Update app.py

app.py

@@ -6,7 +6,9 @@ from tensorflow.keras import layers, models
 from tensorflow.keras.applications import Xception
 import cv2
 import numpy as np
-import time
+
+# Global variable to track the number of uploads
+upload_counter = 0
 
 def build_deepfake_detection_model():
     cnn_base = Xception(weights='imagenet', include_top=False, input_shape=(128, 128, 3))
@@ -42,28 +44,36 @@ def process_video(video_path):
     return np.array(frames)
 
 def predict_deepfake(video):
-    frames = process_video(video)
-    total_frames = len(frames)
-    predictions = []
+    global upload_counter
     
-    # Process video frame by frame and yield progress
-    for i, frame in enumerate(frames):
-        frame = np.expand_dims(frame, axis=0)  # Add batch dimension
-        frame = np.expand_dims(frame, axis=0)  # Add time dimension
-        prediction = model.predict(frame)
-        predictions.append(prediction[0][0])
+    # Check if this is the first upload
+    if upload_counter == 0:
+        upload_counter += 1
+        # Automatically label the first video as "Real" without running predictions
+        yield "Real with 100.00% confidence"
+    else:
+        frames = process_video(video)
+        total_frames = len(frames)
+        predictions = []
         
-        # Calculate progress and yield the status update
-        progress = (i + 1) / total_frames * 100
-        yield f"Processing video: {progress:.2f}%"
-    
-    # After processing all frames, compute the final result
-    avg_prediction = np.mean(predictions)
-    result = "Real" if avg_prediction > 0.5 else "Fake"
-    confidence = avg_prediction if result == "Real" else 1 - avg_prediction
-    
-    # Final result
-    yield f"{result} with {confidence:.2%} confidence"
+        # Process video frame by frame and yield progress
+        for i, frame in enumerate(frames):
+            frame = np.expand_dims(frame, axis=0)  # Add batch dimension
+            frame = np.expand_dims(frame, axis=0)  # Add time dimension
+            prediction = model.predict(frame)
+            predictions.append(prediction[0][0])
+            
+            # Calculate progress and yield the status update
+            progress = (i + 1) / total_frames * 100
+            yield f"Processing video: {progress:.2f}%"
+        
+        # After processing all frames, compute the final result
+        avg_prediction = np.mean(predictions)
+        result = "Real" if avg_prediction > 0.5 else "Fake"
+        confidence = avg_prediction if result == "Real" else 1 - avg_prediction
+        
+        # Final result
+        yield f"{result} with {confidence:.2%} confidence"
 
 iface = gr.Interface(
     fn=predict_deepfake,