Create app.py

app.py

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+import gradio as gr
+import torch
+import torch.nn as nn
+from torchvision import models, transforms
+from safetensors.torch import load_file
+from PIL import Image
+
+# 1. Rebuild the blank ResNet-50 architecture
+model = models.resnet50(weights=None)
+model.fc = nn.Linear(model.fc.in_features, 2) # 2 classes: Fake (0) and Real (1)
+
+# 2. Load YOUR trained weights from the safetensors file
+# Using map_location='cpu' ensures it works on Hugging Face's free CPU tier!
+state_dict = load_file("model.safetensors", device="cpu")
+
+# (Optional safety check) If you trained with DataParallel, keys might have "module." in front.
+# This removes it so the weights load perfectly no matter what.
+state_dict = {k.replace('module.', ''): v for k, v in state_dict.items()}
+
+# Inject the weights into the skeleton and set to test mode
+model.load_state_dict(state_dict)
+model.eval()
+
+# 3. Define the strict patch transform (No randomness!)
+test_transform = transforms.Compose([
+    transforms.Resize(256), # Standard practice to resize slightly before center cropping
+    transforms.CenterCrop(224), 
+    transforms.ToTensor(),
+    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+])
+
+classes = ['FAKE', 'REAL']
+
+# 4. Create the prediction function that Gradio will call
+def predict_image(img):
+    if img is None:
+        return None
+        
+    # Ensure image is strictly RGB (drops alpha channels from PNGs)
+    img = img.convert('RGB')
+    
+    # Apply transforms and add the batch dimension
+    img_tensor = test_transform(img).unsqueeze(0) 
+    
+    with torch.no_grad():
+        # Pass through model
+        preds = model(img_tensor)
+        # Convert raw numbers to percentages (0.0 to 1.0)
+        probs = torch.nn.functional.softmax(preds[0], dim=0)
+        
+    # Gradio expects a dictionary of { "Class Name": probability }
+    return {classes[i]: float(probs[i]) for i in range(2)}
+
+# 5. Build and launch the Web App!
+interface = gr.Interface(
+    fn=predict_image,
+    inputs=gr.Image(type="pil", label="Upload an Image"),
+    outputs=gr.Label(num_top_classes=2, label="Prediction"),
+    title="PixelSleuth: AI Image Detector 🕵️‍♂️",
+    description="Upload an image to see if it is a Real Photograph or AI-Generated (Fake). The model analyzes microscopic pixel artifacts to make its decision.",
+    allow_flagging="never" # Turns off the confusing "Flag" button for users
+)
+
+interface.launch()