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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}
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