restructure backend

pythonScripts/build_index.py

@@ -0,0 +1,61 @@
+import os
+import numpy as np
+from PIL import Image
+import torch
+import open_clip
+import faiss
+
+# --- Configuration ---
+CARDS_FOLDER = "cards_old"
+EMBEDDINGS_FILE = "embeddings.npy"
+IDS_FILE = "ids.npy"
+FAISS_INDEX_FILE = "card_index.faiss"
+
+# --- Device ---
+device = "cuda" if torch.cuda.is_available() else "cpu"
+print("Using device:", device)
+
+# --- Load CLIP model ---
+model, _, preprocess = open_clip.create_model_and_transforms(
+    'ViT-L-14', pretrained='laion2b_s32b_b82k'
+)
+model = model.to(device).eval()
+
+# --- Helper: encode image ---
+def encode_image(path):
+    img = Image.open(path).convert("RGB")
+    with torch.no_grad():
+        emb = model.encode_image(preprocess(img).unsqueeze(0).to(device))
+    return emb.cpu().numpy()
+
+# --- Build embeddings ---
+embeddings = []
+ids = []
+
+for fname in os.listdir(CARDS_FOLDER):
+    if fname.lower().endswith((".jpg", ".png")):
+        path = os.path.join(CARDS_FOLDER, fname)
+        emb = encode_image(path)
+        embeddings.append(emb)
+        ids.append(fname)
+        print("Encoded:", fname)
+
+embeddings = np.vstack(embeddings)
+
+# --- Save embeddings & IDs ---
+np.save(EMBEDDINGS_FILE, embeddings)
+np.save(IDS_FILE, np.array(ids))
+print("Saved embeddings and IDs.")
+
+# --- Normalize embeddings ---
+faiss.normalize_L2(embeddings)
+
+# --- Build FAISS index ---
+d = embeddings.shape[1]  # embedding dimension
+index = faiss.IndexFlatIP(d)  # inner product = cosine similarity
+index.add(embeddings)
+print("FAISS index built with", index.ntotal, "cards.")
+
+# --- Save FAISS index ---
+faiss.write_index(index, FAISS_INDEX_FILE)
+print("FAISS index saved:", FAISS_INDEX_FILE)