adding example ussage

app.py

@@ -4,6 +4,7 @@ import torch.nn as nn
 from torchvision import transforms
 from PIL import Image
 import time
+import os
 
 from concrete.fhe import Configuration
 from concrete.ml.torch.compile import compile_torch_model
@@ -37,6 +38,7 @@ def load_secure_model(model):
     )
     return secure_model
 
+# Load models
 model = load_model('models/deepfake_detection_model.pth', 'cpu')
 secure_model = load_secure_model(model)
 
@@ -72,19 +74,27 @@ def predict(image, mode):
     predicted_class = class_names[preds[0]]
     return f"Predicted: {predicted_class}", f"Time taken: {elapsed_time:.2f} seconds"
 
+
+# Path to example images for "Fake" and "Real" classes
+example_images = [
+    ["data/fake/fake_1.jpeg", "Fast"],   # Fake example
+    ["data/real/real_1.jpg", "Fast"],   # Real example
+]
+
 # Gradio interface
 iface = gr.Interface(
     fn=predict,
     inputs=[
-        gr.Image(type="filepath", label="Upload an Image"),  # Update to gr.Image
-        gr.Radio(choices=["Fast", "Secure"], label="Inference Mode", value="Fast")  # Update to gr.Radio
+        gr.Image(type="filepath", label="Upload an Image"),  # Image input
+        gr.Radio(choices=["Fast", "Secure"], label="Inference Mode", value="Fast")  # Inference mode
     ],
     outputs=[
-        gr.Textbox(label="Prediction"),  # Update to gr.Textbox
-        gr.Textbox(label="Time Taken")  # Update to gr.Textbox
+        gr.Textbox(label="Prediction"),  # Prediction output
+        gr.Textbox(label="Time Taken")  # Time taken output
     ],
+    examples=example_images,  # Add default examples for Fake and Real images
     title="Deepfake Detection Model",
-    description="Upload an image and select the inference mode (Fast or Secure)."
+    description="Upload an image or select a sample and choose the inference mode (Fast or Secure)."
 )
 
 if __name__ == "__main__":