Update app3.py

app3.py

@@ -0,0 +1,56 @@
+from keras.models import load_model
+import numpy as np # linear algebra
+import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
+import matplotlib.pyplot as plt
+from numpy import load 
+import gradio as gr
+
+# from keras.datasets import mnist
+import keras.utils.np_utils as ku
+import keras.models as models
+import keras.layers as layers
+from keras import regularizers
+import numpy.random as nr
+
+# save numpy array as npy file
+from numpy import asarray
+from numpy import save
+# save to npy file
+
+
+import keras
+from keras.layers import Dropout
+
+from keras.preprocessing.image import ImageDataGenerator
+from tensorflow.keras.optimizers import RMSprop,Adam
+from tensorflow.keras.layers import BatchNormalization
+from sklearn.metrics import confusion_matrix
+
+import warnings
+warnings.simplefilter(action='ignore')
+from PIL import Image, ImageFilter
+
+# %matplotlib inline
+
+from tensorflow.keras.preprocessing.image import ImageDataGenerator
+
+nn = load_model('my_model-2.h5')
+def predict_image(img):
+  print("Digit Recognizer")
+  img_3d=img.reshape(-1,28,28)
+  im_resize=img_3d/255.0
+  prediction=nn.predict(im_resize).tolist()[0]
+  return {str(i):prediction[i] for i in range(10)}
+
+'''
+with gr.Blocks() as demo:
+     gr.Title("Digit Recognizer")
+     ac_inputs=gr.Sketchpad()
+     ac_outputs=gr.outputs.Label(num_top_classes=3)
+     greet_btn = gr.Button("Greet")
+     gr.interface(fn=predict_image, inputs="sketchpad",outputs=gr.outputs.Label(num_top_classes=3))
+'''
+label=gr.outputs.Label(num_top_classes=3)
+iface=gr.Interface(predict_image, inputs="sketchpad",outputs=label,title=f"Digit Recognizer",allow_flagging='manual',description="Note:Draw Digits from 0-9 and Try to Draw the Digit in the center for better accuracy")
+
+iface.launch(debug='True')