import gradio as gr import torch from transformers import AutoTokenizer, AutoModelForCausalLM from huggingface_hub import login import os import matplotlib.pyplot as plt import seaborn as sns import numpy as np import time # Authentification login(token=os.environ["HF_TOKEN"]) # Liste des modèles models = [ "meta-llama/Llama-2-13b-hf", "meta-llama/Llama-2-7b-hf", "meta-llama/Llama-2-70b-hf", "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 model = None tokenizer = None def load_model(model_name, progress=gr.Progress()): global model, tokenizer try: for i in progress.tqdm(range(100)): time.sleep(0.01) # Simuler le chargement if i == 25: tokenizer = AutoTokenizer.from_pretrained(model_name) elif i == 75: model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype=torch.float32, device_map="cpu", attn_implementation="eager" ) if tokenizer.pad_token is None: tokenizer.pad_token = tokenizer.eos_token return f"Modèle {model_name} chargé avec succès." except Exception as e: return f"Erreur lors du chargement du modèle : {str(e)}" def analyze_next_token(input_text, temperature, top_p, top_k): global model, tokenizer if model is None or tokenizer is None: return "Veuillez d'abord charger un modèle.", None, None inputs = tokenizer(input_text, return_tensors="pt", padding=True, truncation=True, max_length=512) try: with torch.no_grad(): outputs = model(**inputs) last_token_logits = outputs.logits[0, -1, :] probabilities = torch.nn.functional.softmax(last_token_logits, dim=-1) top_k = 5 top_probs, top_indices = torch.topk(probabilities, top_k) top_words = [tokenizer.decode([idx.item()]).strip() for idx in top_indices] prob_data = {word: prob.item() for word, prob in zip(top_words, top_probs)} prob_plot = plot_probabilities(prob_data) prob_text = "\n".join([f"{word}: {prob:.4f}" for word, prob in prob_data.items()]) attention_heatmap = plot_attention_alternative(inputs["input_ids"][0], last_token_logits) return prob_text, attention_heatmap, prob_plot except Exception as e: return f"Erreur lors de l'analyse : {str(e)}", None, None def generate_text(input_text, temperature, top_p, top_k): global model, tokenizer if model is None or tokenizer is None: return "Veuillez d'abord charger un modèle." inputs = tokenizer(input_text, return_tensors="pt", padding=True, truncation=True, max_length=512) try: with torch.no_grad(): outputs = model.generate( **inputs, max_new_tokens=1, temperature=temperature, top_p=top_p, top_k=top_k ) generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True) return generated_text # Retourne l'input + le nouveau mot except Exception as e: return f"Erreur lors de la génération : {str(e)}" def plot_probabilities(prob_data): words = list(prob_data.keys()) probs = list(prob_data.values()) fig, ax = plt.subplots(figsize=(10, 5)) sns.barplot(x=words, y=probs, ax=ax) 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 plot_attention_alternative(input_ids, last_token_logits): input_tokens = tokenizer.convert_ids_to_tokens(input_ids) attention_scores = torch.nn.functional.softmax(last_token_logits, dim=-1) top_k = min(len(input_tokens), 10) # Limiter à 10 tokens pour la lisibilité top_attention_scores, _ = torch.topk(attention_scores, top_k) fig, ax = plt.subplots(figsize=(12, 6)) sns.heatmap(top_attention_scores.unsqueeze(0).numpy(), annot=True, cmap="YlOrRd", cbar=False, ax=ax) ax.set_xticklabels(input_tokens[-top_k:], rotation=45, ha="right") ax.set_yticklabels(["Attention"], rotation=0) ax.set_title("Scores d'attention pour les derniers tokens") plt.tight_layout() return fig def reset(): global model, tokenizer model = None tokenizer = None return "", 1.0, 1.0, 50, None, None, None, None with gr.Blocks() as demo: gr.Markdown("# Analyse et génération de texte") 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", lines=3) analyze_button = gr.Button("Analyser le prochain token") next_token_probs = gr.Textbox(label="Probabilités du prochain token") with gr.Row(): attention_plot = gr.Plot(label="Visualisation de l'attention") prob_plot = gr.Plot(label="Probabilités des tokens suivants") generate_button = gr.Button("Générer le prochain mot") generated_text = gr.Textbox(label="Texte généré") reset_button = gr.Button("Réinitialiser") load_button.click(load_model, inputs=[model_dropdown], outputs=[load_output]) analyze_button.click(analyze_next_token, inputs=[input_text, temperature, top_p, top_k], outputs=[next_token_probs, attention_plot, prob_plot]) generate_button.click(generate_text, inputs=[input_text, temperature, top_p, top_k], outputs=[generated_text]) reset_button.click(reset, outputs=[input_text, temperature, top_p, top_k, next_token_probs, attention_plot, prob_plot, generated_text]) if __name__ == "__main__": demo.launch()