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from PIL import Image |
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import tensorflow as tf |
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import streamlit as st |
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from deep_learning_pipeline import PredictionPipeline |
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st.title('Malaria Infected Cell Detection using X-ray Images') |
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st.write('This Project is built using CNN (Convolutional Neural Networks) Transfer Learning model that helps to predict whether the given X-ray image of the cell is Malaria Infected or Healthy!!') |
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st.write('') |
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st.write('') |
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uploaded_file = st.file_uploader("Upload an image", type=["png", "jpg", "jpeg"]) |
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if uploaded_file is not None: |
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with st.container(): |
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col1, col2 = st.columns([3, 2]) |
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col1.image(uploaded_file, caption="Uploaded Image", use_column_width=True) |
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if st.button('Predict!!'): |
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pipeline = PredictionPipeline() |
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resnet152v2_y_pred, resnet152v2_y_probs = pipeline.predict(input_img=uploaded_file) |
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col2.balloons() |
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if resnet152v2_y_pred[0][0] == 1: |
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col2.subheader('ResNET 152V2 model: ') |
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col2.success(f'{pipeline.CLASS_NAMES[1]}') |
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r_acc = '{:.2f}'.format(100*(resnet152v2_y_probs[0][0])) |
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col2.success(f'Accuracy: {r_acc}%') |
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elif resnet152v2_y_pred[0][0] == 0: |
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col2.subheader('ResNET 152V2 model: ') |
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col2.success(f'{pipeline.CLASS_NAMES[0]}') |
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r_acc = '{:.2f}'.format(100*(1-resnet152v2_y_probs[0][0])) |
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col2.success(f'Accuracy: {r_acc}%') |
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elif resnet152v2_y_pred[[0]] == -1: |
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col2.error('Error!! Model needs shape (224, 224, 3), but your image is of shape (224, 224,4)') |
