app.py

import gradio as gr
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
import matplotlib.pyplot as plt
import numpy as np
from huggingface_hub import login
import os

login(token=os.environ["HF_TOKEN"])

# Liste des modèles
models = [
    "meta-llama/Llama-2-13b", "meta-llama/Llama-2-7b", "meta-llama/Llama-2-70b",
    "meta-llama/Meta-Llama-3-8B", "meta-llama/Llama-3.2-3B", "meta-llama/Llama-3.1-8B",
    "mistralai/Mistral-7B-v0.1", "mistralai/Mixtral-8x7B-v0.1", "mistralai/Mistral-7B-v0.3",
    "google/gemma-2-2b", "google/gemma-2-9b", "google/gemma-2-27b",
    "croissantllm/CroissantLLMBase"
]

# Variables globales pour stocker le modèle et le tokenizer
model = None
tokenizer = None

def load_model(model_name):
    global model, tokenizer
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", torch_dtype=torch.float16)
    
    # Définir le token de padding s'il n'existe pas
    if tokenizer.pad_token is None:
        tokenizer.pad_token = tokenizer.eos_token
        model.config.pad_token_id = model.config.eos_token_id
    
    return f"Modèle {model_name} chargé avec succès sur GPU."

def generate_text(input_text, temperature, top_p, top_k):
    global model, tokenizer
    
    inputs = tokenizer(input_text, return_tensors="pt", padding=True, truncation=True, max_length=512).to(model.device)
    
    with torch.no_grad():
        outputs = model.generate(
            **inputs,
            max_new_tokens=50,
            temperature=temperature,
            top_p=top_p,
            top_k=top_k,
            output_attentions=True,
            return_dict_in_generate=True
        )
    
    generated_text = tokenizer.decode(outputs.sequences[0], skip_special_tokens=True)
    
    # Obtenir les logits pour le dernier token généré
    last_token_logits = outputs.scores[-1][0]
    
    # Appliquer softmax pour obtenir les probabilités
    probabilities = torch.nn.functional.softmax(last_token_logits, dim=-1)
    
    # Obtenir les top 5 tokens les plus probables
    top_k = 5
    top_probs, top_indices = torch.topk(probabilities, top_k)
    top_words = [tokenizer.decode([idx.item()]) for idx in top_indices]
    
    # Préparer les données pour le graphique des probabilités
    prob_data = {word: prob.item() for word, prob in zip(top_words, top_probs)}
    
    # Extraire les attentions
    attentions = torch.cat([att[-1].mean(dim=1) for att in outputs.attentions], dim=0).cpu().numpy()
    
    attention_data = {
        'attention': attentions,
        'tokens': tokenizer.convert_ids_to_tokens(outputs.sequences[0])
    }
    
    return generated_text, plot_attention(attention_data), plot_probabilities(prob_data)

def plot_attention(attention_data):
    attention = attention_data['attention']
    tokens = attention_data['tokens']
    
    fig, ax = plt.subplots(figsize=(10, 10))
    im = ax.imshow(attention, cmap='viridis')
    plt.colorbar(im)
    ax.set_xticks(range(len(tokens)))
    ax.set_yticks(range(len(tokens)))
    ax.set_xticklabels(tokens, rotation=90)
    ax.set_yticklabels(tokens)
    ax.set_title("Carte d'attention")
    plt.tight_layout()
    return fig

def plot_probabilities(prob_data):
    words = list(prob_data.keys())
    probs = list(prob_data.values())
    
    fig, ax = plt.subplots(figsize=(10, 5))
    ax.bar(words, probs)
    ax.set_title("Probabilités des tokens suivants les plus probables")
    ax.set_xlabel("Tokens")
    ax.set_ylabel("Probabilité")
    plt.xticks(rotation=45)
    plt.tight_layout()
    return fig

def reset():
    return "", 1.0, 1.0, 50, None, None, None

with gr.Blocks() as demo:
    gr.Markdown("# Générateur de texte avec visualisation d'attention")
    
    with gr.Accordion("Sélection du modèle"):
        model_dropdown = gr.Dropdown(choices=models, label="Choisissez un modèle")
        load_button = gr.Button("Charger le modèle")
        load_output = gr.Textbox(label="Statut du chargement")
    
    with gr.Row():
        temperature = gr.Slider(0.1, 2.0, value=1.0, label="Température")
        top_p = gr.Slider(0.1, 1.0, value=1.0, label="Top-p")
        top_k = gr.Slider(1, 100, value=50, step=1, label="Top-k")
    
    input_text = gr.Textbox(label="Texte d'entrée")
    generate_button = gr.Button("Générer")
    
    output_text = gr.Textbox(label="Texte généré")
    
    with gr.Row():
        attention_plot = gr.Plot(label="Visualisation de l'attention")
        prob_plot = gr.Plot(label="Probabilités des tokens suivants")
    
    reset_button = gr.Button("Réinitialiser")
    
    load_button.click(load_model, inputs=[model_dropdown], outputs=[load_output])
    generate_button.click(generate_text, 
                          inputs=[input_text, temperature, top_p, top_k], 
                          outputs=[output_text, attention_plot, prob_plot])
    reset_button.click(reset, 
                       outputs=[input_text, temperature, top_p, top_k, output_text, attention_plot, prob_plot])

demo.launch()