Update app.py

app.py

@@ -1,172 +1,19 @@
-  ## Importing The Dependencies
-
-import numpy as np
-import matplotlib.pyplot as plt
-import seaborn as sns
-import cv2 
-import cv2_imshow
-import PIL 
-import tensorflow as tf
-tf.random.set_seed(3)
-from tensorflow import keras
-from keras.datasets import mnist
-from sklearn.metrics import confusion_matrix
-
-
-#Loading MINST Data from Keras.datasets
-
-(x_train,y_train),(x_test,y_test) = mnist.load_data()
-type(x_train)
-# Shape of Numpy Arrays
-
-print(x_train.shape,y_train.shape,x_test.shape,y_test.shape)
-# Training Data = 60000
-# Testing Data = 10000
-# Image Diemention = 28x28
-# Grayscale Image = 1 Channel
-#Printing 10th images
-
-print(x_train[10])
-print(x_train[10].shape)
-#Displaying The Imgae
-plt.imshow(x_train[25])
-
-#Displaying Labels
-print(y_train[25])
-
-## Image Labels
-print(y_train.shape,y_test.shape)
-#uinque Values in Y_train
-print(np.unique(y_train))
-
-#uinque Values in Y_test
-print(np.unique(y_test))
-
-# We can use these labels as such or we can also apply OneHOtencoding
-
-# All the images have same diemention in this data set ,if not ,we have to resize all the images to a common dimention
-#Scalling the values
-
-x_train = x_train/255
-x_test = x_test/255
-#Printing 10th images
-
-print(x_train[10])
-# Building The Neural Network 
-# Setting up the layers of Neural Network
-
-model = keras.Sequential([
-      keras.layers.Flatten(input_shape=(28,28)),
-      keras.layers.Dense(50,activation='relu'),
-      keras.layers.Dense(50,activation='relu'),
-      keras.layers.Dense(10,activation='sigmoid')
-
-
-
-])
-#Compiling the neural network
-
-model.compile(optimizer='adam',loss = 'sparse_categorical_crossentropy',metrics=['accuracy'])
-# Training the Neural Network
-
-model.fit(x_train,y_train,epochs=10,)
-
-# Training Data Acurracy is : 98.83%
-
-
-
-
-# ***Accuracy on Test Data***
-
-loss,accuracy = model.evaluate(x_test,y_test)
-print(accuracy)
-## **Test Data Acurracy is : 96.99%**
-
-print(x_test.shape)
-#First test point in x_test
-
-plt.imshow(x_test[0])
-plt.show()
-print(y_test[0])
-Y_pred = model.predict(x_test)
-print(Y_pred.shape)
-print(Y_pred[0])
-
-# model.predict gives prediction of probability of each class for that data point
-
-# Converting the prediction probability to class label
-
-Label_for_first_image = np.argmax(Y_pred[0])
-print(Label_for_first_image)
-# Converting the prediction probability to class label for all test data
-
-Y_pred_label = [np.argmax(i) for i in Y_pred]
-print(Y_pred_label)
-
-
-# y_test  - is my true Labels 
-# Y_pred labels -  my prdicted labels
-
-## confusion Matrix
-conf_max = confusion_matrix(y_test,Y_pred_label)
-print(conf_max)
-plt.figure(figsize=(15,7))
-sns.heatmap(conf_max,annot=True,fmt='d',cmap='Blues')
-
-
-## Building a Predictive System
-input_image_path = '/content/download.png'
-
-input_image = cv2.imread(input_image_path)
-
-type(input_image)
-print(input_image)
-cv2_imshow(input_image)
-input_image.shape
-Grayscale = cv2.cvtColor(input_image,cv2.COLOR_RGB2GRAY)
-Grayscale.shape
-input_image_resize = cv2.resize(Grayscale,(28,28))
-input_image_resize.shape
-cv2_imshow(input_image_resize)
-input_image_resize  = input_image_resize/255
-input_reshaped = np.reshape(input_image_resize,[1,28,28])
-input_prediction = model.predict(input_reshaped)
-print(input_prediction)
-input_pred_label = np.argmax(input_prediction)
-print(input_pred_label)
-# Predictive System
-input_image_path = input("Path of the image to be predicted :")
-
-input_image = cv2.imread(input_image_path)
-
-cv2_imshow(input_image)
-
-Grayscale = cv2.cvtColor(input_image,cv2.COLOR_RGB2GRAY)
-
-input_image_resize = cv2.resize(Grayscale,(28,28))
-
-input_image_resize  = input_image_resize/255
-
-input_reshaped = np.reshape(input_image_resize,[1,28,28])
-
-input_prediction = model.predict(input_reshaped)
-
-input_pred_label = np.argmax(input_prediction)
-
-print("the Handwritten digit recognized as : ",input_pred_label)
-
-
-
 import gradio as gr
+from tensorflow import keras as k
+import numpy as np
 
-
-def predict_image(img):
-  img_3d=img.reshape(-1,28,28)
-  im_resize=img_3d/255.0
-  prediction=model.predict(im_resize)
-  pred=np.argmax(input_prediction)
-  return pred
-
-iface = gr.Interface(predict_image, inputs="sketchpad", outputs="label")
-
-iface.launch(debug='False')
+loaded_CNN = k.models.load_model('Digit_classification_model.h5') 
+
+def predict(img):
+    img_array = np.array(img)
+    img_array = img_array.reshape(1, 28, 28)
+    img_array = img_array/255
+    pred = loaded_CNN.predict(img_array)
+    print(pred)
+    return np.argmax(pred)
+
+iface = gr.Interface(predict, inputs = 'sketchpad',
+                     outputs = 'text',
+                     allow_flagging = 'never',
+                     description = 'Draw a Digit Below... (Draw in the centre for best results)')
+iface.launch(share = True, width = 500, height = 500)