| # import streamlit as st | |
| # import pandas as pd | |
| # import torch | |
| # import torch.nn as nn | |
| # import torch.optim as optim | |
| # from sklearn.metrics import r2_score | |
| # class RegressionModel2(nn.Module): | |
| # def __init__(self, input_dim2, hidden_dim2, output_dim2): | |
| # super(RegressionModel2, self).__init__() | |
| # self.fc1 = nn.Linear(input_dim2, hidden_dim2) | |
| # self.relu1 = nn.ReLU() | |
| # self.fc2 = nn.Linear(hidden_dim2, output_dim2) | |
| # self.batch_norm1 = nn.BatchNorm1d(hidden_dim2) | |
| # def forward(self, x2): | |
| # out = self.fc1(x2) | |
| # out = self.relu1(out) | |
| # out = self.batch_norm1(out) | |
| # out = self.fc2(out) | |
| # return out | |
| # # Load the saved model state dictionary | |
| # model = RegressionModel2(3, 32, 1) | |
| # model.load_state_dict(torch.load('model.pt')) | |
| # model.eval() # Set the model to evaluation mode | |
| # # Define a function to make predictions | |
| # def predict_astigmatism(age, axis, aca): | |
| # """ | |
| # This function takes three arguments (age, axis, aca) as input, | |
| # converts them to a tensor, makes a prediction using the loaded model, | |
| # and returns the predicted value. | |
| # """ | |
| # # Prepare the input data | |
| # data = torch.tensor([[age, axis, aca]], dtype=torch.float32) | |
| # # Make prediction | |
| # with torch.no_grad(): | |
| # prediction = model(data) | |
| # # Return the predicted value | |
| # return prediction.item() | |
| # def main(): | |
| # st.set_page_config(page_title='Astigmatism Prediction', page_icon=':eyeglasses:', layout='wide') | |
| # st.write('<style>.st-emotion-cache-1dp5vir.ezrtsby1 { display: none; }</style>', unsafe_allow_html=True) | |
| # st.write("""<style>.st-emotion-cache-czk5ss.e16jpq800 {display: none;}</style>""", unsafe_allow_html=True) | |
| # st.markdown( | |
| # """ | |
| # <style> | |
| # .navbar { | |
| # display: flex; | |
| # justify-content: space-between; | |
| # align-items: center; | |
| # background-color: #f2f2f2; | |
| # padding: 10px; | |
| # } | |
| # .logo img { | |
| # height: 50px; | |
| # } | |
| # .menu { | |
| # list-style-type: none; | |
| # display: flex; | |
| # } | |
| # .menu li { | |
| # margin-left: 20px; | |
| # } | |
| # .text-content { | |
| # margin-top: 50px; | |
| # text-align: center; | |
| # } | |
| # .button { | |
| # margin-top: 20px; | |
| # padding: 10px 20px; | |
| # font-size: 16px; | |
| # } | |
| # </style> | |
| # """, | |
| # unsafe_allow_html=True | |
| # ) | |
| # # st.markdown( | |
| # # """ | |
| # # <body> | |
| # # <header> | |
| # # <nav class="navbar"> | |
| # # <div class="logo"><img src="iol.png" alt="Image description"></div> | |
| # # <ul class="menu"> | |
| # # <li><a href="#">Home</a></li> | |
| # # <li><a href="#">About</a></li> | |
| # # <li><a href="#">Contact</a></li> | |
| # # </ul> | |
| # # </nav> | |
| # # <div class="text-content"> | |
| # # <h2>Enter Variables</h2> | |
| # # <br> | |
| # # </div> | |
| # # </header> | |
| # # </body> | |
| # # """, | |
| # # unsafe_allow_html=True | |
| # # ) | |
| # age = st.number_input('Enter Patient Age:', step=0.1) | |
| # aca_magnitude = st.number_input('Enter ACA Magnitude:', step=0.1) | |
| # aca_axis = st.number_input('Enter ACA Axis:', step=0.1) | |
| # if st.button('Predict!'): | |
| # astigmatism = predict_astigmatism(age, aca_axis, aca_magnitude) | |
| # st.success(f'Predicted Total Corneal Astigmatism: {astigmatism:.4f}') | |
| # if __name__ == '__main__': | |
| # main() | |
| import streamlit as st | |
| import pandas as pd | |
| import torch | |
| import torch.nn as nn | |
