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

# Load the trained model
model = load_model('/content/cat_classifier_model.h5')

# Function to predict whether an image contains a cat
def predict_cat(image_content):
    # Convert image content to PIL Image
    img = Image.open(BytesIO(image_content))
    img = img.convert('RGB')
    img = img.resize((224, 224))

    img_array = np.array(img)
    img_array = np.expand_dims(img_array, axis=0)
    img_array = 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=predict_cat,
    inputs=gr.Image(type='file', label='Upload an image of a tablet'),
    outputs='text'
)

# Launch the interface with share=True to create a public link
iface.launch(share=True)