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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 | |
| # """ | |
| # client = InferenceClient("HuggingFaceH4/zephyr-7b-beta") | |
| # def respond( | |
| # message, | |
| # history: list[tuple[str, str]], | |
| # system_message, | |
| # max_tokens, | |
| # temperature, | |
| # top_p, | |
| # ): | |
| # messages = [{"role": "system", "content": system_message}] | |
| # for val in history: | |
| # if val[0]: | |
| # messages.append({"role": "user", "content": val[0]}) | |
| # if val[1]: | |
| # 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 | |
| # """ | |
| # 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() | |
| # import gradio as gr | |
| # from huggingface_hub import InferenceClient | |
| # # Initialize the client with your desired model | |
| # client = InferenceClient("HuggingFaceH4/zephyr-7b-beta") | |
| # # Format the conversation prompt with history | |
| # def format_prompt(message, history): | |
| # prompt = "<s>" # Beginning of sequence for formatting | |
| # for user_prompt, bot_response in history: | |
| # prompt += f"[INST] {user_prompt} [/INST]" | |
| # prompt += f" {bot_response}</s> " | |
| # prompt += f"[INST] {message} [/INST]" # Format current user message | |
| # return prompt | |
| # # Function to generate responses while keeping conversation context | |
| # def generate( | |
| # prompt, history, temperature=0.9, max_new_tokens=256, top_p=0.95, repetition_penalty=1.0 | |
| # ): | |
| # temperature = float(temperature) | |
| # if temperature < 1e-2: | |
| # temperature = 1e-2 | |
| # top_p = float(top_p) | |
| # generate_kwargs = dict( | |
| # temperature=temperature, | |
| # max_new_tokens=max_new_tokens, | |
| # top_p=top_p, | |
| # repetition_penalty=repetition_penalty, | |
| # do_sample=True, | |
| # seed=42, # Seed for reproducibility | |
| # ) | |
| # # Format the prompt with the history and current message | |
| # formatted_prompt = format_prompt(prompt, history) | |
| # # Stream the generated response | |
| # stream = client.text_generation( | |
| # formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False | |
| # ) | |
| # output = "" | |
| # for response in stream: | |
| # output += response.token.text | |
| # yield output # Yield the streamed output as it's generated | |
| # # Customizable input controls for the chatbot interface | |
| # additional_inputs = [ | |
| # gr.Slider( | |
| # label="Temperature", | |
| # value=0.9, | |
| # minimum=0.0, | |
| # maximum=1.0, | |
| # step=0.05, | |
| # interactive=True, | |
| # info="Higher values produce more diverse outputs", | |
| # ), | |
| # gr.Slider( | |
| # label="Max new tokens", | |
| # value=256, | |
| # minimum=0, | |
| # maximum=1048, | |
| # step=64, | |
| # interactive=True, | |
| # info="The maximum numbers of new tokens", | |
| # ), | |
| # gr.Slider( | |
| # label="Top-p (nucleus sampling)", | |
| # value=0.90, | |
| # minimum=0.0, | |
| # maximum=1, | |
| # step=0.05, | |
| # interactive=True, | |
| # info="Higher values sample more low-probability tokens", | |
| # ), | |
| # gr.Slider( | |
| # label="Repetition penalty", | |
| # value=1.2, | |
| # minimum=1.0, | |
| # maximum=2.0, | |
| # step=0.05, | |
| # interactive=True, | |
| # info="Penalize repeated tokens", | |
| # ) | |
| # ] | |
| # # Define the chatbot interface with interactive sliders and chatbot panel | |
| # gr.ChatInterface( | |
| # fn=generate, | |
| # chatbot=gr.Chatbot(show_label=False, show_share_button=False, show_copy_button=True, likeable=True, layout="panel"), | |
| # additional_inputs=additional_inputs, | |
| # title="""AI Dermatologist Chatbot""" | |
| # ).launch(show_api=False) | |
| import gradio as gr | |
| from huggingface_hub import InferenceClient | |
| # Initialize the client with your desired model | |
| client = InferenceClient("HuggingFaceH4/zephyr-7b-beta") | |
| # Define the system prompt as an AI Dermatologist | |
| def format_prompt(message, history): | |
| prompt = "<s>" | |
| # Start the conversation with a system message | |
| prompt += "[INST] You are an AI Dermatologist designed to assist users with skin and hair care.[/INST]" | |
| for user_prompt, bot_response in history: | |
| prompt += f"[INST] {user_prompt} [/INST]" | |
| prompt += f" {bot_response}</s> " | |
| prompt += f"[INST] {message} [/INST]" | |
| return prompt | |
| # Function to generate responses with the AI Dermatologist context | |
| def generate( | |
| prompt, history, temperature=0.9, max_new_tokens=256, top_p=0.95, repetition_penalty=1.0 | |
| ): | |
| temperature = float(temperature) | |
| if temperature < 1e-2: | |
| temperature = 1e-2 | |
| top_p = float(top_p) | |
| generate_kwargs = dict( | |
| temperature=temperature, | |
| max_new_tokens=max_new_tokens, | |
| top_p=top_p, | |
| repetition_penalty=repetition_penalty, | |
| do_sample=True, | |
| seed=42, | |
| ) | |
| formatted_prompt = format_prompt(prompt, history) | |
| stream = client.text_generation( | |
| formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False | |
| ) | |
| output = "" | |
| for response in stream: | |
| output += response.token.text | |
| yield output | |
| return output | |
| # Customizable input controls for the chatbot interface | |
| additional_inputs = [ | |
| gr.Slider( | |
| label="Temperature", | |
| value=0.9, | |
| minimum=0.0, | |
| maximum=1.0, | |
| step=0.05, | |
| interactive=True, | |
| info="Higher values produce more diverse outputs", | |
| ), | |
| gr.Slider( | |
| label="Max new tokens", | |
| value=256, | |
| minimum=0, | |
| maximum=1048, | |
| step=64, | |
| interactive=True, | |
| info="The maximum numbers of new tokens", | |
| ), | |
| gr.Slider( | |
| label="Top-p (nucleus sampling)", | |
| value=0.90, | |
| minimum=0.0, | |
| maximum=1, | |
| step=0.05, | |
| interactive=True, | |
| info="Higher values sample more low-probability tokens", | |
| ), | |
| gr.Slider( | |
| label="Repetition penalty", | |
| value=1.2, | |
| minimum=1.0, | |
| maximum=2.0, | |
| step=0.05, | |
| interactive=True, | |
| info="Penalize repeated tokens", | |
| ) | |
| ] | |
| # Define the chatbot interface with the starting system message as AI Dermatologist | |
| gr.ChatInterface( | |
| fn=generate, | |
| chatbot=gr.Chatbot(show_label=False, show_share_button=False, show_copy_button=True, likeable=True, layout="panel"), | |
| additional_inputs=additional_inputs, | |
| title="AI Dermatologist" | |
| ).launch(show_api=False) | |