app.py

import gradio as gr
from transformers import T5Tokenizer, T5ForConditionalGeneration


model_name = "cuneytkaya/fine-tuned-t5-small-turkish-mmlu"
tokenizer = T5Tokenizer.from_pretrained(model_name)
model = T5ForConditionalGeneration.from_pretrained(model_name)


def generate_answer(question):
    input_text = f"Soru: {question}"
    
    
    inputs = tokenizer(input_text, return_tensors="pt")
    
    
    outputs = model.generate(**inputs, max_length=50, num_beams=4, early_stopping=True)
    
    
    answer = tokenizer.decode(outputs[0], skip_special_tokens=True)
    
    
    if "Cevap:" in answer:
        return answer.split("Cevap:")[1].strip()
    
    return answer


interface = gr.Interface(
    fn=generate_answer,
    inputs="text",
    outputs="text",
    title="Turkish Exam Question Answering Model",
    description="This model answers questions from Turkish academic exams like KPSS, TUS, etc.",
)


interface.launch(share=True)