Update app.py

app.py

@@ -29,6 +29,81 @@ from dotenv import load_dotenv
 
 load_dotenv()
 
+
+#===========================================================================
+# This block clears caches on startup
+print("Attempting to clear cache directories...")
+# Define the standard cache paths
+hf_cache_path = Path.home() / ".cache/huggingface"
+ul_cache_path = Path.home() / ".config/Ultralytics"
+
+# Safely delete the directories if they exist
+if hf_cache_path.exists() and hf_cache_path.is_dir():
+    try:
+        shutil.rmtree(hf_cache_path)
+        print(f"Successfully cleared Hugging Face cache at: {hf_cache_path}")
+    except OSError as e:
+        print(f"Error clearing Hugging Face cache: {e}")
+
+if ul_cache_path.exists() and ul_cache_path.is_dir():
+    try:
+        shutil.rmtree(ul_cache_path)
+        print(f"Successfully cleared Ultralytics cache at: {ul_cache_path}")
+    except OSError as e:
+        print(f"Error clearing Ultralytics cache: {e}")
+#===============================================================================
+
+#===============================================================================
+
+# Loading general object detection model (YOLO v8)
+try:
+    general_model = YOLO("yolov8n.pt")
+except:
+    general_model = None
+    print("Warning: General YOLO model not loaded. Install ultralytics and download yolov8n.pt")
+    
+
+# Loading the custom YOLO Object Detection model from the Hub
+try:
+    # Define where to find your model file on the Hub
+    REPO_ID = "ConiferousYogi/Weights_for_Aadhar_Card_Fraud_Detection" # <-- Replace with your repo ID
+    FILENAME = "models/best.pt"
+    COMMIT_SHA = "8e491271abe6e223322b307f6a5f33892a0914b6"
+
+    print("Downloading custom object detection model from the Hub...")
+    local_model_path = hf_hub_download(
+        repo_id=REPO_ID,
+        filename=FILENAME,
+        revision=COMMIT_SHA
+    )
+
+    object_detection_model = YOLO(local_model_path)
+    print("Custom object detection model loaded successfully.")
+    print(f"Object Detection Model classes: {object_detection_model.names}")
+
+except Exception as e:
+    object_detection_model = None
+    print(f"Warning: Custom Object Detection model could not be loaded. Error: {e}")
+
+
+#==============================================================================================
+# Loading the pre-trained Aadhaar-specific YOLO model
+try:
+    repo_config = dict(
+        repo_id="arnabdhar/YOLOv8-nano-aadhar-card",
+        filename="model.pt"
+    )
+    # Loading the pre-trained YOLO Aadhar model
+    aadhaar_model = YOLO(hf_hub_download(**repo_config))
+    id2label = aadhaar_model.names
+    print(f"Text extraction model loaded successfully from Hugging Face.")
+    print(f"Text model classes: {id2label}")
+except Exception as e:
+    aadhaar_model=None
+    print(f"Warning: Text extraction model could not be loaded. Error: {e}")
+
+#=====================================================================================
+
 # Initializing Firebase 
 try:
     # Check for the Hugging Face secret first
@@ -55,6 +130,7 @@ except Exception as e:
     db = None
     
 
+#==================================================================================================
 # Initializing tesseract
 tesseract_path = os.getenv("TESSERACT_PATH", r"C:\Program Files\Tesseract-OCR\tesseract.exe")
 pytesseract.pytesseract.tesseract_cmd = tesseract_path
@@ -148,20 +224,7 @@ def get_exif_data(image_path):
     except Exception as e:
         return {"error": str(e)}
     
-    
-# Loading general object detection model (YOLO v8)
-try:
-    general_model = YOLO("yolov8n.pt")
-except:
-    general_model = None
-    print("Warning: General YOLO model not loaded. Install ultralytics and download yolov8n.pt")
 
-# Loading the pre-trained Aadhaar-specific YOLO model
-repo_config = dict(
-    repo_id="arnabdhar/YOLOv8-nano-aadhar-card",
-    filename="model.pt",
-    local_dir="./models"
-)
 
 def detect_objects_yolo(image_path):
     #Detecting objects in image using YOLO
@@ -182,12 +245,7 @@ def detect_objects_yolo(image_path):
     except Exception as e:
         return {"error": str(e)}
 
-
-# Loading the pre-trained YOLO Aadhar model
-aadhaar_model = YOLO(hf_hub_download(**repo_config))
-id2label = aadhaar_model.names
-print(id2label)
-
+#=========================================================================================================
 
 # Verifying if the image is of frudulent Aadhar card or not using object detection
 def run_object_verification(image_path, object_model_raw_results):
@@ -349,7 +407,10 @@ def create_annotated_image(image_path, text_model_results, object_model_results)
         print(f"Error creating annotated image: {e}")
         return None
 
-        
+
+
+
+#================================================================================================================        
 def analyze_aadhar_pair(front_path, back_path):
     # running the text extaction model on both front and back images
     text_model_raw_results_front = aadhaar_model.predict(front_path, verbose=False)[0]
@@ -518,7 +579,7 @@ def analyze_aadhar_pair(front_path, back_path):
 
     return results
 
-
+#=======================================================================================================
 def transform_results_for_template(results):   
     # --- Overall Assessment ---
     risk_level = results.get('assessment', 'UNKNOWN').replace(" FRAUD RISK", "")
@@ -593,7 +654,7 @@ def transform_results_for_template(results):
     }
     return transformed_data
 
-
+#=====================================================================================================================================
 @app.route('/')
 def home():
     return render_template('upload.html')