first commit

app.py

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+import tensorflow as tf
+from tensorflow import keras
+from tensorflow.keras import Sequential
+from tensorflow.keras.layers import Dense, Flatten
+from tensorflow.keras import layers
+import matplotlib.pyplot as plt
+import gradio as gr
+import numpy as np
+import pandas as pd
+from PIL import Image as im
+import PIL 
+#%matplotlib inline
+num_classes = 10
+input_shape = (28, 28, 1)
+
+objt=tf.keras.datasets.mnist
+(X_train, y_train), (X_test,y_test)=objt.load_data()
+
+
+# X_train = X_train.astype("float32") / 255
+# X_test = X_test.astype("float32") / 255
+# # Make sure images have shape (28, 28, 1)
+# X_train = np.expand_dims(X_train, -1)
+# X_test = np.expand_dims(X_test, -1)
+# print("x_train shape:", X_train.shape)
+# print(X_train.shape[0], "train samples")
+# print(X_test.shape[0], "test samples")
+
+
+# # convert class vectors to binary class matrices
+# y_train = keras.utils.to_categorical(y_train, num_classes)
+# y_test = keras.utils.to_categorical(y_test, num_classes)
+
+# X_new=np.concatenate((X_train, X_test))
+# y_new=np.concatenate((y_train, y_test))
+# print(X_train.shape)
+# print(X_new.shape)
+# print(y_new.shape)
+
+# print(y_train)
+
+# model = keras.Sequential(
+#     [
+#         keras.Input(shape=input_shape),
+#         layers.Conv2D(32, kernel_size=(3, 3), activation="relu"),
+#         layers.MaxPooling2D(pool_size=(2, 2)),
+#         layers.Conv2D(64, kernel_size=(3, 3), activation="relu"),
+#         layers.MaxPooling2D(pool_size=(2, 2)),
+#         layers.Flatten(),
+#         layers.Dropout(0.5),
+#         layers.Dense(num_classes, activation="softmax"),
+#     ]
+# )
+
+# model.summary()
+
+# batch_size = 128
+# epochs = 15
+
+# model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"])
+
+# model.fit(X_new, y_new, batch_size=batch_size, epochs=epochs, validation_split=0.1)
+# model.save("keras_digit_test_include.h5")
+
+# score = model.evaluate(X_test, y_test, verbose=0)
+# print("Test loss:", score[0])
+# print("Test accuracy:", score[1])
+
+# loaded_model = keras.models.load_model('keras_digit_accurate.h5')
+# score = loaded_model.evaluate(X_test, y_test, verbose=0)
+# print("Test loss:", score[0])
+# print("Test accuracy:", score[1])
+
+#................................................................................................
+
+
+# for i in range(9):
+#     plt.subplot(330+1+i)
+#     plt.imshow(X_train[i])
+#     plt.show()
+
+# X_train=X_train/255.0
+# X_test=X_test/255.0
+
+# model=tf.keras.models.Sequential([Flatten(input_shape=(28,28)),
+                                        
+#                                         Dense(650,activation='relu'),
+                                        
+#                                         Dense(450,activation='relu'),
+                                        
+#                                         Dense(250,activation='relu'),
+                                        
+#                                         Dense(150,activation='relu'),
+                                        
+#                                         Dense(10,activation=tf.nn.softmax)])
+
+# model.compile(optimizer='adam',
+#               loss='sparse_categorical_crossentropy',
+#               metrics=['accuracy'])
+# model.fit(X_train,y_train, epochs=10)
+# model.save("keras_digit_temp.h5")
+# test=X_test[0].reshape(-1,28,28)
+# predicted=model.predict(test)
+# print(predicted)
+
+#count=0
+
+def predict_digit(img):
+    if img is not None:
+
+        loaded_model = keras.models.load_model('keras_digit_test_include.h5')
+
+        
+        #img_data = im.fromarray(img)
+        #img_data.save(f"image1.jpg")
+        #count=count+1
+        img_3d=img.reshape(-1,28,28)
+        img_resized=img_3d/255.0
+        pred_prob=loaded_model.predict(img_resized)
+
+        pred_prob=pred_prob*100
+
+        print((pred_prob))
+        
+
+        simple = pd.DataFrame(
+        {
+        "a": ["0", "1", "2", "3", "4", "5", "6", "7", "8","9"],
+        "b": pred_prob[0], 
+        }
+        )
+
+        predicted_val=np.argmax(pred_prob)
+        return int(predicted_val), gr.BarPlot.update(
+            simple,
+            x="a",
+            y="b",
+            x_title="Digits",
+            y_title="Identification Probabilities",
+            title="Identification Probability",
+            tooltip=["a", "b"],
+            vertical=False,
+            y_lim=[0, 100],
+        )
+        
+    else:
+        simple_empty = pd.DataFrame(
+        {
+        "a": ["0", "1", "2", "3", "4", "5", "6", "7", "8","9"],
+        "b": [0,0,0,0,0,0,0,0,0,0],
+        }
+        )
+
+        return " ", gr.BarPlot.update(
+            simple_empty,
+            x="a",
+            y="b",
+            x_title="Digits",
+            y_title="Identification Probabilities",
+            title="Identification Probability",
+            tooltip=["a", "b"],
+            vertical=False,
+            y_lim=[0, 100],
+            
+        )
+        
+
+# iface=gr.Interface(prdict_digit, inputs='sketchpad', outputs=['label', gr.Slider(0,100, label='Probably 0'), gr.Slider(0,100, label='Probably 1')] ).launch()
+
+# iface.launch(debug='true')
+
+css='''
+#title_head{
+text-align: center;
+text-weight: bold;
+text-size:30px;
+}
+#name_head{
+text-align: center;
+}
+'''
+
+with gr.Blocks(css=css) as demo:
+    with gr.Row():
+        with gr.Column():
+            gr.Markdown("<h1>Digit Identifier</h1>", elem_id='title_head')
+            gr.Markdown("<h2>Cyperts Project</h2>", elem_id="name_head")
+    with gr.Row():
+        with gr.Column():
+            with gr.Row():
+                skch=gr.Sketchpad()
+            with gr.Row():
+                with gr.Column():
+                    clear=gr.ClearButton(skch)
+                with gr.Column():
+                    btn=gr.Button("Identify")
+                    
+        with gr.Column():
+            gr.Markdown("Identified digit")
+            label=gr.Label("")
+            gr.Markdown("Other possible values")
+            bar = gr.BarPlot()
+    btn.click(predict_digit,inputs=skch,outputs=[label,bar])
+
+           
+        
+    
+demo.launch(debug=True)
+    
+
+

keras_digit_test_include.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:8f25136fc7407f27681f3d285251c4e19909874604f220786505dd887607168c
+size 458272

requirements.txt

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+tensorflow
+keras
+
+scikit_learn<1.3.0
+seaborn==0.12.2
+
+#torchvision<0.15.2
+gradio
+
+#streamlit==1.26.0