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import os
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import keras
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from tensorflow.keras.models import load_model
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import streamlit as st
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import tensorflow as tf
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import numpy as np
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st.header('Dental Classification CNN Model')
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class_names = ['Calculus', 'Dental Caries', 'Gingivitis', 'Hypodontia', 'Tooth discoloration']
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model = load_model('model2.keras')
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def classify_images(image_path):
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input_image = tf.keras.utils.load_img(image_path, target_size=(256,256))
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input_image_array = tf.keras.utils.img_to_array(input_image)
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input_image_exp_dim = tf.expand_dims(input_image_array,0)
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predictions = model.predict(input_image_exp_dim)
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result = tf.nn.softmax(predictions[0])
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outcome = 'The Image belongs to ' + class_names[np.argmax(result)] + ' with a score of '+ str(np.max(result)*100)
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return outcome
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uploaded_file = st.file_uploader('Upload an Image')
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if uploaded_file is not None:
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with open(os.path.join('upload', uploaded_file.name), 'wb') as f:
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f.write(uploaded_file.getbuffer())
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st.image(uploaded_file, width = 200)
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st.markdown(classify_images(uploaded_file)) |
