app.py

import gradio as gr
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing import image
import numpy as np
import tempfile

# Load model
model = load_model('cats_dogs_model.h5')
CLASS_NAMES = ['cat', 'dog']

def predict_image(img):
    # Preprocess image
    img = img.resize((224, 224))
    img_array = image.img_to_array(img)
    img_array = np.expand_dims(img_array, axis=0)
    img_array /= 255.0

    # Make prediction
    predictions = model.predict(img_array)
    predicted_class = CLASS_NAMES[np.argmax(predictions[0])]
    confidence = float(np.max(predictions[0]))
    
    return {predicted_class: confidence, "other_class": 1 - confidence}

# Create Gradio interface
interface = gr.Interface(
    fn=predict_image,
    inputs=gr.Image(type="pil", label="Upload Pet Image"),
    outputs=gr.Label(num_top_classes=2),
    title="Cat vs Dog Classifier 🐱 vs 🐶",
    description="Upload an image of a cat or dog to classify it",
    examples=[["cat_example.jpg"], ["dog_example.jpg"]]
)

if __name__ == "__main__":
    interface.launch(share=True)