Create app.py

app.py

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+import torch
+import torch.nn as nn
+from torchvision.transforms import transforms
+from torchvision.models import resnet50
+import gradio as gr
+
+# Import model
+model = resnet50()
+
+# Freeze all layers
+for param in model.parameters():
+    param.requires_grad = False
+
+# Replace FC
+# Parameters of newly constructed modules have requires_grad=True by default
+num_ftrs = model.fc.in_features
+model.fc = nn.Linear(num_ftrs, 2)
+
+# Load parameters
+model.load_state_dict(torch.load('./weights/tuned_resnet50.pth'))
+
+# Define the transformations to be applied to each iamge
+transform = transforms.Compose([
+    transforms.Resize(256),
+    transforms.CenterCrop(224),
+    transforms.ToTensor(),
+    transforms.Normalize(
+        mean=[0.485, 0.456, 0.406],
+        std=[0.229, 0.224, 0.225]
+    )
+])
+
+def predict(image):
+    # Preprocess
+    image = transform(Image.fromarray(image))
+
+    # Model prediction
+    model.eval()
+    output = model(torch.unsqueeze(image,0))
+    
+    # Cast to desired
+    _, prediction = torch.max(output, 1)    # argmax
+
+    # Prediction mapping
+    mapping = {0: 'Fake', 1: 'Authentic'}
+
+    return mapping[int(prediction.item())]
+
+
+api = gr.Interface(
+    fn=predict, 
+    inputs=gr.Image(shape=(224, 224),label="Upload an Image"), 
+    outputs=gr.Textbox(label="Predicted Class"), 
+    title="Image Forgery Detection System",
+    description= "This system checks whether an image was deepfaked. Input an image to be checked."
+)
+
+api.launch(share=True)