import gradio as gr
import tensorflow as tf

# def greet(name):
#     return "Hello " + name + "!!"

def predict_image(image):
    # Preprocess the image
    image = image.reshape((1, 160, 160, 3))

    model = tf.keras.models.load_model('./cat_dog_recognition.h5')

    # Make prediction
    pred = model.predict(image)

    predicted_label = f"{(1-pred[0][0])*100}% Dog \n{pred[0][0]*100}% Cat \nFinal Prediction : {'Dog' if pred[0][0] < 0.5 else 'Cat'}"
    
    # Return the predicted label
    return predicted_label



# input_image = [gr.components.Image(type="filepath" , label="Input Image"),]

# iface = gr.Interface(fn=detect, inputs=input_image, outputs="text" , title="Cat vs Dog Detector")
# iface.launch()

image_input = gr.inputs.Image(shape=(160,160))
label_output = gr.outputs.Textbox()

# Create the Gradio interface
gr.Interface(fn=predict_image, inputs=image_input, outputs=label_output).launch()