app.py

## libraries for data preprocessing
import numpy as np 
import pandas as pd 

## libraries for training dl models
import tensorflow as tf
from tensorflow import keras 

## libraries for pre-trained neural network
from tensorflow.keras.applications.xception import preprocess_input

## libraries for loading batch images
from tensorflow.keras.preprocessing.image import load_img

import gradio as gr 

  
## lets load the model 
model = keras.models.load_model('xception_v1_17_0.859.h5')



def maize_disease_classifier(image):
    x = np.array(image)
    X = np.array([x])
    X = preprocess_input(X)
    pred = model.predict(X)
    result = pred[0].argmax()
    ## lets create our labels
    labels = {
        0: 'maize ear rot',
        1: 'maize fall armyworm',
        2: 'maize stem borer'
    }

    label = labels[pred[0].argmax()]
    return label
################### Gradio Web APP ################################ 
title = "Maize Disease Classification App"   
Input = gr.Image(shape=(299, 299), label="Please Upload An Image")
Output1 = gr.Textbox(label="Type Of Maize Disease")
description = "Type Of Diseases: Maize Ear Rot, Maize Fall ArmyWorm, Maize Stem Borer"
iface = gr.Interface(fn=maize_disease_classifier, inputs=Input, outputs=Output1, title=title, description=description)

iface.launch(inline=False)