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Update app.py
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app.py
CHANGED
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@@ -36,7 +36,6 @@ with open(f"{BLOCKS_DIR}/embeddings.json", "r") as f:
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image_paths = [item["file-path"] for item in embedding_json]
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image_embeds = np.array([item["embeddings"] for item in embedding_json])
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# ============================== #
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# HELPER: Decode Base64 Image #
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# ============================== #
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@@ -45,16 +44,11 @@ def decode_base64_image(b64_string):
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img = Image.open(BytesIO(img_data)).convert("RGB")
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return img
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# ============================== #
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# API ROUTE #
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# ============================== #
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@app.route("/match", methods=["POST"])
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def match_image():
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"""
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Input: JSON { "images": ["<base64_img1>", "<base64_img2>", ...] }
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Output: Best match path + score for each input image
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"""
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data = request.get_json()
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if "images" not in data:
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return jsonify({"error": "No images provided"}), 400
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@@ -62,16 +56,14 @@ def match_image():
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results = []
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for b64_img in data["images"]:
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try:
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# Decode and embed input image
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img = decode_base64_image(b64_img)
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query_embed = np.array(clip_embd.embed_image([img]))
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# Cosine similarity with stored embeddings
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sims = cosine_similarity(query_embed, image_embeds)[0]
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best_idx = np.argmax(sims)
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results.append({
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"input": b64_img[:50] + "...",
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"best_match": {
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"name": os.path.basename(image_paths[best_idx]),
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"path": image_paths[best_idx],
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@@ -84,8 +76,16 @@ def match_image():
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return jsonify(results)
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# ============================== #
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# MAIN ENTRY #
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# ============================== #
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if __name__ == "__main__":
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app.run(debug=True, port=7860)
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image_paths = [item["file-path"] for item in embedding_json]
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image_embeds = np.array([item["embeddings"] for item in embedding_json])
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# ============================== #
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# HELPER: Decode Base64 Image #
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# ============================== #
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img = Image.open(BytesIO(img_data)).convert("RGB")
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return img
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# ============================== #
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# API ROUTE #
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# ============================== #
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@app.route("/match", methods=["POST"])
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def match_image():
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data = request.get_json()
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if "images" not in data:
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return jsonify({"error": "No images provided"}), 400
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results = []
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for b64_img in data["images"]:
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try:
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img = decode_base64_image(b64_img)
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query_embed = np.array(clip_embd.embed_image([img]))
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sims = cosine_similarity(query_embed, image_embeds)[0]
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best_idx = np.argmax(sims)
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results.append({
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"input": b64_img[:50] + "...",
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"best_match": {
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"name": os.path.basename(image_paths[best_idx]),
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"path": image_paths[best_idx],
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return jsonify(results)
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# ============================== #
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# SIMPLE HTML UI #
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# ============================== #
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@app.route("/", methods=["GET", "POST"])
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def index():
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return render_template("index.html")
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# ============================== #
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# MAIN ENTRY #
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# ============================== #
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if __name__ == "__main__":
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app.run(debug=True, port=7860)
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