fix: Add date.floor method to fix issues with invalid hr 24:00:00 data

app.py

@@ -81,30 +81,31 @@ def clean_data(df):
     else:
         raise ValueError("Dataframe does not contain necessary columns.")
 
-def master(file):
-    # read file
-    data = pd.read_csv(file.name)
-
-    # clean data
-    data = clean_data(data)
-
-    # Convert timestamp to datetime after cleaning
-    data['timestamp'] = pd.to_datetime(data['timestamp'])
-
-    data.set_index("timestamp", inplace=True)
-
-    # Check if data has enough records to create sequences
-    if len(data) < TIME_STEPS:
-        return "Not enough data to create sequences. Need at least {} records.".format(TIME_STEPS)
-
-    df_test_value = normalize_data(data)
-    # plot input test data
-    plot1 = plot_test_data(df_test_value)
-    # predict
-    anomalies = get_anomalies(df_test_value)
-    #plot anomalous data points
-    plot2 = plot_anomalies(df_test_value, data, anomalies)
-    return plot2
+def clean_data(df):
+    # Check if the DataFrame already contains the correct columns
+    if "timestamp" in df.columns and "value" in df.columns:
+        df["timestamp"] = pd.to_datetime(df["timestamp"])
+        return df
+
+    # Check if DataFrame contains the columns to be converted
+    elif "Date" in df.columns and "Hour" in df.columns and "Hourly_Labor_Hours_Total" in df.columns:
+        # Convert "Date" and "Hour" columns into datetime format
+        df["timestamp"] = pd.to_datetime(df["Date"]) + pd.to_timedelta(df["Hour"].astype(int), unit='h')
+
+        # Handle the case where hour is 24
+        df.loc[df["timestamp"].dt.hour == 24, "timestamp"] = df["timestamp"] + pd.DateOffset(days=1)
+        df["timestamp"] = df["timestamp"].dt.floor('h')
+
+        # Keep only necessary columns
+        df = df[["timestamp", "Hourly_Labor_Hours_Total"]]
+
+        # Rename column
+        df.rename(columns={"Hourly_Labor_Hours_Total": "value"}, inplace=True)
+
+        return df
+
+    else:
+        raise ValueError("Dataframe does not contain necessary columns.")
 
 outputs = gr.outputs.Image()