app.py

import os
os.environ["HF_HOME"] = "/tmp/hf"  # cache scriibil în Space

from fastapi import FastAPI, Request
import json
import faiss
from sentence_transformers import SentenceTransformer
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

app = FastAPI()

# ---------------------------
# 1. Încarcă modelul
# ---------------------------
MODEL_NAME = "google/flan-t5-small"  # public și mic
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForSeq2SeqLM.from_pretrained(MODEL_NAME)

# ---------------------------
# 2. Încarcă articolele și embeddings
# ---------------------------
with open("articles.json", "r", encoding="utf-8") as f:
    articles = json.load(f)

# fiecare articol -> text
sentences = [a["content"] for a in articles]

# embeddings rapide
embedder = SentenceTransformer("all-MiniLM-L6-v2")
embeddings = embedder.encode(sentences)
index = faiss.IndexFlatL2(embeddings.shape[1])
index.add(embeddings)

# ---------------------------
# 3. Endpoint pentru întrebări
# ---------------------------
@app.post("/ask")
async def ask(request: Request):
    data = await request.json()
    question = data.get("question", "")

    # căutare semantică
    q_emb = embedder.encode([question])
    D, I = index.search(q_emb, k=3)
    context = " ".join([sentences[i] for i in I[0]])

    # prompt pentru model
    prompt = f"Context: {context}\nÎntrebare: {question}\nRăspuns:"

    inputs = tokenizer(prompt, return_tensors="pt")
    outputs = model.generate(**inputs, max_new_tokens=150)
    answer = tokenizer.decode(outputs[0], skip_special_tokens=True)

    return {"answer": answer}

# ---------------------------
# 4. Run server
# ---------------------------
if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=7860)