import os
from uuid import uuid4
from fastapi import FastAPI
from fastapi.responses import FileResponse
from fastapi.middleware.cors import CORSMiddleware
from fastapi.staticfiles import StaticFiles
from pydantic import BaseModel, Field
from sentence_transformers import SentenceTransformer
import weaviate
from weaviate.classes.init import Auth


WEAVIATE_URL = os.getenv("WEAVIATE_URL", "https://hrdhwtqlrkqmc8sfizwvpq.c0.asia-southeast1.gcp.weaviate.cloud")
WEAVIATE_API_KEY = os.getenv("WEAVIATE_API_KEY", "pMDX7ysJPkSTUMdV3gEwxhGmyB7wB301fLaJ")
CLASS_NAME = "PdfChunk"

app = FastAPI()

app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_methods=["*"],
    allow_headers=["*"],
)

app.mount("/static", StaticFiles(directory="static"), name="static")

model = SentenceTransformer("all-MiniLM-L6-v2")

client = weaviate.connect_to_weaviate_cloud(
    cluster_url=WEAVIATE_URL,
    auth_credentials=Auth.api_key(WEAVIATE_API_KEY),
)

class QueryRequest(BaseModel):
    text: str = Field(..., min_length=1)
    top_k: int = Field(default=7, ge=1, le=100)

@app.get("/")
def root():
    return {"message": "PDF RAG API is running. Use POST / to query."}

@app.get("/openapi.yaml")
def serve_openapi_yaml():
    return FileResponse("static/openapi.yaml", media_type="text/yaml")

@app.post("/")
def query_weaviate(q: QueryRequest):
    try:
        query_vector = model.encode(q.text).tolist()
        collection = client.collections.get(CLASS_NAME)

        results = collection.query.near_vector(
            near_vector=query_vector,
            limit=q.top_k,
        )

        return {
            "query": q.text,
            "results": [
                {
                    "text": obj.properties.get("text", ""),
                    "source": obj.properties.get("source", ""),
                }
                for obj in results.objects
            ]
        }

    except Exception as e:
        return {"error": str(e)}