Create app.py

app.py

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+import torch
+from torchvision import models, transforms
+from PIL import Image
+import gradio as gr
+
+# =======================
+# Configuration
+# =======================
+device = "cuda" if torch.cuda.is_available() else "cpu"
+MODEL_PATH = "cattle_breed_efficientnetb3_pytorch.pth"  # Upload this to the Space
+CLASS_NAMES = ["Gir", "Deoni", "Murrah"]
+
+# =======================
+# Load Model
+# =======================
+model = models.efficientnet_b3(pretrained=False)
+model.classifier[1] = torch.nn.Linear(model.classifier[1].in_features, len(CLASS_NAMES))
+model.load_state_dict(torch.load(MODEL_PATH, map_location=device))
+model.to(device)
+model.eval()
+
+# =======================
+# Image Preprocessing
+# =======================
+transform = transforms.Compose([
+    transforms.Resize((300, 300)),
+    transforms.ToTensor(),
+    transforms.Normalize([0.485, 0.456, 0.406],
+                         [0.229, 0.224, 0.225])
+])
+
+# =======================
+# Prediction Function
+# =======================
+def predict(image):
+    image = image.convert("RGB")
+    img_tensor = transform(image).unsqueeze(0).to(device)
+    with torch.no_grad():
+        output = model(img_tensor)
+        pred_idx = torch.argmax(output, dim=1).item()
+    return CLASS_NAMES[pred_idx]
+
+# =======================
+# Gradio Interface
+# =======================
+iface = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil"),
+    outputs="text",
+    title="Indian Bovine Breed Classifier",
+    description="Upload an image of a cow and the model will predict its breed."
+)
+
+iface.launch()