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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)
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