Create app.py

app.py

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+import gradio as gr
+from transformers import ViTFeatureExtractor, ViTForImageClassification
+import torch
+import requests
+
+# Load the pre-trained ViT model and feature extractor
+model = ViTForImageClassification.from_pretrained("umitkantar/ViTBasePatch16Art")
+feature_extractor = ViTFeatureExtractor.from_pretrained("umitkantar/ViTBasePatch16Art")
+
+# Define the prediction function
+def ai_image_detector(image):
+    # Preprocess the input image
+    inputs = feature_extractor(images=image, return_tensors="pt")
+    
+    # Make a prediction using the loaded model
+    outputs = model(**inputs)
+    predicted_class_idx = torch.argmax(outputs.logits).item()
+    
+    # Calculate the confidence score (percentage)
+    confidence_score = torch.softmax(outputs.logits, dim=1)[0][predicted_class_idx].item() * 100
+    
+    return {f"AI-generated Probability (%): {confidence_score:.2f}": confidence_score}
+
+# Set up the Gradio interface
+image_input = gr.inputs.Image()
+gr.Interface(ai_image_detector, image_input, "label", capture_session=True).launch()