app.py

from flask import Flask, request, render_template
from transformers import T5ForConditionalGeneration, T5Tokenizer
import torch

app = Flask(__name__)

# Load the fine-tuned model and tokenizer
model = T5ForConditionalGeneration.from_pretrained('./finetuned_t5')
tokenizer = T5Tokenizer.from_pretrained('./finetuned_t5')
model.eval()

@app.route('/', methods=['GET', 'POST'])
def index():
    answer = ""
    if request.method == 'POST':
        question = request.form['question']
        input_text = f"question: {question.strip()}"
        inputs = tokenizer(input_text, max_length=128, truncation=True, padding=True, return_tensors="pt")
        outputs = model.generate(
            inputs['input_ids'],
            max_length=64,
            num_beams=4,
            early_stopping=True,
            no_repeat_ngram_size=2
        )
        answer = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return render_template('index.html', answer=answer)

if __name__ == '__main__':
    app.run(debug=True)