Create app.py

app.py

@@ -0,0 +1,48 @@
+import gradio as gr
+import torch
+import torch.nn as nn
+
+# Define the model architecture
+class SumModel(nn.Module):
+    def __init__(self):
+        super(SumModel, self).__init__()
+        self.fc1 = nn.Linear(2, 128)
+        self.fc2 = nn.Linear(128, 128)
+        self.fc3 = nn.Linear(128, 1)
+
+    def forward(self, x):
+        x = torch.relu(self.fc1(x))
+        x = torch.relu(self.fc2(x))
+        x = self.fc3(x)
+        return x
+
+# Load the pre-trained model
+model = SumModel()
+model.load_state_dict(torch.load('sum_model.pth'))
+model.eval()  # Set the model to evaluation mode
+
+# Function to predict the sum of two numbers
+def calculate_sum(num1, num2):
+    # Prepare the input tensor
+    inputs = torch.tensor([[num1, num2]]).float()
+    
+    # Forward pass
+    with torch.no_grad():  # Disable gradient tracking during inference
+        outputs = model(inputs)
+    
+    # Get the predicted sum
+    predicted_sum = outputs.item()
+    
+    return predicted_sum
+
+# Create a Gradio interface
+iface = gr.Interface(
+    fn=calculate_sum,
+    inputs=["number", "number"],
+    outputs="number",
+    title="Sum Predictor",
+    description="Enter two numbers to predict their sum"
+)
+
+# Launch the Gradio app
+iface.launch()