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import streamlit as st |
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import tensorflow as tf |
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from tensorflow import keras |
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from PIL import Image |
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from tensorflow.keras.preprocessing import image |
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import io |
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from collections import Counter |
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import numpy as np |
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def load_image(): |
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uploaded_file = st.file_uploader(label='Pick an image to test') |
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if uploaded_file is not None: |
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image_data = uploaded_file.getvalue() |
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st.image(image_data) |
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def load_models(): |
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model_name = 'Model/model.h5' |
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model = tf.keras.models.load_model(model_name) |
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return model |
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def load_labels(): |
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with open('Oxford-102_Flower_dataset_labels.txt', 'r') as file: |
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data = file.read().splitlines() |
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flower_dict = dict(enumerate(data, 1)) |
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return flower_dict |
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def load_image(): |
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uploaded_file = st.file_uploader(label='Pick an image to test') |
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if uploaded_file is not None: |
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image_data = uploaded_file.getvalue() |
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st.image(image_data) |
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img = Image.open(io.BytesIO(image_data)) |
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img = img.resize((224,224)) |
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return img |
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else: |
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return None |
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def predict(model, categories, img): |
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img_array = tf.keras.preprocessing.image.img_to_array(img) |
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prediction = [img_array] |
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prediction_test = [1] |
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test_ds = tf.data.Dataset.from_tensor_slices((prediction, prediction_test)) |
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test_ds = test_ds.cache().batch(32).prefetch(buffer_size = tf.data.experimental.AUTOTUNE) |
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prediction = model.predict(test_ds) |
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prediction_dict = dict(enumerate(prediction.flatten(), 1)) |
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k = Counter(prediction_dict) |
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high = k.most_common(3) |
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percentages = [] |
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flowers = [] |
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for i in high: |
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key, value = i |
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flowers.append(categories[key]) |
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percentages.append(np.round(value*100, 2)) |
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return flowers, percentages |
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def main(): |
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st.title('Oxford 102 Flower CLassification Demo') |
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model = load_models() |
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categories = load_labels() |
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image = load_image() |
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result = st.button('Run on image') |
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if result: |
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st.write('Calculating results...') |
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flowers, percentages = predict(model, categories, image) |
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st.text(flowers) |
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st.text(percentages) |
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if __name__ == '__main__': |
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main() |
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