app.py

import gradio as gr
from tensorflow.keras.models import Sequential
model = Sequential([
  layers.experimental.preprocessing.Rescaling(1./255, input_shape=(img_height, img_width, 3)),
  layers.Conv2D(16, 3, padding='same', activation='relu'),
  layers.MaxPooling2D(),
  layers.Conv2D(32, 3, padding='same', activation='relu'),
  layers.MaxPooling2D(),
  layers.Conv2D(64, 3, padding='same', activation='relu'),
  layers.MaxPooling2D(),
  layers.Flatten(),
  layers.Dense(128, activation='relu'),
  layers.Dense(num_classes,activation='softmax')
])

def predict_image(img):
  img_2d=img.reshape(-1,180,180,3)
  prediction=model.predict(img_2d)[0]
  return {class_names[i]: float(prediction[i]) for i in range(5)}

image = gr.inputs.Image(shape=(180,180))
label = gr.outputs.Label(num_top_classes=5)

gr.Interface(fn=predict_image, inputs=image, outputs=label,interpretation='default').launch()