Update app.py

app.py

@@ -1,8 +1,24 @@
 # Import necessary libraries
 import torch
 
+class Net(neural_network_module.Module):
+    def __init__(self):
+        super(Net, self).__init__()
+        self.fc1 = neural_network_module.Linear(len(data[0][0]), 128)
+        self.fc2 = neural_network_module.Linear(128, 64)
+        self.fc3 = neural_network_module.Linear(64, 1)
+
+    def forward(self, x):
+        x = torch.relu(self.fc1(x))
+        x = torch.relu(self.fc2(x))
+        x = self.fc3(x)
+        return x
+    
+    # Create an instance of the network
+        
 # Load the model
-model = torch.load("model.pth")
+model = Net()
+model.save_state_dict(torch.load('model.pth'))
 
 agents = [
     'Brimstone',
@@ -69,6 +85,7 @@ ranks = [
     'Radiant',
 ]
 
+
 def preprocess_data(data):
     # Preprocess the data (replace this with your specific preprocessing steps)
     data[0] = ranks.index(data[0])
@@ -87,10 +104,10 @@ def make_prediction(rank,map,agent_picks):
     processed_data = preprocess_data(data)
 
     # Feed the data to the model
-    output = model(processed_data)
+    prediction = model(processed_data)
 
     # Post-process the output (replace this with your specific post-processing steps)
-    prediction = model(data)
+    #prediction = model(data)
     prediction = prediction.item()
     prediction = 0 if prediction < 0 else prediction