num_detect.py

# -*- coding: utf-8 -*-
"""num_detect.ipynb

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/1GcHZ0KGkpSs8vsjRbjMHBRVZ6M86nqYj
"""

from keras.models import load_model
model=load_model(r"C:\Users\Abhijeet Tripathi\Downloads\num_detect (1).keras")


import  numpy as np
import cv2
from keras.preprocessing import image
import matplotlib.pyplot as plt
def mnist_compatible(image_path, target_size=(28, 28)):

    img = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)
    plt.imshow(img)
    plt.show()



    img_resized = cv2.resize(img, target_size)


    img_inverted = 255 - img_resized


    img_normalized = img_inverted.astype('float32') / 255.0


    img_array = image.img_to_array(img_normalized)

    img_reshaped = img_array.reshape((*target_size, 1))

    return img_reshaped

def predict(dict):
    print(dict)
    path = dict['composite']
    arr = mnist_compatible(path)
    arr = np.expand_dims(arr, axis=0)
    return str(np.argmax(model.predict(arr)))

import gradio as gr

# Import the Brush class
from gradio import Brush

iface = gr.Interface(
    fn=predict,
    inputs=gr.Paint(label="Input Image Component",type="filepath",brush=Brush(colors=["#32cc70"]),canvas_size=(301,601)),
    outputs="text"
)

iface.launch(share='True')