app.py

import gradio as gr
from huggingface_hub import InferenceClient
from backend import MedicalAssistant

"""
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")
assistant = MedicalAssistant()

# 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

def respond(
        message,
        history: list[tuple[str, str]]
    ):
    response = assistant.generate_response(message)
    return response

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()