File size: 5,770 Bytes
b8d173a afe13a1 b8d173a afe13a1 b8d173a afe13a1 b8d173a afe13a1 b8d173a afe13a1 b8d173a afe13a1 b8d173a afe13a1 e9fb974 580ce8d 22f0baf e9fb974 afe13a1 e9fb974 afe13a1 e9fb974 afe13a1 e9fb974 afe13a1 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 |
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()
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;
}
.error {
color: red;
font-weight: bold;
}
</style>
""",
unsafe_allow_html=True
)
age = st.number_input('Enter Patient Age:', min_value=0.0, max_value=120.0, step=0.1)
if age < 18 or age > 90:
st.markdown('<p class="error">Error: Age must be between 18 and 90.</p>', unsafe_allow_html=True)
aca_magnitude = st.number_input('Enter ACA Magnitude:', min_value=0.0, max_value=20.0, step=0.1)
if aca_magnitude < 0 or aca_magnitude > 10:
st.markdown('<p class="error">Error: ACA Magnitude must be between 0 and 10.</p>', unsafe_allow_html=True)
aca_axis = st.number_input('Enter ACA Axis:', min_value=0.0, max_value=180.0, step=0.1)
if aca_axis < 0 or aca_axis > 180:
st.markdown('<p class="error">Error: ACA Axis must be between 0 and 180.</p>', unsafe_allow_html=True)
if st.button('Predict!'):
if 18 <= age <= 90 and 0 <= aca_magnitude <= 10 and 0 <= aca_axis <= 180:
astigmatism = predict_astigmatism(age, aca_axis, aca_magnitude)
st.success(f'Predicted Total Corneal Astigmatism: {astigmatism:.4f}')
else:
st.error('Please correct the input errors before predicting.')
if __name__ == '__main__':
main() |