app.py

import tensorflow
from tensorflow import keras
from keras.models import load_model
model1 = load_model("inception.h5")

img_width, img_height = 180, 180
class_names = ['daisy', 'dandelion', 'roses', 'sunflowers', 'tulips']
num_classes = len(class_names)

def predict_image(img):
    img_4d = img.reshape(-1, img_width, img_height, 3)      # 4D coz model trained on multiple 3Ds
    prediction = model1.predict(img_4d)[0]
    return {class_names[i]: float(prediction[i]) for i in range(num_classes)}


import gradio as gr
image = gr.inputs.Image(shape=(img_height, img_width))
label = gr.outputs.Label(num_top_classes=num_classes)
examples = [
                ["NAME: OLUMIDE TOLULOPE SAMUEL,"],
                ["MATRIC NO: HNDCOM/22/037"],
                ["CLASS: HND1"],
                ["LEVEL: 300L"],
                ["DEPARTMENT: COMPUTER SCIENCE"],
             ],


gr.Interface(fn=predict_image, inputs=image, outputs=label, 
             title="Flower Classification using InceptionV3",
             description="A flower classification app built using python and deployed using gradio",
             examples=examples,
             interpretation='default').launch()