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Jfink09 commited on
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e9fb974
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1 Parent(s): dccf1b6

Update app.py

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Files changed (1) hide show
  1. app.py +82 -38
app.py CHANGED
@@ -1,15 +1,17 @@
1
  import streamlit as st
 
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  import torch
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  import torch.nn as nn
 
 
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- # Define your model architecture here (same as before)
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  class RegressionModel2(nn.Module):
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  def __init__(self, input_dim2, hidden_dim2, output_dim2):
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  super(RegressionModel2, self).__init__()
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  self.fc1 = nn.Linear(input_dim2, hidden_dim2)
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- self.relu1 = nn.ReLU() # ReLU activation function
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  self.fc2 = nn.Linear(hidden_dim2, output_dim2)
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- self.batch_norm1 = nn.BatchNorm1d(hidden_dim2) # Batch normalization
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  def forward(self, x2):
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  out = self.fc1(x2)
@@ -18,39 +20,81 @@ class RegressionModel2(nn.Module):
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  out = self.fc2(out)
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  return out
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- input_dim2 = X2_train.shape[1] # change to [1] for pentacam dataset X_train.shape[1]
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- hidden_dim2 = 32 # Was 16
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- output_dim2 = 1
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-
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- # Load your saved model state dictionary (assuming 'model.pt' is uploaded)
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- model2 = RegressionModel2(input_dim2, hidden_dim2, output_dim2)
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  model2.load_state_dict(torch.load('model.pt'))
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- model2.eval() # Set the model to evaluation mode
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-
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- def predict(age, aca, axis):
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- """
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- This function takes three arguments (age, axis, aca) as input,
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- prepares the data, makes a prediction using the loaded model,
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- and returns the predicted value.
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- """
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- # Prepare the input data
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- data = torch.tensor([[age, aca, axis]], dtype=torch.float32)
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-
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- # Make prediction
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- with torch.no_grad():
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- prediction = model2(data)
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-
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- # Return the predicted value
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- return prediction.item()
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-
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- # Streamlit App
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- st.title("Astigmatism Prediction App")
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- st.write("Enter the patient's information:")
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-
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- age = st.number_input("Age", min_value=0)
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- aca = st.number_input("ACA Magnitude", min_value=0)
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- axis = st.number_input("ACA Axis", min_value=0)
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-
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- if st.button("Predict"):
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- predicted_value = predict(age, aca, axis)
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- st.write(f"Predicted Astigmatism Value: {predicted_value}")
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  import streamlit as st
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+ import pandas as pd
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  import torch
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  import torch.nn as nn
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+ import torch.optim as optim
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+ from sklearn.metrics import r2_score
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  class RegressionModel2(nn.Module):
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  def __init__(self, input_dim2, hidden_dim2, output_dim2):
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  super(RegressionModel2, self).__init__()
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  self.fc1 = nn.Linear(input_dim2, hidden_dim2)
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+ self.relu1 = nn.ReLU()
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  self.fc2 = nn.Linear(hidden_dim2, output_dim2)
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+ self.batch_norm1 = nn.BatchNorm1d(hidden_dim2)
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  def forward(self, x2):
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  out = self.fc1(x2)
 
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  out = self.fc2(out)
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  return out
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+ # Load the trained model
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+ model2 = RegressionModel2(3, 32, 1)
 
 
 
 
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  model2.load_state_dict(torch.load('model.pt'))
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+ model2.eval()
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+
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+ def predict_astigmatism(age, aca_magnitude, aca_axis):
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+ input_data = torch.tensor([[age, aca_magnitude, aca_axis]], dtype=torch.float32)
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+ output = model2(input_data)
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+ return output.item()
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+
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+ def main():
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+ st.set_page_config(page_title='Astigmatism Prediction', page_icon=':eyeglasses:', layout='wide')
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+ st.markdown(
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+ """
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+ <style>
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+ .navbar {
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+ display: flex;
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+ justify-content: space-between;
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+ align-items: center;
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+ background-color: #f2f2f2;
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+ padding: 10px;
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+ }
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+ .logo img {
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+ height: 50px;
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+ }
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+ .menu {
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+ list-style-type: none;
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+ display: flex;
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+ }
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+ .menu li {
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+ margin-left: 20px;
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+ }
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+ .text-content {
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+ margin-top: 50px;
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+ text-align: center;
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+ }
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+ .button {
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+ margin-top: 20px;
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+ padding: 10px 20px;
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+ font-size: 16px;
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+ }
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+ </style>
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+ """,
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+ unsafe_allow_html=True
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+ )
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+
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+ st.markdown(
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+ """
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+ <body>
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+ <header>
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+ <nav class="navbar">
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+ <div class="logo"><img src="iol.png" alt="Image description"></div>
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+ <ul class="menu">
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+ <li><a href="#">Home</a></li>
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+ <li><a href="#">About</a></li>
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+ <li><a href="#">Contact</a></li>
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+ </ul>
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+ </nav>
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+ <div class="text-content">
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+ <h2>Enter Variables</h2>
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+ <br>
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+ </div>
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+ </header>
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+ </body>
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+ """,
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+ unsafe_allow_html=True
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+ )
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+
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+ age = st.number_input('Enter Patient Age:', min_value=0, step=1)
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+ aca_magnitude = st.number_input('Enter ACA Magnitude:', min_value=0.0, step=0.1)
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+ aca_axis = st.number_input('Enter ACA Axis:', min_value=0, max_value=180, step=1)
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+
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+ if st.button('Predict!'):
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+ astigmatism = predict_astigmatism(age, aca_magnitude, aca_axis)
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+ st.success(f'Predicted Astigmatism: {astigmatism:.4f}')
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+
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+ if __name__ == '__main__':
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+ main()