Improve output

app.py

@@ -15,20 +15,15 @@
 urllib.request.urlretrieve(url_model, model_path)
 urllib.request.urlretrieve(url_scaler, scaler_path)
 
-single_prediction = SinglePrediction(model_path, scaler_path, "data/preprocessing/raw_features_2024.csv")
+single_prediction = SinglePrediction(model_path, scaler_path, "data/preprocessing/raw_features_2024.csv",
+                                     "data/metadata/metadata.json")
 
 
 @app.route('/')
 def hello():
     return 'Hello World!'
 
 
-@app.route('/metadata')
-def metadata():
-    metadata_json = json.load(open("data/metadata/metadata.json"))
-    return metadata_json
-
-
 @app.route('/predict', methods=['POST'])
 def predict():
     if request.method == 'POST':

deploy/single_prediction.py

@@ -1,4 +1,5 @@
 import torch
+import json
 import pandas as pd
 from torch.utils.data import TensorDataset, DataLoader
 from model.scaler import Scaler
@@ -9,7 +10,10 @@
 
 
 class SinglePrediction:
-    def __init__(self, model_path, scaler_path, raw_features_path):
+    def __init__(self, model_path, scaler_path, raw_features_path, metadata_path):
+        self.metadata = json.load(open(metadata_path))
+        self.labels_readable = [self.metadata["parking_sg"]["fields"][field]["label"] for field in parking_data_labels]
+        self.max_capacity = [self.metadata["parking_sg"]["fields"][field]["max_cap"] for field in parking_data_labels]
         self.scaler = Scaler.load(scaler_path)
         self.single_prediction_features = SinglePredictionFeatures(raw_features_path)
         self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
@@ -36,11 +40,17 @@ def predict_for_date(self, date):
 
         output_scaled_back = self.scaler.inverse_transform(pd.DataFrame(output, columns=parking_data_labels))
 
-        return [dict(zip(parking_data_labels, row)) for row in output_scaled_back.tolist()]
+        return {
+            "predictions": output_scaled_back.tolist()[0],
+            "labels": parking_data_labels,
+            "labels_readable": self.labels_readable,
+            "max_capacity": self.max_capacity
+        }
 
 
 if __name__ == "__main__":
-    predict = SinglePrediction("model_scripted.pt", "scaler.pkl", "../data/preprocessing/raw_features_2024.csv")
+    predict = SinglePrediction("../model_scripted.pt", "../scaler.pkl", "../data/preprocessing/raw_features_2024.csv",
+                               "../data/metadata/metadata.json")
     print(predict.predict_for_date("2023-12-08 08:00"))
     print(predict.predict_for_date("2023-12-10 18:00"))
     print(predict.predict_for_date("2023-12-12 12:00"))