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app (2).py

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+import gradio as gr
+from PIL import Image
+import torch
+import torch.nn as nn
+import torchvision.transforms as transforms
+import torchvision.models as models
+import os
+import torch
+
+# Set device
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+# Load the main classifier (Detector_best_model.pth)
+main_model = models.mobilenet_v3_large(weights=None)  # Updated: weights=None
+
+#num_ftrs = main_model.fc.in_features
+# main_model.fc = nn.Linear(num_ftrs, 2)  # 2 classes: AI-generated_Image, Real_Image
+
+num_ftrs = main_model.classifier[3].in_features
+main_model.classifier[3] = nn.Linear(num_ftrs, 2)
+
+# main_model.fc = nn.Sequential(
+#     nn.Dropout(p=0.5),  # Match the training architecture
+#     nn.Linear(num_ftrs, 2)  # 2 classes: AI-generated Image, Real Image
+# )
+
+main_model.load_state_dict(torch.load('best_model3_mobilenetv3_large.pth', map_location=device, weights_only=True))  # Updated: weights_only=True
+main_model = main_model.to(device)
+main_model.eval()
+
+# Define class names for the classifier based on the Folder structure
+classes_name = ['AI-generated Image', 'Real Image']
+
+def convert_to_rgb(image):
+    """
+    Converts 'P' mode images with transparency to 'RGBA', and then to 'RGB'.
+    This is to avoid transparency issues during model training.
+    """
+    if image.mode in ('P', 'RGBA'):
+        return image.convert('RGB')
+    return image
+
+# Define preprocessing transformations (same used during training)
+preprocess = transforms.Compose([
+    transforms.Lambda(convert_to_rgb),
+    transforms.Resize((224, 224)),  # Resize here, no need for shape argument in gr.Image
+    transforms.ToTensor(),
+    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])  # ImageNet normalization
+])
+
+def classify_image(image):
+    # Open the image using PIL
+    image = Image.fromarray(image)
+    
+    # Preprocess the image
+    input_image = preprocess(image).unsqueeze(0).to(device)
+    
+    # Perform inference with the main classifier
+    with torch.no_grad():
+        output = main_model(input_image)
+        probabilities = torch.nn.functional.softmax(output[0], dim=0)
+        confidence, predicted_class = torch.max(probabilities, 0)
+    
+    # Main classifier result
+    main_prediction = classes_name[predicted_class]
+    main_confidence = confidence.item()
+    
+    return f"Image is : {main_prediction} (Confidence: {main_confidence:.4f})"
+
+# Gradio interface (updated)
+image_input = gr.Image(image_mode="RGB")  # Removed shape argument
+output_text = gr.Textbox()
+
+gr.Interface(fn=classify_image, inputs=image_input, outputs=[output_text], 
+             title="Detect AI-generated Image ",
+             description="Upload an image to Detected AI-generated Image .",
+             theme="default").launch()

requirements.txt

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+Pillow==10.3.0
+torch==2.4.1
+torchvision==0.19.1
+numpy==1.26.4
+gradio
+transformers