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import tensorflow as tf |
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import streamlit as st |
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import pandas as pd |
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import numpy as np |
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import matplotlib.pyplot as plt |
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from PIL import Image |
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from sklearn.metrics import classification_report |
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st.set_option('deprecation.showfileUploaderEncoding', False) |
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@st.cache_resource() |
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def load_model(): |
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model = tf.keras.models.load_model('model.h5') |
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return model |
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def predict_class(image, model): |
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image = tf.cast(image, tf.float32) |
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image = tf.image.resize(image, [224, 224]) |
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image = tf.expand_dims(image, axis=0) |
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prediction = model.predict(image) |
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return prediction |
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model = load_model() |
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st.title("Food Vision Classification") |
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file = st.file_uploader("Upload an iamge of food", type=["jpg", "jpeg", "png"]) |
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col1, col2 = st.columns(2) |
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col1.subheader("Image to be Predicted") |
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col2.subheader("Results") |
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if file is None: |
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st.text("Waiting for upload....") |
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else: |
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test_image = Image.open(file) |
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slot = st.empty() |
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col1.image(test_image, caption="Input Image", width=300) |
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slot.text('Running inference.....') |
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pred = tf.squeeze(predict_class(test_image, model)) |
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class_names = pd.read_csv("class_names.txt") |
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class_names = np.array(class_names["labels"]) |
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result = class_names[tf.argmax(pred)] |
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slot.text('Done') |
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output = 'The image is a ' + result |
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col2.success(output) |
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st.subheader("Metrics") |
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