app.py

import cv2
from tensorflow.keras.models import load_model
import gradio as gr
import tensorflow as tf
import cv2
import numpy as np
from tensorflow.keras.models import load_model

# Load the pre-trained model
new_model = load_model('cat_classifier_model.h5')

def classify_image(image_path):
    img = image.load_img(image_path, target_size=(224, 224))
    img_array = image.img_to_array(img)
    img_array = np.expand_dims(img_array, axis=0)
    img_array /= 255.0  # Rescale to values between 0 and 1 (same as during training)

    prediction = model.predict(img_array)
    if prediction[0][0] > 0.5:
        return "not a tablet"
    else:
        return "is a tablet"

# Create a Gradio interface
iface = gr.Interface(
    fn=classify_image,
    inputs=gr.Image(),
    outputs="text",
    live=True,
      
)

# Launch the Gradio interface
iface.launch()