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('', unsafe_allow_html=True)
# st.write("""""", unsafe_allow_html=True)
# st.markdown(
# """
#
# """,
# unsafe_allow_html=True
# )
# # st.markdown(
# # """
# #
# #
# #
# # """,
# # 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('', unsafe_allow_html=True)
st.write("""""", unsafe_allow_html=True)
st.markdown(
"""
""",
unsafe_allow_html=True
)
# Use session state to store input values
if 'age' not in st.session_state:
st.session_state.age = None
if 'aca_magnitude' not in st.session_state:
st.session_state.aca_magnitude = None
if 'aca_axis' not in st.session_state:
st.session_state.aca_axis = None
# Age input
age = st.number_input('Enter Patient Age (15-90 Years):', min_value=18.0, max_value=90.0, step=0.1, value=st.session_state.age)
if age != st.session_state.age:
st.session_state.age = age
if age is not None and (age < 18 or age > 90):
st.markdown('Error: Age must be between 18 and 90.
', unsafe_allow_html=True)
# ACA Magnitude input
aca_magnitude = st.number_input('Enter ACA Magnitude (0-10 Diopters):', min_value=0.0, max_value=10.0, step=0.1, value=st.session_state.aca_magnitude)
if aca_magnitude != st.session_state.aca_magnitude:
st.session_state.aca_magnitude = aca_magnitude
if aca_magnitude is not None and (aca_magnitude < 0 or aca_magnitude > 10):
st.markdown('Error: ACA Magnitude must be between 0 and 10.
', unsafe_allow_html=True)
# ACA Axis input
aca_axis = st.number_input('Enter ACA Axis (0-180 Degrees):', min_value=0.0, max_value=180.0, step=0.1, value=st.session_state.aca_axis)
if aca_axis != st.session_state.aca_axis:
st.session_state.aca_axis = aca_axis
if aca_axis is not None and (aca_axis < 0 or aca_axis > 180):
st.markdown('Error: ACA Axis must be between 0 and 180.
', unsafe_allow_html=True)
if st.button('Predict!'):
if age is not None and aca_magnitude is not None and aca_axis is not None:
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.')
else:
st.error('Please fill in all fields before predicting.')
if __name__ == '__main__':
main()