Create app.py

app.py

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+import gradio as gr
+from PIL import Image
+import torch
+from torchvision import transforms, models
+
+# Load trained model
+checkpoint = torch.load("animal_model.pth", map_location="cpu")
+class_names = checkpoint["class_names"]
+
+# Define model architecture
+model = models.resnet50(weights=None)  # same as trained
+model.fc = torch.nn.Linear(model.fc.in_features, len(class_names))
+model.load_state_dict(checkpoint["model_state_dict"])
+model.eval()
+
+# Image preprocessing
+transform = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406],
+                         std=[0.229, 0.224, 0.225])
+])
+
+# Prediction function
+def predict(image):
+    img = Image.fromarray(image).convert("RGB")
+    img = transform(img).unsqueeze(0)  # add batch dimension
+
+    with torch.no_grad():
+        outputs = model(img)
+        _, pred = torch.max(outputs, 1)
+
+    return class_names[pred.item()]
+
+# Gradio Interface
+app = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="numpy"),
+    outputs="text",
+    title="Animal Image Classifier",
+    description="Upload an image of an animal and the model will classify it."
+)
+
+if __name__ == "__main__":
+    app.launch()