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Jfink09 commited on Jul 31, 2024

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8a7bb30

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1 Parent(s): 9cf86b7

Update app.py

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app.py +42 -171

app.py CHANGED Viewed

@@ -1,127 +1,9 @@
-# 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):
@@ -143,52 +25,27 @@ 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>
@@ -223,21 +80,35 @@ def main():
         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()

 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):
 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>
         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()