Create app.py

app.py

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+import torch
+import torchvision.transforms as transforms
+from torchvision import models
+from PIL import Image
+import gradio as gr
+
+# Load your trained ResNet-50 model
+model = models.resnet50(pretrained=False)  # Load ResNet-50 architecture
+model.load_state_dict(torch.load("model.pth"))  # Load the trained weights (.pth)
+model.eval()  # Set model to evaluation mode
+
+# Define the transformation required for the input image
+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]),
+])
+
+# Define the labels for ImageNet (or your specific dataset labels)
+# This is typically a list of class labels for classification
+LABELS = ["class_1", "class_2", "class_3", "class_4", "class_5",  # Replace with your classes
+          "class_6", "class_7", "class_8", "class_9", "class_10"]
+
+# Define the prediction function
+def predict(image):
+    image = Image.open(image).convert("RGB")  # Open the image and convert to RGB
+    image = transform(image).unsqueeze(0)  # Apply transformations and add batch dimension
+
+    with torch.no_grad():
+        outputs = model(image)  # Get model predictions
+    _, predicted = torch.max(outputs, 1)  # Get the class with highest probability
+    return LABELS[predicted.item()]  # Return the predicted class label
+
+# Set up the Gradio interface
+interface = gr.Interface(fn=predict, inputs=gr.inputs.Image(type="pil"), outputs="text")
+
+# Launch the interface
+interface.launch()