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import gradio as gr
import os
os.environ['KERAS-BACKEND'] = 'tensorflow'
import keras_core as keras
import numpy as np
from keras.models import load_model
import cv2
import tensorflow as tf
import tensorflow.image
def image_predict(img_):
model = load_model('models/final_model.h5')
img = img_ / 255.0
img = tf.image.central_crop(img, central_fraction = .85).numpy()
img = cv2.resize(img, dsize = [224, 224])
img = np.expand_dims(img, axis = 0)
pred = model.predict(img, verbose = 1)
pred = np.argmax(pred, axis = 1)
if pred == 0:
answer = "The inputted image is an Airplane"
elif pred == 1:
answer = "The inputted image is a Car"
return answer
# image_ = gr.Image(label = 'Input Image to be predicted')
# output = gr.Textbox(label = 'Prediction')
# demo = gr.Interface(fn = image_predict, inputs = [image_], outputs = output)
with gr.Blocks() as demo:
image_ = gr.Image(label = 'Input Image to be predicted')
output = gr.Textbox(label = 'Prediction')
btn = gr.Button('Predict')
btn.click(fn = image_predict, inputs = [image_], outputs = output)
demo.launch(share = False) |