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import os
os.environ["TRANSFORMERS_CACHE"] = "/tmp/hf_cache"
from flask import Flask, request, jsonify
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
import torch
app = Flask(__name__)
# 🔁 Yeni model ismi burada
model_name = "memorease/base-t5-v2"
print("[Startup] Loading model...")
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
print("[Startup] Model loaded.")
@app.route("/ask", methods=["POST"])
def ask_question():
try:
input_text = request.json.get("text")
if not input_text:
return jsonify({"error": "Missing 'text'"}), 400
prompt = f"Only generate a factual and relevant question about this memory: {input_text}"
inputs = tokenizer(prompt, return_tensors="pt", padding=True, truncation=True)
with torch.no_grad():
outputs = model.generate(**inputs, max_new_tokens=64)
question = tokenizer.decode(outputs[0], skip_special_tokens=True)
return jsonify({"question": question})
except Exception as e:
return jsonify({"error": str(e)}), 500
@app.route("/", methods=["GET"])
def healthcheck():
return jsonify({"status": "running"})
if __name__ == "__main__":
app.run(host="0.0.0.0", port=7860)
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