Update app.py

app.py

@@ -3,7 +3,7 @@ import torch
 import torch.nn as nn
 import os
 
-# Define the model architecture
+# the model architecture
 class AddModel(nn.Module):
     def __init__(self):
         super(AddModel, self).__init__()
@@ -19,31 +19,28 @@ class AddModel(nn.Module):
         x = self.fc3(x)
         return x
 
-# Load the model from a specified path
+# load the model from a specified path
 def load_model(model_path):
     model = AddModel()
     model.load_state_dict(torch.load(model_path, map_location=torch.device('cpu')))
     model.eval()  # Set the model to evaluation mode
     return model
 
-# Function to make predictions
+#predictions
 def predict_sum(model, x1, x2):
     with torch.no_grad():
         input_tensor = torch.tensor([[x1, x2]], dtype=torch.float32)
         prediction = model(input_tensor)
         return prediction.item()
 
-# Streamlit app
 def main():
     st.title("Sum Predictor using Neural Network")
-
-    # Specify the path to your model
-    model_path = "./models/best_model.pth"  # Update with your model path
+    
+    model_path = "./models/MA1T.pth"
     if os.path.exists(model_path):
         model = load_model(model_path)
         st.success("Model loaded successfully.")
         
-        # User input for prediction
         x1 = st.number_input("Enter the first number:", value=0.0)
         x2 = st.number_input("Enter the second number:", value=0.0)