Update main.py

main.py

@@ -1,149 +1,65 @@
-from fastapi import FastAPI, UploadFile, Form
-from fastapi.middleware.cors import CORSMiddleware
-import requests
-import io
-import faiss
-import json
 import os
+import json
+import faiss
 import numpy as np
-from PIL import Image
+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"
 
-# Init FastAPI
 app = FastAPI()
+
+# Enable CORS (for frontend HTML to connect)
 app.add_middleware(
     CORSMiddleware,
-    allow_origins=["*"],  # you can restrict to your Vercel URL later
+    allow_origins=["*"],
     allow_credentials=True,
     allow_methods=["*"],
-    allow_headers=["*"]
+    allow_headers=["*"],
 )
 
-# Load CLIP model once
-print("🧠 Loading CLIP model...")
-model = SentenceTransformer("clip-ViT-B-32")
-
-# Load dataset
-PRODUCTS_FILE = "products.json"
-INDEX_FILE = "products.index"
-
-with open(PRODUCTS_FILE, "r", encoding="utf-8", errors="ignore") as f:
+# Load product metadata
+with open("id_mapping.json", "r", encoding="utf-8") as f:
     products = json.load(f)
 
-# Build or load FAISS index
-if os.path.exists(INDEX_FILE):
-    print("📦 Loading existing FAISS index...")
-    index = faiss.read_index(INDEX_FILE)
-else:
-    print("⚡ Building FAISS index from products.json (first startup only)...")
-    # Encode product names (lightweight, avoids downloading images)
-    texts = [p["name"] + " " + p["category"] + " " + p["brand"]
-             for p in products]
-    embeddings = model.encode(
-        texts, convert_to_numpy=True, normalize_embeddings=True)
-    index = faiss.IndexFlatIP(embeddings.shape[1])
-    index.add(embeddings.astype("float32"))
-    faiss.write_index(index, INDEX_FILE)
-    print(f"✅ Saved FAISS index with {index.ntotal} vectors")
+# 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")
 
-def embed_image(img: Image.Image):
-    return model.encode(img, convert_to_numpy=True, normalize_embeddings=True)
-
-
-def embed_text(query: str):
-    return model.encode([query], convert_to_numpy=True, normalize_embeddings=True)[0]
-
-
-@app.post("/match")
-async def match(
-    file: UploadFile = None,
-    image_url: str = Form(None),
-    min_score: float = Form(0.6),
-    top_k: int = Form(60),
-    categories: str = Form(None),
-    brands: str = Form(None),
-    min_price: float = Form(0),
-    max_price: float = Form(9999)
-):
-    try:
-        # Get query image
-        if file:
-            img = Image.open(io.BytesIO(await file.read())).convert("RGB")
-        elif image_url:
-            img = Image.open(io.BytesIO(requests.get(
-                image_url).content)).convert("RGB")
-        else:
-            return {"matches": []}
-
-        # Encode query
-        q_emb = embed_image(img).reshape(1, -1)
-
-        # Search FAISS
-        scores, ids = index.search(q_emb, top_k)
-
-        # Parse filters
-        categories = json.loads(categories) if categories else []
-        brands = json.loads(brands) if brands else []
-
-        # Collect results
-        results = []
-        for score, idx in zip(scores[0], ids[0]):
-            if score < min_score:
-                continue
-            p = products[idx]
-
-            # Apply filters
-            if categories and p["category"] not in categories:
-                continue
-            if brands and p["brand"] not in brands:
-                continue
-            if not (min_price <= p["price"] <= max_price):
-                continue
 
-            results.append({**p, "score": float(score)})
-        return {"matches": results}
-    except Exception as e:
-        return {"error": str(e)}
+@app.get("/")
+def root():
+    return {"message": "🚀 Visual Product Matcher API is running!"}
 
 
 @app.post("/search_text")
-async def search_text(
-    query: str = Form(...),
-    min_score: float = Form(0.6),
-    top_k: int = Form(60),
-    categories: str = Form(None),
-    brands: str = Form(None),
-    min_price: float = Form(0),
-    max_price: float = Form(9999)
-):
-    try:
-        # Encode text query
-        q_emb = embed_text(query).reshape(1, -1)
-
-        # Search FAISS
-        scores, ids = index.search(q_emb, top_k)
-
-        # Parse filters
-        categories = json.loads(categories) if categories else []
-        brands = json.loads(brands) if brands else []
-
-        # Collect results
-        results = []
-        for score, idx in zip(scores[0], ids[0]):
-            if score < min_score:
-                continue
-            p = products[idx]
-
-            # Apply filters
-            if categories and p["category"] not in categories:
-                continue
-            if brands and p["brand"] not in brands:
-                continue
-            if not (min_price <= p["price"] <= max_price):
-                continue
-
-            results.append({**p, "score": float(score)})
-        return {"matches": results}
-    except Exception as e:
-        return {"error": str(e)}
+def search_text(query: str = Form(...), top_k: int = 5):
+    """
+    Search products using text query.
+    """
+    query_emb = model.encode([query], convert_to_numpy=True)
+    distances, indices = index.search(query_emb, top_k)
+    results = [products[i] for i in indices[0]]
+    return {"query": query, "results": results}
+
+
+@app.post("/search_image")
+async def search_image(file: UploadFile = File(...), top_k: int = 5):
+    """
+    Search products using image query.
+    """
+    image_bytes = await file.read()
+    image = Image.open(io.BytesIO(image_bytes)).convert("RGB")
+    image_emb = model.encode([image], convert_to_numpy=True)
+    distances, indices = index.search(image_emb, top_k)
+    results = [products[i] for i in indices[0]]
+    return {"results": results}