app.py

import gradio as gr
import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

# 1) Load the base M2M100 tokenizer (avoids the “non-consecutive added token” error)
BASE_MODEL = "facebook/m2m100_418M"
tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL)

# 2) Load your fine-tuned French⇄Zarma model
FINETUNED_MODEL = "Mamadou2727/Feriji_model"
model = AutoModelForSeq2SeqLM.from_pretrained(FINETUNED_MODEL)

# 3) Ensure the model’s embedding matrix matches the tokenizer vocab size
model.resize_token_embeddings(len(tokenizer))

# 4) Move model to GPU if available
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
model.eval()

# 5) Correct ISO 639-3 code for Zarma (“dje”)
LANG_CODES = {
    "French": "fr",
    "Zarma":  "yo"
}

def translate(text: str, num_seqs: int):
    # set source & target language codes
    tokenizer.src_lang = LANG_CODES["French"]
    tokenizer.tgt_lang = LANG_CODES["Zarma"]

    # tokenize & move to device
    inputs = tokenizer(text, return_tensors="pt", padding=True).to(device)

    # generate translations
    with torch.no_grad():
        outputs = model.generate(
            **inputs,
            forced_bos_token_id=tokenizer.lang_code_to_id[LANG_CODES["Zarma"]],
            num_beams=num_seqs,
            num_return_sequences=num_seqs,
            length_penalty=1.0,
            early_stopping=True
        )

    # decode & join multiple hypotheses
    translations = tokenizer.batch_decode(outputs, skip_special_tokens=True)
    return "\n\n".join(translations)

# 6) Build Gradio app
with gr.Blocks() as app:
    gr.Markdown(
        """
        # FERIJI Translator: French ⇄ Zarma  
        *Beta version – academic & research use only.*
        """
    )

    with gr.Row():
        inp = gr.Textbox(lines=7, label="Français / French")
        beams = gr.Slider(
            label="Nombre de séquences retournées",
            minimum=1, maximum=5, value=1, step=1
        )
        out = gr.Textbox(lines=7, label="Zarma")

    btn = gr.Button("Traduire")
    btn.click(fn=translate, inputs=[inp, beams], outputs=out, api_name="predict")

# On HF Spaces you don’t need share=True
app.launch()