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# 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() |