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Jfink09 commited on Jul 31, 2024

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afe13a1

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1 Parent(s): 8a7bb30

Update app.py

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Files changed (1) hide show

app.py +149 -71

app.py CHANGED Viewed

@@ -1,46 +1,131 @@
-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')
@@ -75,40 +160,33 @@ def main():
             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
+# 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')
             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()