app.py

import gradio as gr
import tensorflow as tf
from tensorflow import keras


# load pre-trained model
model_path = "/Users/chaninderrishi/Desktop/ML/projects/waste-sorting/models/prod3"
pre_trained_model = keras.models.load_model(model_path)

# classification labels
labels = ['compost', 'e-waste', 'recycle', 'trash']


def classify_image(input):
    prediction = pre_trained_model.predict(input) 
    confidences = {labels[i]: float(prediction[i]) for i in range(4)}
    return confidences

# create Gradio interface
iface = gr.Interface(fn=classify_image, 
             inputs=gr.Image(shape=(224, 224)),
             outputs=gr.Label(num_top_classes=3),
             #examples=["banana.jpg", "car.jpg"]
             )
             
iface.launch(share=True)