updated api

app.py

@@ -3,8 +3,21 @@ import io
 import numpy as np
 import tensorflow as tf
 from fastapi import FastAPI, File, UploadFile
+from fastapi.middleware.cors import CORSMiddleware 
 from PIL import Image
-from huggingface_hub import hf_hub_download, login
+from huggingface_hub import hf_hub_download, snapshot_download, login
+
+# Source repo configs
+REPO_ID = "SaniaE/MRCNN_Petrol_Pump_Segmentation"
+FILENAME = "mask_rcnn_petrol station_0080.h5"
+token = os.getenv("HF_Token")
+
+snapshot_download(
+    repo_id=REPO_ID, 
+    allow_patterns=["mrcnn/*"], 
+    local_dir=".", 
+    token=token
+)
 
 # Import Mask RCNN modules
 from mrcnn.config import Config
@@ -26,17 +39,15 @@ class PredictionConfig(Config):
     NUM_CLASSES = 1 + 1     
     DETECTION_MIN_CONFIDENCE = 0.9
 
-# Configuration constants
-REPO_ID = "SaniaE/MRCNN_Petrol_Pump_Segmentation"
-FILENAME = "mask_rcnn_petrol station_0080.h5"
+# Configuration constants 
 config = PredictionConfig()
 graph = tf.get_default_graph()
 model_eval = None
 
+
 @app.on_event("startup")
 def load_model():
     global model_eval
-    token = os.getenv("HF_Token")          
     
     if token: 
         login(token=token)
@@ -47,10 +58,12 @@ def load_model():
     
     weights_path = hf_hub_download(repo_id=REPO_ID, filename=FILENAME, token=token)
     
-    model_eval = modellib.MaskRCNN(mode="inference", model_dir=".", config=config)
-    model_eval.load_weights(weights_path, by_name=True)
-    
-    model_eval.keras_model._make_predict_function()
+    # Initialize model within the same graph context
+    with graph.as_default():
+        model_eval = modellib.MaskRCNN(mode="inference", model_dir=".", config=config)
+        model_eval.load_weights(weights_path, by_name=True)
+        model_eval.keras_model._make_predict_function()
+        
     print("Model loaded successfully.")