Update app.py

app.py

@@ -5,26 +5,25 @@ from src.model import LSTM  # Adjust to your model path
 
 # Load the model
 device = 'cuda' if torch.cuda.is_available() else 'cpu'
-model_path = "./water_forecast_2.pth"  # Your model path
+model_path = "./water_forecast_2.pth"
 model = LSTM(input_size=8, lstm_layer_sizes=[128, 128, 128], output_size=3).to(device)
 model.load_state_dict(torch.load(model_path, map_location=device, weights_only=True))
 model.eval()
 
 # Define the prediction function
 def predict_water_usage(state_idx, target_year, structured_data):
-    # Convert input data to tensor
     tensor_data = torch.tensor(np.array(list(structured_data.values())), dtype=torch.float32).to(device)
     with torch.no_grad():
         output = model(tensor_data)
     return output.tolist()
 
-# Set up Gradio interface
+# Update Gradio interface using gr.components
 inputs = [
-    gr.inputs.Number(label="State Index"),
-    gr.inputs.Number(label="Target Year"),
-    gr.inputs.JSON(label="Structured Data")  # Expects JSON input
+    gr.components.Number(label="State Index"),
+    gr.components.Number(label="Target Year"),
+    gr.components.JSON(label="Structured Data")  # Expects JSON input
 ]
-outputs = gr.outputs.JSON(label="Prediction")
+outputs = gr.components.JSON(label="Prediction")
 
 interface = gr.Interface(fn=predict_water_usage, inputs=inputs, outputs=outputs)