wizzseen/binary_classification

1import cv22from tensorflow.keras.models import load_model3import gradio as gr4import tensorflow as tf5import cv26import numpy as np7from tensorflow.keras.models import load_model8from tensorflow.keras.models import load_model9from tensorflow.keras.preprocessing import image10import numpy as np11 12# Load the trained model13model = load_model('cat_classifier_model.h5')14 15def predict_cat(image_pil):16    img_resized = image_pil.resize((224, 224))17 18    img_array = np.array(img_resized)19    img_array = np.expand_dims(img_array, axis=0)20    img_array = img_array / 255.021    prediction = model.predict(img_array)22    if prediction[0][0] > 0.5:23        return "not a tablet"24    else:25        return "is a tablet"26 27# Create a Gradio interface28iface = gr.Interface(29    fn=predict_cat,30    inputs=gr.Image(type='pil', label='Upload an image of a tablet'),31    outputs='text'32)33 34# Launch the interface with share=True to create a public link35iface.launch(share=True)36 37 38 39 40 41 42 43 44