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import streamlit as st |
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import tensorflow as tf |
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from tensorflow.keras.applications.imagenet_utils import decode_predictions |
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import cv2 |
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from PIL import Image, ImageOps |
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import numpy as np |
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@st.cache(allow_output_mutation=True) |
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def load_model(): |
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model=tf.keras.models.load_model('keras.h5') |
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return model |
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with st.spinner('Model is being loaded..'): |
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model=load_model() |
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st.write(""" |
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# Image Classification |
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""" |
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) |
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file = st.file_uploader("Upload the image to be classified U0001F447", type=["jpg", "png"]) |
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st.set_option('deprecation.showfileUploaderEncoding', False) |
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def upload_predict(upload_image, model): |
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size = (224,224) |
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image = ImageOps.fit(upload_image, size, Image.ANTIALIAS) |
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image = np.asarray(image) |
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img = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) |
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img_resize = cv2.resize(img, dsize=(224, 224),interpolation=cv2.INTER_CUBIC) |
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image = np.asarray(img_resize, dtype=np.float32).reshape(1, 224, 224, 3) |
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image = (image / 127.5) - 1 |
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prediction = model.predict(image) |
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index = np.argmax(prediction) |
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classname = ['Glass', 'NoGlass'] |
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class_name = classname[index] |
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return class_name |
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if file is None: |
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st.text("Please upload an image file") |
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else: |
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image = Image.open(file) |
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st.image(image, use_column_width=True) |
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predictions = upload_predict(image, model) |
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image_class = str(predictions) |
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st.write("The image is classified as",image_class) |
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