app.py

import gradio as gr
from huggingface_hub import InferenceClient

client = InferenceClient("meta-llama/Llama-3.2-3B-Instruct")

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 upload_schedule(files):
    # Process the uploaded files here
    return f"Uploaded {len(files)} file(s)"

with gr.Blocks() as demo:
    with gr.Row():
        with gr.Column(scale=1):
            gr.File(label="Upload Schedule", file_count="multiple", type="file", fn=upload_schedule)
        with gr.Column(scale=3):
            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()