| import torch.optim as optim | |
| from sklearn.metrics import r2_score | |
| import math | |
| class RegressionModel2(nn.Module): | |
| def __init__(self, input_dim2, hidden_dim2, output_dim2): | |
| super(RegressionModel2, self).__init__() | |
| self.fc1 = nn.Linear(input_dim2, hidden_dim2) | |
| self.relu1 = nn.ReLU() | |
| self.fc2 = nn.Linear(hidden_dim2, output_dim2) | |
| self.batch_norm1 = nn.BatchNorm1d(hidden_dim2) | |
| def forward(self, x2): | |
| out = self.fc1(x2) | |
| out = self.relu1(out) | |
| out = self.batch_norm1(out) | |
| out = self.fc2(out) | |
| return out | |
| # Load the saved model state dictionary | |
| model = RegressionModel2(3, 32, 1) | |
| model.load_state_dict(torch.load('model.pt')) | |
| model.eval() # Set the model to evaluation mode | |
| def predict_astigmatism(age, axis, aca): | |
| """ | |
| This function takes three arguments (age, axis, aca) as input, | |
| converts them to a tensor, makes a prediction using the loaded model, | |
| and returns the predicted value. | |
| """ | |
| # Prepare the input data | |
| data = torch.tensor([[age, axis, aca]], dtype=torch.float32) | |
| # Make prediction | |
| with torch.no_grad(): | |
| prediction = model(data) | |
| # Return the predicted value | |
| return prediction.item() | |
| def predict_axis(aca_magnitude, aca_axis): | |
| # Convert axis to radians | |
| aca_axis_rad = math.radians(aca_axis) | |
| # Calculate X and Y components | |
| X = aca_magnitude * math.cos(2 * aca_axis_rad) | |
| Y = aca_magnitude * math.sin(2 * aca_axis_rad) | |
| # Calculate intermediate axis prediction | |
| Z = math.degrees(0.5 * math.atan2(Y, X)) | |
| # Determine final predicted axis | |
| if X > 0: | |
| if Y > 0: | |
| predicted_axis = Z | |
| else: | |
| predicted_axis = Z + 180 | |
| else: | |
| predicted_axis = Z + 90 | |
| # Ensure the axis is between 0 and 180 degrees | |
| predicted_axis = predicted_axis % 180 | |
| return predicted_axis | |
| def main(): | |
| st.set_page_config(page_title='Astigmatism Prediction', page_icon=':eyeglasses:', layout='wide') | |
| st.write('<style>.st-emotion-cache-1dp5vir.ezrtsby1 { display: none; }</style>', unsafe_allow_html=True) | |
| st.write("""<style>.st-emotion-cache-czk5ss.e16jpq800 {display: none;}</style>""", unsafe_allow_html=True) | |
| st.markdown( | |
| """ | |
| <style> | |
| .navbar { | |
| display: flex; | |
| justify-content: space-between; | |
| align-items: center; | |
| background-color: #f2f2f2; | |
| padding: 10px; | |
| } | |
| .logo img { | |
| height: 50px; | |
| } | |
| .menu { | |
| list-style-type: none; | |
| display: flex; | |
| } | |
| .menu li { | |
| margin-left: 20px; | |
| } | |
| .text-content { | |
| margin-top: 50px; | |
| text-align: center; | |
| } | |
| .button { | |
| margin-top: 20px; | |
| padding: 10px 20px; | |
| font-size: 16px; | |
| } | |
| </style> | |
| """, | |
| unsafe_allow_html=True | |
| ) | |
| st.title('Total Corneal Astigmatism Prediction') | |
| age = st.number_input('Enter Patient Age:', min_value=0.0, step=0.1) | |
| aca_magnitude = st.number_input('Enter ACA Magnitude:', min_value=0.0, step=0.1) | |
| aca_axis = st.number_input('Enter ACA Axis:', min_value=0.0, max_value=180.0, step=0.1) | |
| if st.button('Predict!'): | |
| # Predict magnitude | |
| tca_magnitude = predict_astigmatism(age, aca_axis, aca_magnitude) | |
| # Predict axis | |
| tca_axis = predict_axis(aca_magnitude, aca_axis) | |
| st.success(f'Predicted Total Corneal Astigmatism Magnitude: {tca_magnitude:.4f} D') | |
| st.success(f'Predicted Total Corneal Astigmatism Axis: {tca_axis:.2f}°') | |
| if __name__ == '__main__': | |
| main() |