app.py

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"])

# Structure hiérarchique des modèles
models_hierarchy = {
    "meta-llama": {
        "Llama-2": ["7b", "13b", "70b"],
        "Llama-3": ["8B", "3.2-3B", "3.1-8B"]
    },
    "mistralai": {
        "Mistral": ["7B-v0.1", "7B-v0.3"],
        "Mixtral": ["8x7B-v0.1"]
    },
    "google": {
        "gemma": ["2b", "9b", "27b"]
    },
    "croissantllm": {
        "CroissantLLM": ["Base"]
    }
}

# Langues supportées par modèle
models_and_languages = {
    "meta-llama/Llama-2-7b-hf": ["en"],
    "meta-llama/Llama-2-13b-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"]
}

# Paramètres recommandés pour chaque modèle
model_parameters = {
    "meta-llama/Llama-2-13b-hf": {"temperature": 0.8, "top_p": 0.9, "top_k": 40},
    "meta-llama/Llama-2-7b-hf": {"temperature": 0.8, "top_p": 0.9, "top_k": 40},
    "meta-llama/Llama-2-70b-hf": {"temperature": 0.8, "top_p": 0.9, "top_k": 40},
    "meta-llama/Meta-Llama-3-8B": {"temperature": 0.75, "top_p": 0.9, "top_k": 50},
    "meta-llama/Llama-3.2-3B": {"temperature": 0.75, "top_p": 0.9, "top_k": 50},
    "meta-llama/Llama-3.1-8B": {"temperature": 0.75, "top_p": 0.9, "top_k": 50},
    "mistralai/Mistral-7B-v0.1": {"temperature": 0.7, "top_p": 0.9, "top_k": 50},
    "mistralai/Mixtral-8x7B-v0.1": {"temperature": 0.8, "top_p": 0.95, "top_k": 50},
    "mistralai/Mistral-7B-v0.3": {"temperature": 0.7, "top_p": 0.9, "top_k": 50},
    "google/gemma-2-2b": {"temperature": 0.7, "top_p": 0.95, "top_k": 40},
    "google/gemma-2-9b": {"temperature": 0.7, "top_p": 0.95, "top_k": 40},
    "google/gemma-2-27b": {"temperature": 0.7, "top_p": 0.95, "top_k": 40},
    "croissantllm/CroissantLLMBase": {"temperature": 0.8, "top_p": 0.92, "top_k": 50}
}

# Variables globales
model = None
tokenizer = None
selected_language = None

def update_model_choices(company):
    return gr.Dropdown(choices=list(models_hierarchy[company].keys()), value=None)

def update_variation_choices(company, model_name):
    return gr.Dropdown(choices=models_hierarchy[company][model_name], value=None)

def load_model(company, model_name, variation, progress=gr.Progress()):
    global model, tokenizer
    
    full_model_name = f"{company}/{model_name}-{variation}"
    if full_model_name not in models_and_languages:
        full_model_name = f"{company}/{model_name}{variation}"
    
    try:
        progress(0, desc="Chargement du tokenizer")
        tokenizer = AutoTokenizer.from_pretrained(full_model_name)
        progress(0.5, desc="Chargement du modèle")
        
        # Configurations spécifiques par modèle
        if "mixtral" in full_model_name.lower():
            model = AutoModelForCausalLM.from_pretrained(
                full_model_name,
                torch_dtype=torch.float16,
                device_map="auto",
                load_in_8bit=True
            )
        else:
            model = AutoModelForCausalLM.from_pretrained(
                full_model_name,
                torch_dtype=torch.float16,
                device_map="auto"
            )
        
        if tokenizer.pad_token is None:
            tokenizer.pad_token = tokenizer.eos_token
        
        progress(1.0, desc="Modèle chargé")
        available_languages = models_and_languages[full_model_name]
        
        # Mise à jour des sliders avec les valeurs recommandées
        params = model_parameters[full_model_name]
        return (
            f"Modèle {full_model_name} chargé avec succès. Langues disponibles : {', '.join(available_languages)}",
            gr.Dropdown(choices=available_languages, value=available_languages[0], visible=True, interactive=True),
            params["temperature"],
            params["top_p"],
            params["top_k"]
        )
    except Exception as e:
        return f"Erreur lors du chargement du modèle : {str(e)}", gr.Dropdown(visible=False), None, None, None

# Le reste du code reste inchangé...

with gr.Blocks() as demo:
    gr.Markdown("# LLM&BIAS")
    
    with gr.Accordion("Sélection du modèle"):
        company_dropdown = gr.Dropdown(choices=list(models_hierarchy.keys()), label="Choisissez une société")
        model_dropdown = gr.Dropdown(label="Choisissez un modèle", choices=[])
        variation_dropdown = gr.Dropdown(label="Choisissez une variation", choices=[])
        load_button = gr.Button("Charger le modèle")
        load_output = gr.Textbox(label="Statut du chargement")
        language_dropdown = gr.Dropdown(label="Choisissez une langue", visible=False)
        language_output = gr.Textbox(label="Langue sélectionnée")
    
    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")
    
    company_dropdown.change(update_model_choices, inputs=[company_dropdown], outputs=[model_dropdown])
    model_dropdown.change(update_variation_choices, inputs=[company_dropdown, model_dropdown], outputs=[variation_dropdown])
    load_button.click(load_model, 
                      inputs=[company_dropdown, model_dropdown, variation_dropdown], 
                      outputs=[load_output, language_dropdown, temperature, top_p, top_k])
    language_dropdown.change(set_language, inputs=[language_dropdown], outputs=[language_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, language_dropdown, language_output])

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