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# Importing Necessary Libraries
import tensorflow as tf
import cv2
import numpy as np
import gradio as gr
# Importing the Model
def model(up_img):
cnn = tf.keras.models.load_model("Handwritten_Digit_Recognition_Model.h5")
#import image
gray_image = cv2.cvtColor(up_img, cv2.COLOR_BGR2GRAY)
#preprocess
gray_image = np.expand_dims(gray_image, axis = -1)
gray_image = gray_image.reshape((-1, 28, 28, 1))
#predict
prediction = cnn.predict(gray_image)
#converting the float values to int values for y_test
prediction = np.argmax(prediction, axis = 1)
return "The input digit is: {}".format(int(prediction))
# Running the Model on Gradio
demo = gr.Interface(fn = model, inputs = 'image', outputs = 'text')
demo.launch()
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