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("unsloth/Llama-3.2-1B-Instruct") # client = InferenceClient( model="https://fevhx93hdo4c770t.us-east-1.aws.endpoints.huggingface.cloud") def respond( message, history: list[tuple[str, str]], # system_message, # max_tokens, # temperature, # top_p, ): system_message = "You are a Dietician Assistant specializing in providing general guidance on diet, " "nutrition, and healthy eating habits. Answer questions thoroughly with scientifically " "backed advice, practical tips, and easy-to-understand explanations. Keep in mind that " "your role is to assist, not replace a registered dietitian, so kindly remind users to " "consult a professional for personalized advice when necessary." max_tokens = 1512 temperature = 0.9 top_p = 0.95 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, stop=["<|im_end|><|im_end|>", "<|im_end|>", "|im_end|>"], #temperature=temperature, #top_p=top_p, ): token = message.choices[0].delta.content if not token: break response += token yield response.strip("|") """ For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface """ demo = gr.ChatInterface( fn=respond, #title="Hi there! I'm your Dietician Assistant, here to help you with general advice on diet, nutrition, and healthy eating habits. Let's explore your questions.", title="Your Personal Dietician Assistant: Expert Guidance on Healthy Eating and Nutrition", examples=["How can I lose weight safely?", "What is a healthy weight loss rate?", "How do I meal prep efficiently?",], # 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)", # ), # ], css=".svelte-7ddecg h1, .message p {color: #64A149 !important; } .message{background-color: #64A149 !important; }" ) if __name__ == "__main__": demo.launch()