run.py

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()