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 from langdetect import detect # 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" ] # Dictionnaire des langues supportées par modèle model_languages = { "meta-llama/Llama-2-13b-hf": ["en"], "meta-llama/Llama-2-7b-hf": ["en"], "meta-llama/Llama-2-70b-hf": ["en"], "meta-llama/Meta-Llama-3-8B": ["en"], "meta-llama/Llama-3.2-3B": ["en", "de", "fr", "it", "pt", "hi", "es", "th"], "meta-llama/Llama-3.1-8B": ["en", "de", "fr", "it", "pt", "hi", "es", "th"], "mistralai/Mistral-7B-v0.1": ["en"], "mistralai/Mixtral-8x7B-v0.1": ["en", "fr", "it", "de", "es"], "mistralai/Mistral-7B-v0.3": ["en"], "google/gemma-2-2b": ["en"], "google/gemma-2-9b": ["en"], "google/gemma-2-27b": ["en"], "croissantllm/CroissantLLMBase": ["en", "fr"] } # Variables globales model = None tokenizer = None def load_model(model_name, progress=gr.Progress()): global model, tokenizer try: progress(0, desc="Chargement du tokenizer") tokenizer = AutoTokenizer.from_pretrained(model_name) progress(0.5, desc="Chargement du modèle") # Configuration générique pour tous les modèles model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype=torch.float16, device_map="auto", low_cpu_mem_usage=True ) if tokenizer.pad_token is None: tokenizer.pad_token = tokenizer.eos_token progress(1.0, desc="Modèle chargé") return f"Modèle {model_name} chargé avec succès." except Exception as e: return f"Erreur lors du chargement du modèle : {str(e)}" def ensure_token_display(token): """Assure que le token est affiché correctement.""" if token.isdigit() or (token.startswith('-') and token[1:].isdigit()): return tokenizer.decode([int(token)]) return token 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 # Détection de la langue detected_lang = detect(input_text) if detected_lang not in model_languages.get(model.config._name_or_path, []): return f"Langue détectée ({detected_lang}) non supportée par ce modèle.", None, None inputs = tokenizer(input_text, return_tensors="pt", padding=True, truncation=True, max_length=512).to(model.device) try: with torch.no_grad(): outputs = model(**inputs) last_token_logits = outputs.logits[0, -1, :] probabilities = torch.nn.functional.softmax(last_token_logits / temperature, dim=-1) top_k = min(top_k, probabilities.size(-1)) top_probs, top_indices = torch.topk(probabilities, top_k) top_words = [ensure_token_display(tokenizer.decode([idx.item()])) for idx in top_indices] prob_data = {word: prob.item() for word, prob in zip(top_words, top_probs)} prob_text = "Prochains tokens les plus probables :\n\n" for word, prob in prob_data.items(): prob_text += f"{word}: {prob:.2%}\n" prob_plot = plot_probabilities(prob_data) attention_plot = plot_attention(inputs["input_ids"][0], last_token_logits) return prob_text, attention_plot, 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." # Détection de la langue detected_lang = detect(input_text) if detected_lang not in model_languages.get(model.config._name_or_path, []): return f"Langue détectée ({detected_lang}) non supportée par ce modèle." inputs = tokenizer(input_text, return_tensors="pt", padding=True, truncation=True, max_length=512).to(model.device) try: outputs = model.generate( **inputs, max_new_tokens=50, do_sample=True, temperature=temperature, top_p=top_p, top_k=top_k ) generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True) return generated_text 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=(12, 6)) bars = ax.bar(range(len(words)), probs, color='lightgreen') ax.set_title("Probabilités des tokens suivants les plus probables") ax.set_xlabel("Tokens") ax.set_ylabel("Probabilité") ax.set_xticks(range(len(words))) ax.set_xticklabels(words, rotation=45, ha='right') for i, (bar, word) in enumerate(zip(bars, words)): height = bar.get_height() ax.text(i, height, f'{height:.2%}', ha='center', va='bottom', rotation=0) plt.tight_layout() return fig def plot_attention(input_ids, last_token_logits): input_tokens = [ensure_token_display(tokenizer.decode([id])) for id in input_ids] attention_scores = torch.nn.functional.softmax(last_token_logits, dim=-1) top_k = min(len(input_tokens), 10) top_attention_scores, _ = torch.topk(attention_scores, top_k) fig, ax = plt.subplots(figsize=(14, 7)) sns.heatmap(top_attention_scores.unsqueeze(0).cpu().numpy(), annot=True, cmap="YlOrRd", cbar=True, ax=ax, fmt='.2%') ax.set_xticklabels(input_tokens[-top_k:], rotation=45, ha="right", fontsize=10) ax.set_yticklabels(["Attention"], rotation=0, fontsize=10) ax.set_title("Scores d'attention pour les derniers tokens", fontsize=16) cbar = ax.collections[0].colorbar cbar.set_label("Score d'attention", fontsize=12) cbar.ax.tick_params(labelsize=10) 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 avec LLM") 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 la suite du texte") 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()