Image-Classifier/app.py at master · Exstaa/Image-Classifier · GitHub

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import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.models import  load_model
import streamlit as st
import numpy as np

st.header('Image Classification Model')
model = load_model(r'C:\Users\user\Downloads\image classification\Image_classify.keras')
data_cat = ['apple',
 'banana',
 'beetroot',
 'bell pepper',
 'cabbage',
 'capsicum',
 'carrot',
 'cauliflower',
 'chilli pepper',
 'corn',
 'cucumber',
 'eggplant',
 'garlic',
 'ginger',
 'grapes',
 'jalepeno',
 'kiwi',
 'lemon',
 'lettuce',
 'mango',
 'onion',
 'orange',
 'paprika',
 'pear',
 'peas',
 'pineapple',
 'pomegranate',
 'potato',
 'raddish',
 'soy beans',
 'spinach',
 'sweetcorn',
 'sweetpotato',
 'tomato',
 'turnip',
 'watermelon']
img_height = 180
img_width = 180
image =st.text_input('Enter Image name','Apple.jpg')

image_load = tf.keras.utils.load_img(image, target_size=(img_height,img_width))
img_arr = tf.keras.utils.array_to_img(image_load)
img_bat=tf.expand_dims(img_arr,0)

predict = model.predict(img_bat)

score = tf.nn.softmax(predict)
st.image(image, width=200)
st.write('Veg/Fruit in image is ' + data_cat[np.argmax(score)])
st.write('With accuracy of ' + str(np.max(score)*100))