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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) |