main.py

import os
import json
import faiss
import numpy as np
from fastapi import FastAPI, UploadFile, File, Form
from fastapi.middleware.cors import CORSMiddleware
from sentence_transformers import SentenceTransformer
from PIL import Image
import io

# Fix caching permissions for Hugging Face
os.environ["HF_HOME"] = "./cache"
os.environ["TRANSFORMERS_CACHE"] = "./cache"
os.environ["SENTENCE_TRANSFORMERS_HOME"] = "./cache"

app = FastAPI()

# Enable CORS (so frontend on Netlify can call backend on HF)
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],  # for now allow all, can restrict to Netlify domain
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

# Load product metadata
with open("id_mapping.json", "r", encoding="utf-8") as f:
    products = json.load(f)

# Load FAISS index
index = faiss.read_index("products.index")

# Load CLIP model
print("🧠 Loading CLIP model...")
model = SentenceTransformer("sentence-transformers/clip-ViT-B-32", cache_folder="./cache")


@app.get("/")
def root():
    return {"message": "🚀 Visual Product Matcher API is running!"}


@app.post("/search_text")
def search_text(query: str = Form(...), top_k: int = 5, min_score: float = 0.0):
    """
    Search products using text query.
    """
    query_emb = model.encode([query], convert_to_numpy=True)
    distances, indices = index.search(query_emb, top_k)

    results = []
    for score, idx in zip(distances[0], indices[0]):
        if score >= min_score:  # filter by threshold
            item = products[idx]
            item["score"] = float(score)
            results.append(item)

    return {"matches": results}


@app.post("/match")  # 👈 Renamed to match frontend
async def search_image(file: UploadFile = File(None), image_url: str = Form(None), top_k: int = 5, min_score: float = 0.0):
    """
    Search products using image query (upload or URL).
    """
    if file:
        image_bytes = await file.read()
        image = Image.open(io.BytesIO(image_bytes)).convert("RGB")
    elif image_url:
        import requests
        response = requests.get(image_url)
        image = Image.open(io.BytesIO(response.content)).convert("RGB")
    else:
        return {"error": "No image provided"}

    image_emb = model.encode([image], convert_to_numpy=True)
    distances, indices = index.search(image_emb, top_k)

    results = []
    for score, idx in zip(distances[0], indices[0]):
        if score >= min_score:
            item = products[idx]
            item["score"] = float(score)
            results.append(item)

    return {"matches": results}