user", "content": message}) | |
| 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 | |
| """ | |
| demo = gr.ChatInterface( | |
| respond, | |
| 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)", | |
| ), | |
| ], | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch() |