app.py

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

model_id = "google/flan-t5-base"  # можно попробовать flan-t5-large
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSeq2SeqLM.from_pretrained(model_id)
device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)

def respond(message, history=None):
    if history is None:
        history = []

    prompt = f"Answer the following question about Innopolis University clearly and concisely.\nQuestion: {message}\nAnswer:"
    inputs = tokenizer(prompt, return_tensors="pt").to(device)

    with torch.no_grad():
        outputs = model.generate(
            **inputs,
            max_new_tokens=100,
            do_sample=False,
            eos_token_id=tokenizer.eos_token_id
        )
    answer = tokenizer.decode(outputs[0], skip_special_tokens=True)

    # Отрезаем если модель включила prompt в ответ
    if answer.lower().startswith(prompt.lower()):
        answer = answer[len(prompt):].strip()

    history.append((message, answer))
    return history

iface = gr.ChatInterface(fn=respond, title="Innopolis Q&A")
iface.launch()