init backend

python_service/app.py

@@ -0,0 +1,81 @@
+from fastapi import FastAPI, File, UploadFile
+from fastapi.responses import JSONResponse
+from fastapi.middleware.cors import CORSMiddleware
+from PIL import Image
+import torch, open_clip, numpy as np, faiss
+import io
+
+app = FastAPI(title="Pokemon Card Image Service")
+
+# --- Allow CORS for Bun / Flutter ---
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+import os
+
+BASE = os.path.dirname(os.path.abspath(__file__))
+
+# --- Configuration ---
+FAISS_INDEX_FILE = os.path.join(BASE, "card_index.faiss")
+EMBEDDINGS_FILE = os.path.join(BASE, "embeddings.npy")
+IDS_FILE = os.path.join(BASE, "ids.npy")
+TOP_K = 5
+
+# --- Load CLIP model ---
+device = "cuda" if torch.cuda.is_available() else "cpu"
+print("Loading model on device:", device)
+model, _, preprocess = open_clip.create_model_and_transforms(
+    "ViT-L-14", pretrained="laion2b_s32b_b82k"
+)
+model = model.to(device).eval()
+
+# --- Load FAISS index ---
+print("Loading FAISS index from disk...")
+index = faiss.read_index(FAISS_INDEX_FILE)
+
+# --- Load IDs / metadata ---
+ids = np.load(IDS_FILE)
+metadata = {idx: {"id": ids[idx], "name": ids[idx]} for idx in range(len(ids))}
+
+print("Service ready! FAISS index contains", index.ntotal, "cards.")
+
+# --- Helper: encode image ---
+def encode_image_bytes(image_bytes):
+    img = Image.open(io.BytesIO(image_bytes)).convert("RGB")
+    print("Image loaded successfully:", img.size)
+    with torch.no_grad():
+        emb = model.encode_image(preprocess(img).unsqueeze(0).to(device))
+    emb = emb.cpu().numpy()
+    faiss.normalize_L2(emb)
+    print("Embedding shape:", emb.shape)
+    return emb
+
+# --- API endpoint ---
+@app.post("/query")
+async def query_image(file: UploadFile = File(...)):
+    try:
+        image_bytes = await file.read()
+        print(f"Received {len(image_bytes)} bytes from {file.filename}")
+
+        emb = encode_image_bytes(image_bytes)
+
+        # --- FAISS search ---
+        D, I = index.search(emb, TOP_K)
+        print("FAISS distances:", D)
+        print("FAISS indices:", I)
+
+        results = [metadata[int(i)] for i in I[0]]
+        return JSONResponse(content={"results": results})
+
+    except Exception as e:
+        print("ERROR during query:", e)
+        return JSONResponse(content={"error": str(e)}, status_code=500)
+
+# --- Run server ---
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=5001)