Update app.py

app.py

@@ -1,46 +1,46 @@
-# 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()
+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')