app.py

from fastapi import FastAPI, UploadFile, File, Form
from fastapi.middleware.cors import CORSMiddleware
import os
from PyPDF2 import PdfReader
from sentence_transformers import SentenceTransformer
import faiss
import numpy as np
import requests

app = FastAPI()

# РАЗРЕШАЕМ ДОСТУП ДЛЯ VERCEL
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"], # В продакшене замени на свой домен vercel
    allow_methods=["*"],
    allow_headers=["*"],
)

# Инициализация модели (загрузится один раз при старте Space)
model = SentenceTransformer('paraphrase-multilingual-MiniLM-L12-v2')
index = None
chunks = []

@app.post("/upload")
async def upload_pdf(file: UploadFile = File(...)):
    global index, chunks
    pdf_reader = PdfReader(file.file)
    text = ""
    for page in pdf_reader.pages:
        text += page.extract_text()
    
    # Простой чанкинг
    new_chunks = [text[i:i+800] for i in range(0, len(text), 800)]
    chunks.extend(new_chunks)
    
    # Эмбеддинги и FAISS
    embeddings = model.encode(new_chunks)
    dimension = embeddings.shape[1]
    
    if index is None:
        index = faiss.IndexFlatL2(dimension)
    
    index.add(np.array(embeddings).astype('float32'))
    return {"message": f"Загружено {len(new_chunks)} фрагментов"}

@app.post("/ask")
async def ask(question: str = Form(...)):
    if not index or not chunks:
        return {"answer": "Сначала загрузи PDF!"}
    
    # Поиск
    q_emb = model.encode([question])
    D, I = index.search(np.array(q_emb).astype('float32'), k=3)
    
    context = "\n".join([chunks[i] for i in I[0]])
    
    # Запрос к DeepSeek (или любому другому API)
    response = requests.post(
        "https://api.deepseek.com/v1/chat/completions",
        headers={"Authorization": f"Bearer {os.getenv('DEEPSEEK_API_KEY')}"},
        json={
            "model": "deepseek-chat",
            "messages": [
                {"role": "system", "content": f"Отвечай кратко на основе текста:\n{context}"},
                {"role": "user", "content": question}
            ],
            "temperature": 0.1
        }
    )
    
    ans_data = response.json()
    return {"answer": ans_data['choices'][0]['message']['content']}