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| import os | |
| 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 | |
| """ | |
| client = InferenceClient("meta-llama/Meta-Llama-3-8B",token=os.getenv('HF_API_TOKEN')) | |
| # ## None type | |
| # def respond( | |
| # message: str, | |
| # history: list[tuple[str, str]], # This will not be used | |
| # system_message: str, | |
| # max_tokens: int, | |
| # temperature: float, | |
| # top_p: float, | |
| # ): | |
| # messages = [{"role": "system", "content": system_message}] | |
| # # Append only the latest user message | |
| # messages.append({"role": "user", "content": message}) | |
| # response = "" | |
| # try: | |
| # # Generate response from the model | |
| # for message in client.chat_completion( | |
| # messages, | |
| # max_tokens=max_tokens, | |
| # stream=True, | |
| # temperature=temperature, | |
| # top_p=top_p, | |
| # ): | |
| # if message.choices[0].delta.content is not None: | |
| # token = message.choices[0].delta.content | |
| # response += token | |
| # yield response | |
| # except Exception as e: | |
| # yield f"An error occurred: {e}" | |
| # """ | |
| # For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface | |
| # """ | |
| # demo = 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__": | |
| # demo.launch() | |
| ####19 | |
| def respond( | |
| message: str, | |
| history: list[tuple[str, str]], # This will not be used | |
| system_message: str, | |
| max_tokens: int, | |
| temperature: float, | |
| top_p: float, | |
| ): | |
| # Combine the system message and user input into a single prompt | |
| prompt = f"{system_message}\n{message}" | |
| response = "" | |
| try: | |
| # Generate response from the model using text generation method | |
| for message in client.text_generation( | |
| prompt=prompt, | |
| max_new_tokens=max_tokens, | |
| temperature=temperature, | |
| top_p=top_p, | |
| ): | |
| if message.token is not None: | |
| response += message.token | |
| yield response | |
| except Exception as e: | |
| yield f"An error occurred: {e}" | |
| # Define the Gradio interface | |
| demo = 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__": | |
| demo.launch() | |
| # import os | |
| # import gradio as gr | |
| # from huggingface_hub import InferenceClient | |
| # # Initialize the Hugging Face Inference Client | |
| # client = InferenceClient( | |
| # "meta-llama/Meta-Llama-3.1-8B-Instruct", | |
| # token= os.getenv("HF_API_TOKEN"),# Replace with your actual token | |
| # ) | |
| # # Define a function to handle the chat input and get a response from the model | |
| # def chat_with_model(user_input): | |
| # # Call the client to get the model's response | |
| # response = "" | |
| # for message in client.chat_completion( | |
| # messages=[{"role": "user", "content": user_input}], | |
| # max_tokens=500, | |
| # stream=True, | |
| # ): | |
| # response += message.choices[0].delta.content | |
| # return response | |
| # # Create a Gradio interface with a chat component | |
| # with gr.Blocks() as demo: | |
| # chatbot = gr.Chatbot() | |
| # with gr.Row(): | |
| # txt = gr.Textbox(show_label=False, placeholder="Type your message here...") | |
| # txt.submit(chat_with_model, inputs=txt, outputs=chatbot) | |
| # demo.launch() | |