predict.py

import os
import cv2
import numpy as np
import torch

from model import CatDogCNN


LABELS = {
    0: "cat",
    1: "dog"
}

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

model = CatDogCNN().to(device)

# Load model from the same directory as this file
_model_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "cat_dog_model.pth")
model.load_state_dict(torch.load(_model_path, map_location=device))
model.eval()


def preprocess_image(image):
    if image is None:
        raise ValueError("No image provided")

    if hasattr(image, "convert"):
        image = np.array(image.convert("RGB"))
    else:
        image = np.asarray(image)

    if image.ndim == 2:
        image = cv2.cvtColor(image, cv2.COLOR_GRAY2RGB)
    elif image.shape[-1] == 4:
        image = cv2.cvtColor(image, cv2.COLOR_RGBA2RGB)
    elif image.shape[-1] == 1:
        image = cv2.cvtColor(image, cv2.COLOR_GRAY2RGB)

    image = cv2.resize(image, (64, 64))
    image = image.astype(np.float32) / 255.0
    image = np.transpose(image, (2, 0, 1))
    image = torch.from_numpy(image).float().unsqueeze(0)

    return image


def predict(image):
    if image is None:
        return {"cat": 0.0, "dog": 0.0}

    image = preprocess_image(image)
    image = image.to(device)

    with torch.no_grad():
        logits = model(image)
        probabilities = torch.softmax(logits, dim=1)[0]

    return {
        "cat": float(probabilities[0]),
        "dog": float(probabilities[1])
    }