app.py

import gradio as gr
import tensorflow as tf
import cv2
import numpy as np
# from transformers import pipeline
from keras_preprocessing.image import img_to_array

# pipeline = pipeline(task="dogs-cat-classification", model="carlos-pino/dogs-cats")
# Get model
model = './saves/dogs-cats.h5'

CNN_MODEL = tf.keras.models.load_model(model)
weight_model = CNN_MODEL.get_weights()
CNN_MODEL.set_weights(weight_model)

IMAGE_SIZE = 100
def predict(frame):
    # Parse to gray
    img = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    img = cv2.resize(img, (IMAGE_SIZE, IMAGE_SIZE), interpolation=cv2.INTER_CUBIC)

    # Img normalized
    img = np.array(img).astype(float) / 255

    # Parse to 2D array
    image = img_to_array(img)
    image = np.expand_dims(image, axis=0)

    # Predict
    prediction = CNN_MODEL.predict(image)
    prediction = prediction[0][0]

    # Classification
    if prediction <= 0.5:
        return {"Cat": prediction}

    return {"Dog": prediction}

gr.Interface(
    predict,
    inputs=gr.inputs.Image(label="Upload cat or dog candidate", type="filepath"),
    outputs=gr.outputs.Label(num_top_classes=2),
    title="Dog or Cat?",
).launch()