09

app.py

@@ -17,6 +17,7 @@ import numpy as np
 from PIL import Image
 from flask import Flask, request, send_file
 from flask_cors import CORS
+import logging
 
 import torch
 from groundingdino.util.inference import load_model, predict
@@ -38,6 +39,7 @@ predictor = SamPredictor(sam)
 # ─── Flask app ──────────────────────────────────────────────────────────────────
 app = Flask(__name__)
 CORS(app)
+app.config["DEBUG"] = True
 
 def segment(image_pil: Image.Image, prompt: str):
     # Use the proper image preprocessing for GroundingDINO
@@ -86,9 +88,11 @@ def segment_endpoint():
         image_pil = Image.open(image.stream).convert("RGB")
         mask = segment(image_pil, prompt)
     except ValueError as e:
+        app.logger.error(f"ValueError: {e}")
         return {"error": str(e)}, 422
     except Exception as e:
-        return {"error": "not supported"}, 500
+        app.logger.exception("Exception in /segment endpoint")
+        return {"error": str(e)}, 500  # Return actual exception message
 
     overlay = request.args.get("overlay", "false").lower() == "true"
     if overlay:
@@ -109,6 +113,4 @@ if __name__ == "__main__":
     parser.add_argument("--host", default="127.0.0.1")
     parser.add_argument("--port", default=7860, type=int)
     args = parser.parse_args()
-    app.run(host=args.host, port=args.port)
-    args = parser.parse_args()
-    app.run(host=args.host, port=args.port)
+    app.run(host=args.host, port=args.port, debug=True)