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| import streamlit as st | |
| import torch | |
| import torch.nn as nn | |
| 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 dictionaries | |
| def load_models(): | |
| model_j0 = RegressionModel2(3, 32, 1) | |
| model_j0.load_state_dict(torch.load('j0_model-2.pt')) | |
| model_j0.eval() | |
| model_j45 = RegressionModel2(3, 32, 1) | |
| model_j45.load_state_dict(torch.load('j45_model-2.pt')) | |
| model_j45.eval() | |
| return model_j0, model_j45 | |
| model_j0, model_j45 = load_models() | |
| def calculate_initial_j0_j45(magnitude, axis_deg): | |
| """Calculate initial J0 and J45 from magnitude and axis (in degrees).""" | |
| axis_rad = math.radians(axis_deg) | |
| j0 = magnitude * math.cos(2 * axis_rad) | |
| j45 = magnitude * math.sin(2 * axis_rad) | |
| return j0, j45 | |
| def predict_new_j0_j45(age, aca_magnitude, aca_axis_deg): | |
| """Predict new J0 and J45 using the loaded models.""" | |
| initial_j0, initial_j45 = calculate_initial_j0_j45(aca_magnitude, aca_axis_deg) | |
| input_data_j0 = torch.tensor([[age, aca_axis_deg, initial_j0]], dtype=torch.float32) | |
| input_data_j45 = torch.tensor([[age, aca_axis_deg, initial_j45]], dtype=torch.float32) | |
| with torch.no_grad(): | |
| new_j0 = model_j0(input_data_j0).item() | |
| new_j45 = model_j45(input_data_j45).item() | |
| return new_j0, new_j45 | |
| def main(): | |
| st.title('Total Corneal Astigmatism Prediction') | |
| # User input fields | |
| age = st.number_input('Enter Patient Age (18-90 Years):', min_value=18.0, max_value=90.0, value=58.0, step=1.0) | |
| aca_magnitude = st.number_input('Enter ACA Magnitude (0-10 Diopters):', min_value=0.0, max_value=10.0, value=2.3, step=0.1) | |
| aca_axis = st.number_input('Enter ACA Axis (0-180 Degrees):', min_value=0.0, max_value=180.0, value=97.7, step=0.1) | |
| if st.button('Predict'): | |
| # Calculate initial J0 and J45 | |
| initial_j0, initial_j45 = calculate_initial_j0_j45(aca_magnitude, aca_axis) | |
| # Make prediction | |
| new_j0, new_j45 = predict_new_j0_j45(age, aca_magnitude, aca_axis) | |
| # Calculate TCA magnitude and axis | |
| tca_magnitude = math.sqrt(new_j0**2 + new_j45**2) | |
| tca_axis = 0.5 * math.degrees(math.atan2(new_j45, new_j0)) | |
| if tca_axis < 0: | |
| tca_axis += 180 | |
| # Display results | |
| st.subheader('Prediction Results') | |
| col1, col2 = st.columns(2) | |
| with col1: | |
| st.write(f"Initial J0: {initial_j0:.2f}") | |
| st.write(f"Initial J45: {initial_j45:.2f}") | |
| st.write(f"Predicted J0: {new_j0:.2f}") | |
| st.write(f"Predicted J45: {new_j45:.2f}") | |
| with col2: | |
| st.write(f"Predicted TCA Magnitude: {tca_magnitude:.2f} D") | |
| st.write(f"Predicted TCA Axis: {tca_axis:.1f}°") | |
| # Optional: Display input tensors and raw outputs for verification | |
| if st.checkbox('Show detailed model inputs and outputs'): | |
| st.subheader('Model Details') | |
| st.write("Input tensor for J0:", torch.tensor([[age, aca_axis, initial_j0]], dtype=torch.float32)) | |
| st.write("Input tensor for J45:", torch.tensor([[age, aca_axis, initial_j45]], dtype=torch.float32)) | |
| st.write("Raw J0 output:", new_j0) | |
| st.write("Raw J45 output:", new_j45) | |
| if __name__ == '__main__': | |
| main() |