dev check working

app.py

@@ -10,6 +10,60 @@ def greet_json():
 
 #--------------------------------------------------------------------------------------------------------------------
 
+import os
+import gdown
+
+file_id = "1zhisRgRi2qBFX73VFhzh-Ho93MORQqVa" 
+output_dir = "./downloads"  
+output_file = "file.h5"  
+
+if not os.path.exists(output_dir):
+    os.makedirs(output_dir)
+
+output_path = os.path.join(output_dir, output_file)
+
+url = f"https://drive.google.com/uc?id={file_id}"
+
+try:
+    gdown.download(url, output_path, quiet=False)
+    print(f"File downloaded successfully to: {output_path}")
+except Exception as e:
+    print(f"Error downloading file: {e}")
+
+output_file = "file.h5"  
+file_path = os.path.join(output_dir, output_file)
+
+
+#--------------------------------------------------------------------------------------------------------------------
+
+file_id = "1wIaycDFGTF3e0PpAHKk-GLnxk4cMehOU" 
+output_dir = "./downloads"  
+output_file = "file2.h5"  
+
+if not os.path.exists(output_dir):
+    os.makedirs(output_dir)
+
+output_path = os.path.join(output_dir, output_file)
+
+url = f"https://drive.google.com/uc?id={file_id}"
+
+try:
+    gdown.download(url, output_path, quiet=False)
+    print(f"File downloaded successfully to: {output_path}")
+except Exception as e:
+    print(f"Error downloading file: {e}")
+
+output_file = "file2.h5"  
+file_path = os.path.join(output_dir, output_file)
+
+
+if os.path.exists(file_path):
+    print(f"The file '{output_file}' exists at '{file_path}'.")
+else:
+    print(f"The file '{output_file}' does not exist at '{file_path}'.")
+
+#--------------------------------------------------------------------------------------------------------------------
+
 import os
 import numpy as np
 import tensorflow as tf
@@ -32,8 +86,8 @@ class UnifiedDeepfakeDetector:
     def __init__(self):
         self.input_shape = (224, 224, 3)
         self.vgg_model = self.build_vgg16_model()
-        self.dense_model = tf.keras.models.load_model('deepfake_detection_model.h5')
-        self.cnn_model = tf.keras.models.load_model('audio_deepfake_detection_model_cnn.h5')
+        self.dense_model = tf.keras.models.load_model('file.h5')
+        self.cnn_model = tf.keras.models.load_model('file2.h5')
         self.melody_machine = pipeline(model="MelodyMachine/Deepfake-audio-detection-V2")
 
     def build_vgg16_model(self):