app.py

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
import gradio as gr
import json
import random

# Model yükle
model_id = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

# Hafızadan örnekleri yükle
with open("memory_questions.json", "r") as f:
    memory_data = json.load(f)

# Few-shot prompt oluşturan fonksiyon
def get_few_shot_prompt(memory, k=5):
    examples = random.sample(memory_data, k)
    few_shot = "\n".join(
        [f"Memory: {ex['description']}\nQuestion: {ex['question']}" for ex in examples]
    )
    return f"{few_shot}\nMemory: {memory}\nQuestion:"

# Ana fonksiyon
def generate_question(memory):
    prompt = get_few_shot_prompt(memory)
    input_ids = tokenizer(prompt, return_tensors="pt").input_ids
    output = model.generate(input_ids, max_new_tokens=50, do_sample=False)
    result = tokenizer.decode(output[0], skip_special_tokens=True)

    # Sadece cevabı ayıkla
    lines = result.strip().split("Question:")
    return lines[-1].strip() if len(lines) > 1 else result.strip()

# Gradio arayüzü
iface = gr.Interface(
    fn=generate_question,
    inputs=gr.Textbox(label="Your Memory"),
    outputs=gr.Textbox(label="Generated Question"),
    title="MemoRease - TinyLLaMA Chat Generator",
    description="Write a memory, get a question to help recall more details!"
)

iface.launch()