Update app.py

app.py

@@ -1,9 +1,12 @@
+
+##model.load_weights('dfdc_cnn_lstm_model_finetuned.keras')
 import gradio as gr
 import tensorflow as tf
 from tensorflow.keras import layers, models
 from tensorflow.keras.applications import Xception
 import cv2
 import numpy as np
+import time
 
 def build_deepfake_detection_model():
     cnn_base = Xception(weights='imagenet', include_top=False, input_shape=(128, 128, 3))
@@ -21,7 +24,7 @@ def build_deepfake_detection_model():
     model = models.Model(inputs=input_layer, outputs=output)
     return model
 
-# Load the model (you'll need to upload your model weights to Hugging Face)
+# Load the model (ensure to replace the path with your actual model weights)
 model = build_deepfake_detection_model()
 model.load_weights('dfdc_cnn_lstm_model_finetuned.keras')
 
@@ -40,25 +43,35 @@ def process_video(video_path):
 
 def predict_deepfake(video):
     frames = process_video(video)
+    total_frames = len(frames)
     predictions = []
-    for frame in frames:
+    
+    # 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
     
-    return f"{result} with {confidence:.2%} confidence"
+    # Final result
+    yield f"{result} with {confidence:.2%} confidence"
 
 iface = gr.Interface(
     fn=predict_deepfake,
     inputs=gr.Video(),
     outputs="text",
     title="Deepfake Detection",
-    description="Upload a video to check if it's a deepfake or not."
+    description="Upload a video to check if it's a deepfake or not.",
+    live=True  # This allows real-time progress updates in Gradio
 )
 
-iface.launch()
+iface.launch()