updates

config.json

@@ -1,10 +1,16 @@
-{
-    "architectures": ["LSTMPredictor"],
-    "input_dim": 10,
-    "hidden_dim": 64,
-    "output_dim": 1,
-    "forecast_horizon": 3,
-    "n_layers": 2,
-    "dropout": 0.2
-  }
-  
+
+  {
+    "input_dim": 5,                        
+    "hidden_dim": 32,                      
+    "output_dim": 1,                       
+    "forecast_horizon": 3,                 
+    "n_layers": 2,                         
+    "dropout": 0.2,                        
+    "seq_length": 10,                      
+    "batch_size": 16,                      
+    "device": "cpu",                       
+    "model_path": "lstm_crisis_severity_predictor_20241116_092126.pt",  
+    "repo_id": "your_username/your_model_repo",  
+    "data_path": "test_data.npy",          
+    "output_path": "predictions.npy"       
+}

inference.py

@@ -22,64 +22,4 @@
 
 # # Print the response (the predictions)
 # print(response.json())
-import torch
-import numpy as np
-
-# Define the model architecture (this should match the one used during training)
-class LSTMPredictor(torch.nn.Module):
-    def __init__(self, input_dim, hidden_dim, output_dim, forecast_horizon, n_layers, dropout):
-        super(LSTMPredictor, self).__init__()
-        self.lstm = torch.nn.LSTM(input_dim, hidden_dim, n_layers, dropout=dropout, batch_first=True)
-        self.fc = torch.nn.Linear(hidden_dim, output_dim)
-        self.forecast_horizon = forecast_horizon
-
-    def forward(self, x):
-        # Forward pass through LSTM
-        lstm_out, _ = self.lstm(x)
-        # Only get the output from the last time step
-        out = self.fc(lstm_out[:, -1, :])
-        return out
-
-# Load the model
-def load_model(model_path):
-    model_state = torch.load(model_path)
-    model = LSTMPredictor(
-        input_dim=model_state['model_architecture']['input_dim'],
-        hidden_dim=model_state['model_architecture']['hidden_dim'],
-        output_dim=model_state['model_architecture']['output_dim'],
-        forecast_horizon=model_state['model_architecture']['forecast_horizon'],
-        n_layers=model_state['model_architecture']['n_layers'],
-        dropout=model_state['model_architecture']['dropout']
-    )
-    model.load_state_dict(model_state['model_state_dict'])
-    model.eval()  # Set model to evaluation mode
-    return model
-
-# Inference function
-def predict(model, features):
-    # Convert input features to tensor
-    input_tensor = torch.FloatTensor(features)
-    
-    # Get model prediction
-    with torch.no_grad():
-        predictions = model(input_tensor).numpy()  # No gradients needed for inference
-    
-    return predictions.tolist()
-
-# Main function to load the model and make predictions
-if __name__ == "__main__":
-    # Load the trained model
-    model = load_model('lstm_crisis_severity_predictor_20241116_092126.pt')  # Replace with actual model path
-
-    # Example input data (features) - Replace with actual features
-    test_features = np.array([[1.23, 4.56, 7.89, 10.11]])  # Example test features
-    
-    # Reshape for LSTM: (batch_size, seq_len, input_dim)
-    test_features = test_features.reshape((test_features.shape[0], 1, test_features.shape[1]))
-
-    # Get predictions
-    predictions = predict(model, test_features)
-    
-    # Output predictions
-    print("Predictions:", predictions)