goai_helpers/goai_traduction.py

import torch
import spaces
from transformers import pipeline, AutoModelForSeq2SeqLM, AutoTokenizer, GenerationConfig
from peft import PeftModel, PeftConfig
import os
import unicodedata
from huggingface_hub import login

max_length = 512
auth_token = os.getenv('HF_SPACE_TOKEN')
login(token=auth_token)


@spaces.GPU
def goai_traduction(text, src_lang, tgt_lang):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    
    if src_lang == "fra_Latn" and tgt_lang == "mos_Latn":
        model_id = "ArissBandoss/nllb-200-distilled-600M-finetuned-fr-to-mos-V4"
    elif src_lang == "mos_Latn" and tgt_lang == "fra_Latn":
        model_id = "ArissBandoss/3b-new-400"  
    else:
        model_id = "ArissBandoss/nllb-200-distilled-600M-finetuned-fr-to-mos-V4"
    
    print(f"Chargement du modèle: {model_id}")
    tokenizer = AutoTokenizer.from_pretrained(model_id, token=auth_token)
    model = AutoModelForSeq2SeqLM.from_pretrained(model_id, token=auth_token).to(device)
    
    print(f"Texte brut ({len(text)} caractères / {len(text.split())} mots):")
    print(text)
    
    print(f"Configuration du modèle:")
    print(f"- tokenizer.model_max_length: {tokenizer.model_max_length}")
    print(f"- Position embeddings shape: {model.model.encoder.embed_positions.weights.shape}")
    print(f"- decoder.embed_positions shape: {model.model.decoder.embed_positions.weights.shape}")
    
    # Configuration du tokenizer
    tokenizer.src_lang = src_lang
    
    # Tokenisation
    inputs = tokenizer(text, return_tensors="pt", truncation=False).to(device)
    input_ids = inputs["input_ids"][0]
    
    print("Tokens d'entrée:")
    print(f"- Nombre de tokens: {input_ids.shape[0]}")
    print(f"- Premiers tokens: {input_ids[:10].tolist()}")
    print(f"- Derniers tokens: {input_ids[-10:].tolist()}")
    
    # ID du token de langue cible
    tgt_lang_id = tokenizer.convert_tokens_to_ids(tgt_lang)
    print(f"Token ID de la langue cible ({tgt_lang}): {tgt_lang_id}")
    bad_words_ids = [[tokenizer.eos_token_id]]
    
    for length_penalty in [1.0, 1.5, 2.0]:
        for num_beams in [5, 10]:
            print(f"\nTest avec length_penalty={length_penalty}, num_beams={num_beams}")
            

            outputs = model.generate(
                **inputs,
                forced_bos_token_id=tgt_lang_id,
                max_new_tokens=2048,
                early_stopping=False,
                num_beams=num_beams,
                no_repeat_ngram_size=0,
                bad_words_ids=bad_words_ids,
                length_penalty=length_penalty
            )
            
        
            translation = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]
            
            print(f"Traduction ({len(translation)} caractères / {len(translation.split())} mots):")
            print(translation)
            output_ids = outputs[0]
            print(f"- Nombre de tokens générés: {output_ids.shape[0]}")
            print(f"- Premiers tokens générés: {output_ids[:10].tolist()}")
            print(f"- Derniers tokens générés: {output_ids[-10:].tolist()}")
            
    return translation

def real_time_traduction(input_text, src_lang, tgt_lang):
    return goai_traduction(input_text, src_lang, tgt_lang)