initial commit

num_detect.py

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+# -*- 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')

requirement.txt

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+tensorflow
+numpy
+opencv-python
+matplotlib
+gradio