app.py

import gradio as gr
from core.predict import ImageClassifier
import os
from PIL import Image

cwd = os.getcwd()
model_path = os.path.join(cwd,'model','cnn-trained-model.pth')
class_name = {0 : 'Cat' ,1 : 'Dog' ,2 : 'person'}
classifier = ImageClassifier(model_path=model_path,class_name=None)


def classify_image(image):
    image_path = 'uploaded_image.jpg'
    image.save(image_path)

    label,output_path = classifier.predict(image_path)

    return label,Image.open(output_path)

demo = gr.Interface(
    fn=classify_image,
    inputs = gr.Image(type='pil'),
    outputs=[gr.Textbox(label="Prediction"),gr.Image(label="Labeled Image")],
    title="Image Classification Gradio app",
    description="Upload an Image to classify it as Dog,Cat or Person"
)


if __name__ == '__main__':
    demo.launch()