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("models/meta-llama/Meta-Llama-3-70B-Instruct")

css = """
body, html {
    height: 100%;
    margin: 0;
    font-family: Arial, Helvetica, sans-serif;
    position: relative;
}
body::before {
    content: "";
    background-image: url('./favicon.jpg');
    background-size: cover;
    background-repeat: no-repeat;
    background-attachment: fixed;
    opacity: 0.5;  /* Ajustez l'opacité ici pour la transparence */
    top: 0;
    left: 0;
    bottom: 0;
    right: 0;
    position: absolute;
    z-index: -1;  /* Placez l'image derrière le contenu */
}
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;
}
"""

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(
    fn=respond,
    css=css,
    title="Voici notre Chatbot: Le Spéc'IA'liste du vrac",
    examples=[
        ["Calcul moi ma facture si j'ai 12 pied par 35 pied de gravier 0-3/4 pour un epaisseur de 3 pouces en livraison zone 4"],
        ["Je veux connaitre les produits de paillis chez le specialiste du vrai"]
    ],
    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 (echantillons nucleus)",
        )
    ]
)

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