app.py

import gradio as gr
from huggingface_hub import InferenceClient

# Initialisation du client d'inférence pour Hugging Face
client = InferenceClient("meta-llama/Meta-Llama-3-8B-Instruct")

css = """
body, html {
    height: 100%;
    margin: 0;
    font-family: Arial, Helvetica, sans-serif;
}
.container {
    min-height: 100%;
    background-image: url('./favicon.ico/'); /* Remplacez 'path_to_your_image.jpg' par le chemin de votre image */
    background-position: center;
    background-repeat: no-repeat;
    background-size: cover;
}
h1 {
    background: radial-gradient(circle, red, black);
    -webkit-background-clip: text;
    -webkit-text-fill-color: transparent;
    font-size: 2em;
    text-align: center;
    margin-top: 0;
}
"""

# Définition de la fonction de réponse
def respond(message, history, system_message, max_tokens, temperature, top_p):
    messages = [{"role": "system", "content": system_message}]
    for user_msg, bot_msg in history:
        if user_msg:
            messages.append({"role": "user", "content": user_msg})
        if bot_msg:
            messages.append({"role": "assistant", "content": bot_msg})
    messages.append({"role": "user", "content": message})

    response = client.chat_completion(
        messages,
        max_tokens=max_tokens,
        stream=False,
        temperature=temperature,
        top_p=top_p
    )
    return response['choices'][0]['message']

# Création de l'interface Gradio
demo = gr.Interface(
    fn=respond,
    inputs=[
        "text",
        gr.Dataframe(headers=["User", "Assistant"], datatype=["str", "str"], value=[["Hello", "Hi there!"]]),
        "text",
        "number",
        "number",
        "number"
    ],
    outputs="text",
    title="Chat with Meta-Llama",
    description="This is a Gradio interface for chatting with a model hosted on Hugging Face.",
    css=css
)

if __name__ == "__main__":
    demo.launch()