gopiashokan
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Upload 3 files
Browse files- app.py +69 -0
- model/model.h5 +3 -0
- requirements.txt +5 -0
app.py
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import numpy as np
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import PIL.Image as Image
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import tensorflow as tf
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import streamlit as st
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from streamlit_extras.add_vertical_space import add_vertical_space
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from warnings import filterwarnings
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filterwarnings('ignore')
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def streamlit_config():
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# page configuration
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st.set_page_config(page_title='Classification', layout='centered')
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# page header transparent color
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page_background_color = """
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<style>
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[data-testid="stHeader"]
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{
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background: rgba(0,0,0,0);
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}
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</style>
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"""
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st.markdown(page_background_color, unsafe_allow_html=True)
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# title and position
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st.markdown(f'<h1 style="text-align: center;">Potato Disease Classification</h1>',
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unsafe_allow_html=True)
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add_vertical_space(4)
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# Streamlit Configuration Setup
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streamlit_config()
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def prediction(image_path, class_names=['Potato___Early_blight', 'Potato___Late_blight', 'Potato___healthy']):
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img = Image.open(image_path)
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img_resized = img.resize((256,256))
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img_array = tf.keras.preprocessing.image.img_to_array(img_resized)
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img_array = np.expand_dims(img_array, axis=0)
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model = tf.keras.models.load_model(r'model\model.h5')
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prediction = model.predict(img_array)
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predicted_class = class_names[np.argmax(prediction)]
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confidence = round(np.max(prediction)*100, 2)
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add_vertical_space(1)
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st.markdown(f'<h4 style="color: orange;">Predicted Class : {predicted_class}<br>Confident : {confidence}%</h3>',
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unsafe_allow_html=True)
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add_vertical_space(1)
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st.image(img.resize((400,300)))
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col1,col2,col3 = st.columns([0.1,0.9,0.1])
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with col2:
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input_image = st.file_uploader(label='Upload the Image', type=['jpg', 'jpeg', 'png'])
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if input_image is not None:
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col1,col2,col3 = st.columns([0.2,0.8,0.2])
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with col2:
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prediction(input_image)
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model/model.h5
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version https://git-lfs.github.com/spec/v1
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oid sha256:8e69552fb43cb1ac43301ca71b19f20e7f7ff4fe2ff13ef308feaf93b2fb45ff
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size 2286592
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requirements.txt
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numpy
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pillow
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tensorflow
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streamlit
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streamlit_extras
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