app.py_V04_NOK

# V04


import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
import requests

def get_hfhub_models():
    """Récupère la liste des modèles disponibles sur Hugging Face Hub"""
    response = requests.get("https://huggingface.co./api/models")
    if response.status_code == 200:
        models = [model['modelId'] for model in response.json()['models']]
        return models
    else:
        raise Exception(f"Erreur lors de la récupération des modèles : {response.status_code}")

def load_model(model_name):
    """Charge le modèle et le tokenizer"""
    model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True)
    tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
    return model, tokenizer

def generate_text(model, tokenizer, input_text, max_length, temperature):
    """Génère du texte en utilisant le modèle"""
    inputs = tokenizer(input_text, return_tensors="pt")
    output = model.generate(**inputs, max_length=max_length, temperature=temperature)
    return tokenizer.decode(output[0], skip_special_tokens=True)

def main(input_text, max_length, temperature, model_name):
    """Fonction principale pour générer le texte"""
    model, tokenizer = load_model(model_name)
    generated_text = generate_text(model, tokenizer, input_text, max_length, temperature)
    return generated_text

demo = gr.Blocks()

with demo:
    gr.Markdown("# Modèle de Langage")
    
    with gr.Row():
        input_text = gr.Textbox(label="Texte d'entrée")
    with gr.Row():
        max_length_slider = gr.Slider(50, 500, label="Longueur maximale", value=200)
        temperature_slider = gr.Slider(0.1, 1.0, label="Température", value=0.7)
        model_name_dropdown = gr.Dropdown(choices=get_hfhub_models(), label="Sélectionnez un modèle", interactive=True)
    with gr.Row():
        submit_button = gr.Button("Soumettre")
        
    output_text = gr.Textbox(label="Texte généré")
    
    submit_button.click(
        main,
        inputs=[input_text, max_length_slider, temperature_slider, model_name_dropdown],
        outputs=output_text,
        queue=False
    )

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