Create app.py

app.py

@@ -0,0 +1,152 @@
+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 dictionaries
+model_x = RegressionModel2(3, 32, 1)
+model_x.load_state_dict(torch.load('j0_model.pt'))
+model_x.eval()
+
+model_y = RegressionModel2(3, 32, 1)
+model_y.load_state_dict(torch.load('j45_model.pt'))
+model_y.eval()
+
+def predict_components(age, axis, aca):
+    """
+    This function predicts both x and y components using the loaded models.
+    """
+    data = torch.tensor([[age, axis, aca]], dtype=torch.float32)
+    
+    with torch.no_grad():
+        x_pred = model_x(data).item()
+        y_pred = model_y(data).item()
+    
+    return x_pred, y_pred
+
+def calculate_magnitude_and_axis(x, y):
+    """
+    Calculate magnitude and axis from x and y components.
+    """
+    magnitude = math.sqrt(x**2 + y**2)
+    
+    # Calculate intermediate axis
+    intermediate_axis = math.degrees(0.5 * math.atan2(y, x))
+    
+    # Calculate final axis
+    if x > 0:
+        if y > 0:
+            final_axis = intermediate_axis
+        else:
+            final_axis = intermediate_axis + 180
+    else:
+        final_axis = intermediate_axis + 90
+    
+    # Ensure axis is between 0 and 180
+    final_axis = final_axis % 180
+    
+    return magnitude, final_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;
+        }
+        .error {
+            color: red;
+            font-weight: bold;
+        }
+        </style>
+        """,
+        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('<p class="error">Error: Age must be between 18 and 90.</p>', 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('<p class="error">Error: ACA Magnitude must be between 0 and 10.</p>', 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('<p class="error">Error: ACA Axis must be between 0 and 180.</p>', 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:
+                x_pred, y_pred = predict_components(age, aca_axis, aca_magnitude)
+                predicted_magnitude, predicted_axis = calculate_magnitude_and_axis(x_pred, y_pred)
+                st.success(f'Predicted Total Corneal Astigmatism Magnitude: {predicted_magnitude:.4f} D')
+                st.success(f'Predicted Total Corneal Astigmatism Axis: {predicted_axis:.1f}°')
+            else:
+                st.error('Please correct the input errors before predicting.')
+        else:
+            st.error('Please fill in all fields before predicting.')
+
+if __name__ == '__main__':
+    main()