Create app.py

app.py

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+import streamlit as st
+import torch
+import torch.nn as nn
+import os
+
+# Define the model architecture
+class AddModel(nn.Module):
+    def __init__(self):
+        super(AddModel, self).__init__()
+        self.fc1 = nn.Linear(2, 32)
+        self.relu1 = nn.ReLU()
+        self.fc2 = nn.Linear(32, 64)
+        self.relu2 = nn.ReLU()
+        self.fc3 = nn.Linear(64, 1)
+
+    def forward(self, x):
+        x = self.relu1(self.fc1(x))
+        x = self.relu2(self.fc2(x))
+        x = self.fc3(x)
+        return x
+
+# 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
+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
+    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)
+
+        if st.button("Predict"):
+            predicted_sum = predict_sum(model, x1, x2)
+            st.write(f"The predicted sum of {x1} and {x2} is: {predicted_sum:.2f}")
+    else:
+        st.error("Model file not found. Please upload the model.")
+
+if __name__ == "__main__":
+    main()