app.py

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

# Load fine-tuned Assamese ↔ English model
MODEL_NAME = "Sazid2/assamese-english-translator"
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForSeq2SeqLM.from_pretrained(MODEL_NAME)

# Translation function
def translate_text(text):
    if not text.strip():
        return "⚠️ Please enter some text."
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
    with torch.no_grad():
        outputs = model.generate(**inputs, max_length=128)
    translation = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return translation

# Gradio UI
demo = gr.Interface(
    fn=translate_text,
    inputs=gr.Textbox(label="Enter Assamese or English text", placeholder="Type here..."),
    outputs=gr.Textbox(label="Translation"),
    title="🇮🇳 Assamese ↔ English Translator",
    description="Fine-tuned model for Assamese ↔ English translation using Helsinki-NLP/opus-mt-mul-en base.",
    theme="soft"
)

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