Create app.py

app.py

@@ -0,0 +1,91 @@
+import os
+import json
+import base64
+import numpy as np
+from flask import Flask, request, jsonify
+from langchain_experimental.open_clip.open_clip import OpenCLIPEmbeddings
+from sklearn.metrics.pairwise import cosine_similarity
+from io import BytesIO
+from PIL import Image
+# from matplotlib.offsetbox import OffsetImage, AnnotationBbox
+from io import BytesIO
+from pathlib import Path
+
+# ============================== #
+#        INITIALIZE APP          #
+# ============================== #
+app = Flask(__name__)
+clip_embd = OpenCLIPEmbeddings()
+
+BASE_DIR = Path("/app")
+BLOCKS_DIR       = BASE_DIR / "blocks"
+# STATIC_DIR       = BASE_DIR / "static"
+# GEN_PROJECT_DIR  = BASE_DIR / "generated_projects"
+BACKDROP_DIR     = BLOCKS_DIR / "Backdrops"
+SPRITE_DIR       = BLOCKS_DIR / "sprites"
+CODE_BLOCKS_DIR  = BLOCKS_DIR / "code_blocks"
+# === new: outputs rooted under BASE_DIR ===
+OUTPUT_DIR       = BASE_DIR / "outputs"
+
+# ============================== #
+#   LOAD PRE-COMPUTED EMBEDS     #
+# ============================== #
+with open(f"{BLOCKS_DIR}/embeddings.json", "r") as f:
+    embedding_json = json.load(f)
+
+image_paths = [item["file-path"] for item in embedding_json]
+image_embeds = np.array([item["embeddings"] for item in embedding_json])
+
+
+# ============================== #
+#   HELPER: Decode Base64 Image  #
+# ============================== #
+def decode_base64_image(b64_string):
+    img_data = base64.b64decode(b64_string)
+    img = Image.open(BytesIO(img_data)).convert("RGB")
+    return img
+
+
+# ============================== #
+#         API ROUTE              #
+# ============================== #
+@app.route("/match", methods=["POST"])
+def match_image():
+    """
+    Input: JSON { "images": ["<base64_img1>", "<base64_img2>", ...] }
+    Output: Best match path + score for each input image
+    """
+    data = request.get_json()
+    if "images" not in data:
+        return jsonify({"error": "No images provided"}), 400
+
+    results = []
+    for b64_img in data["images"]:
+        try:
+            # Decode and embed input image
+            img = decode_base64_image(b64_img)
+            query_embed = np.array(clip_embd.embed_image([img]))  # embed_image can take PIL images
+
+            # Cosine similarity with stored embeddings
+            sims = cosine_similarity(query_embed, image_embeds)[0]
+            best_idx = np.argmax(sims)
+
+            results.append({
+                "input": b64_img[:50] + "...",  # partial preview
+                "best_match": {
+                    "name": os.path.basename(image_paths[best_idx]),
+                    "path": image_paths[best_idx],
+                    "similarity": float(sims[best_idx])
+                }
+            })
+        except Exception as e:
+            results.append({"error": str(e)})
+
+    return jsonify(results)
+
+
+# ============================== #
+#          MAIN ENTRY            #
+# ============================== #
+if __name__ == "__main__":
+    app.run(debug=True, port=7860)