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Update app.py
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app.py
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import
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import tensorflow as tf
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from
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import gradio as gr
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# Load
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"""Load and preprocess the MNIST dataset."""
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(X_train, y_train), (X_test, y_test) = tf.keras.datasets.mnist.load_data()
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X_train = X_train.astype("float32") / 255
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X_test = X_test.astype("float32") / 255
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X_train = X_train.reshape(-1, 28, 28, 1)
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X_test = X_test.reshape(-1, 28, 28, 1)
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return (X_train, y_train), (X_test, y_test)
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# Build the CNN model
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def build_model(input_shape, num_classes):
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"""Build the CNN model."""
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inputs = keras.layers.Input(input_shape)
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x = keras.layers.Conv2D(28, kernel_size=(3, 3), activation='relu')(inputs)
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x = keras.layers.MaxPooling2D(pool_size=(2, 2))(x)
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x = keras.layers.Flatten()(x)
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x = keras.layers.Dense(128, activation='relu')(x)
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outputs = keras.layers.Dense(num_classes, activation='softmax')(x)
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return keras.models.Model(inputs=inputs, outputs=outputs)
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# Preprocess input for prediction
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def preprocess_image(image):
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"""Resize and normalize the input image for prediction."""
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image = np.array(image.convert('L')) # Convert to grayscale
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image = image.astype("float32") / 255 # Normalize
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image = image.reshape(1, 28, 28, 1) # Reshape to model's input
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return image
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# Predict digit
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def predict_digit(image):
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"""Predict the digit in the uploaded image."""
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processed_image = preprocess_image(image)
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prediction = model.predict(processed_image)
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class_id = np.argmax(prediction)
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confidence = prediction[0][class_id]
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label = classes_names[class_id]
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results = {name: float(prediction[0][i]) for i, name in enumerate(classes_names)}
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return label, results
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if __name__ == "__main__":
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# Parameters
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classes_names = ["zero", "one", "two", "three", "four", "five", "six", "seven", "eight", "nine"]
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input_shape = (28, 28, 1)
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num_classes = len(classes_names)
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# Load data
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(X_train, y_train), (X_test, y_test) = load_data()
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# Build and train model
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model = build_model(input_shape, num_classes)
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model.compile(optimizer="adam", loss="sparse_categorical_crossentropy", metrics=["accuracy"])
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print("Training model...")
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model.fit(X_train, y_train, epochs=3, batch_size=64) # Quick training for demonstration
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)
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gr.outputs.Textbox(label="Predicted Digit"),
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gr.outputs.Label(num_top_classes=10, label="Prediction Confidence"),
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],
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title=title,
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description=description,
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examples=examples,
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live=True,
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)
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print("Launching Gradio interface...")
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interface.launch()
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import os
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os.system("pip uninstall -y gradio")
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os.system("pip install gradio==3.50.2")
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import tensorflow as tf
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from matplotlib import pyplot as plt
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import numpy as np
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import gradio as gr
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# Load the model
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model = tf.keras.models.load_model('number_recognition_model_colab.keras')
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def recognize_digit(image):
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if image is not None:
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image = image.reshape((1,28,28,1)).astype('float32')/255
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prediction = model.predict(image)
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return {str(i) : float(prediction[0][i]) for i in range(10)}
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else:
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return ''
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iface = gr.Interface(
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fn = recognize_digit,
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inputs=gr.Image(shape=(28,28),image_mode = 'L',invert_colors=True, source = 'canvas'),
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outputs=gr.Label(top_num_classes=3))
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iface.launch()
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