cats-vs-dogs

import tensorflow as tf
from PIL import Image
from huggingface_hub import hf_hub_download
import numpy as np

# Download the model from Hugging Face Hub
model_path = hf_hub_download(
    repo_id="sirunchained/cats-vs-dogs",
    filename="cats-vs-dogs.keras"
)

# Load the model
model = tf.keras.models.load_model(model_path)

classes = ["Cat", "Dog"]
IMG_SIZE = 150

def predict_image(image_path):
    img = Image.open(image_path).resize((IMG_SIZE, IMG_SIZE))
    img_array = np.array(img)
    img_array = tf.expand_dims(img_array, axis=0)
    img_array = tf.cast(img_array, tf.float32)

    predictions = model.predict(img_array)
    predicted_confidence = predictions[0][0] # Since it's a binary classifier with sigmoid output

    if predicted_confidence >= 0.5:
        predicted_class = "Dog"
        confidence = predicted_confidence
    else:
        predicted_class = "Cat"
        confidence = 1 - predicted_confidence

    return predicted_class, confidence

# Example usage:
# predicted_class, confidence = predict_image("path/to/your/image.jpg")
# print(f"Predicted: {predicted_class} with confidence: {confidence:.2f}")