Update app.py

app.py

@@ -47,8 +47,53 @@ except Exception as e:
     print(f"Firebase initialization failed: {e}")
     firebase_initialized = False
 
-# Rest of your imports and class definitions remain the same...
-# [Keep all your existing imports and the MinMaxScaler class]
+class MinMaxScaler:
+    def __init__(self, feature_axis=None, minmax_range=(0, 1)):
+        self.feature_axis = feature_axis
+        self.min_ = None
+        self.max_ = None
+        self.scale_ = None
+        self.minmax_range = minmax_range
+    
+    def load_attributes(self, attributes):
+        self.min_ = np.array(attributes['min_']) if isinstance(attributes['min_'], list) else attributes['min_']
+        self.max_ = np.array(attributes['max_']) if isinstance(attributes['max_'], list) else attributes['max_']
+        self.scale_ = np.array(attributes['scale_']) if isinstance(attributes['scale_'], list) else attributes['scale_']
+        self.minmax_range = tuple(attributes['minmax_range']) if isinstance(attributes['minmax_range'], list) else attributes['minmax_range']
+    
+    def fit(self, X):
+        if X.ndim == 3 and self.feature_axis is not None:
+            axis = tuple(i for i in range(X.ndim) if i != self.feature_axis)
+            self.min_ = np.min(X, axis=axis)
+            self.max_ = np.max(X, axis=axis)
+        elif X.ndim == 2:
+            self.min_ = np.min(X, axis=0)
+            self.max_ = np.max(X, axis=0)
+        elif X.ndim == 1:
+            self.min_ = np.min(X)
+            self.max_ = np.max(X)
+        else:
+            raise ValueError("Data must be 1D, 2D, or 3D.")
+        
+        self.scale_ = self.max_ - self.min_
+        return self
+        
+    def transform(self, X):
+        if self.min_ is None or self.max_ is None or self.scale_ is None:
+        # Handle the case where scaler wasn't fitted (though it should be if attributes loaded)
+        # Or raise an error
+            raise ValueError("Scaler attributes not loaded or scaler not fitted.")
+        X_scaled = (X - self.min_) / self.scale_
+        X_scaled = X_scaled * (self.minmax_range[1] - self.minmax_range[0]) + self.minmax_range[0]
+        return X_scaled
+    
+    def inverse_transform(self, X_scaled):
+        if self.min_ is None or self.max_ is None or self.scale_ is None:
+        # Handle the case where scaler wasn't fitted
+            raise ValueError("Scaler attributes not loaded or scaler not fitted.")
+        X = (X_scaled - self.minmax_range[0]) / (self.minmax_range[1] - self.minmax_range[0])
+        X = X * self.scale_ + self.min_
+        return X
 
 # --- AQI breakpoints and calculation functions ---
 aqi_breakpoints = {