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import streamlit as st
import torch
import torch.nn as nn
# Define your model architecture here (same as before)
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() # ReLU activation function
self.fc2 = nn.Linear(hidden_dim2, output_dim2)
self.batch_norm1 = nn.BatchNorm1d(hidden_dim2) # Batch normalization
def forward(self, x2):
out = self.fc1(x2)
out = self.relu1(out)
out = self.batch_norm1(out)
out = self.fc2(out)
return out
# Load your saved model state dictionary (assuming 'model.pt' is uploaded)
model2 = RegressionModel2(input_dim2, hidden_dim2, output_dim2)
model2.load_state_dict(torch.load('model.pt'))
model2.eval() # Set the model to evaluation mode
def predict(age, aca, axis):
"""
This function takes three arguments (age, axis, aca) as input,
prepares the data, makes a prediction using the loaded model,
and returns the predicted value.
"""
# Prepare the input data
data = torch.tensor([[age, aca, axis]], dtype=torch.float32)
# Make prediction
with torch.no_grad():
prediction = model2(data)
# Return the predicted value
return prediction.item()
# Streamlit App
st.title("Astigmatism Prediction App")
st.write("Enter the patient's information:")
age = st.number_input("Age", min_value=0)
aca = st.number_input("ACA Magnitude", min_value=0)
axis = st.number_input("ACA Axis", min_value=0)
if st.button("Predict"):
predicted_value = predict(age, aca, axis)
st.write(f"Predicted Astigmatism Value: {predicted_value}") |