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# 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()
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