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import streamlit as st |
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import pandas as pd |
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import torch |
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import torch.nn as nn |
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import torch.optim as optim |
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from sklearn.metrics import r2_score |
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class RegressionModel2(nn.Module): |
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def __init__(self, input_dim2, hidden_dim2, output_dim2): |
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super(RegressionModel2, self).__init__() |
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self.fc1 = nn.Linear(input_dim2, hidden_dim2) |
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self.relu1 = nn.ReLU() |
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self.fc2 = nn.Linear(hidden_dim2, output_dim2) |
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self.batch_norm1 = nn.BatchNorm1d(hidden_dim2) |
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def forward(self, x2): |
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out = self.fc1(x2) |
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out = self.relu1(out) |
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out = self.batch_norm1(out) |
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out = self.fc2(out) |
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return out |
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model = RegressionModel2(3, 32, 1) |
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model.load_state_dict(torch.load('model.pt')) |
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model.eval() |
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def predict_astigmatism(age, axis, aca): |
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""" |
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This function takes three arguments (age, axis, aca) as input, |
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converts them to a tensor, makes a prediction using the loaded model, |
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and returns the predicted value. |
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""" |
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data = torch.tensor([[age, axis, aca]], dtype=torch.float32) |
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with torch.no_grad(): |
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prediction = model(data) |
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return prediction.item() |
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def main(): |
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st.set_page_config(page_title='Astigmatism Prediction', page_icon=':eyeglasses:', layout='wide') |
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st.write('<style>.st-emotion-cache-1dp5vir.ezrtsby1 { display: none; }</style>', unsafe_allow_html=True) |
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st.write("""<style>.st-emotion-cache-czk5ss.e16jpq800 {display: none;}</style>""", unsafe_allow_html=True) |
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st.markdown( |
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""" |
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<style> |
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.navbar { |
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display: flex; |
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justify-content: space-between; |
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align-items: center; |
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background-color: #f2f2f2; |
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padding: 10px; |
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} |
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.logo img { |
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height: 50px; |
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} |
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.menu { |
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list-style-type: none; |
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display: flex; |
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} |
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.menu li { |
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margin-left: 20px; |
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} |
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.text-content { |
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margin-top: 50px; |
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text-align: center; |
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} |
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.button { |
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margin-top: 20px; |
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padding: 10px 20px; |
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font-size: 16px; |
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} |
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</style> |
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""", |
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unsafe_allow_html=True |
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) |
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age = st.number_input('Enter Patient Age:', step=0.1) |
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aca_magnitude = st.number_input('Enter ACA Magnitude:', step=0.1) |
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aca_axis = st.number_input('Enter ACA Axis:', step=0.1) |
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if st.button('Predict!'): |
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astigmatism = predict_astigmatism(age, aca_axis, aca_magnitude) |
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st.success(f'Predicted Total Corneal Astigmatism: {astigmatism:.4f}') |
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if __name__ == '__main__': |
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main() |
