inference.py

import torch
import torch.nn as nn
import torchvision.models as models
import torchvision.transforms as transforms
from PIL import Image

# ----------------------------
# Labels (all breeds)
# ----------------------------
breeds = [
    "Alambadi", "Amritmahal", "Ayrshire", "Banni", "Bargur", "Bhadawari", "Brown_Swiss",
    "Dangi", "Deoni", "Gir", "Guernsey", "Hallikar", "Hariana", "Holstein_Friesian",
    "Jaffrabadi", "Jersey", "Kangayam", "Kankrej", "Kasargod", "Kenkatha", "Kherigarh",
    "Khillari", "Krishna_Valley", "Malnad_gidda", "Mehsana", "Murrah", "Nagori", "Nagpuri",
    "Nili_Ravi", "Nimari", "Ongole", "Pulikulam", "Rathi", "Red_Dane", "Red_Sindhi",
    "Sahiwal", "Surti", "Tharparkar", "Toda", "Umblachery", "Vechur"
]

# ----------------------------
# Load Model
# ----------------------------
def load_model():
    model = models.resnet18(pretrained=False)
    model.fc = nn.Linear(model.fc.in_features, len(breeds))
    model.load_state_dict(torch.load("bovine_model.pth", map_location="cpu"))
    model.eval()
    return model

model = load_model()

# ----------------------------
# Image Preprocessing
# ----------------------------
transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
])

# ----------------------------
# Prediction Function
# ----------------------------
def predict(image: Image.Image):
    img = transform(image).unsqueeze(0)
    with torch.no_grad():
        outputs = model(img)
        _, predicted = torch.max(outputs, 1)
    return {"breed": breeds[predicted.item()]}