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##
## name: Alert on high Revenue Processing Error
## description:
##  * aggregate error stats from revenue_results_hybrid
##  * include files with error rate higher than threshold
##
## author: Michael S. Huang
## created: 2024-07-03
## 

########### START - User Configurable Params ###########
ERROR_RATIO_THRESHOLD = 0.05
########### END - User Configurable Params ###########



########### START - Local Mode Config ###########
# Step 1: Uncomment download_preview_input flag and run Preview successfully with the Datasources you want
download_preview_input=False
# Step 2: In MarinOne, go to Scripts -> Preview -> Logs, download 'dataSourceDict' pickle file, and update pickle_path below
# pickle_path = ''
pickle_path = '/Users/mhuang/Downloads/pickle/amazon_music_revenue_results_20240702.pkl'
# Step 3: Copy this script into local IDE with Python virtual env loaded with pandas and numpy.
# Step 4: Run locally with below code to init dataSourceDict

# determine if code is running on server or locally
def is_executing_on_server():
    try:
        # Attempt to access a known restricted builtin
        dict_items = dataSourceDict.items()
        return True
    except NameError:
        # NameError: dataSourceDict object is missing (indicating not on server)
        return False

local_dev = False

if is_executing_on_server():
    print("Code is executing on server. Skip init.")
elif len(pickle_path) > 3:
    print("Code is NOT executing on server. Doing init.")
    local_dev = True
    # load dataSourceDict via pickled file
    import pickle
    dataSourceDict = pickle.load(open(pickle_path, 'rb'))

    # print shape and first 5 rows for each entry in dataSourceDict
    for key, value in dataSourceDict.items():
        print(f"Shape of dataSourceDict[{key}]: {value.shape}")
        # print(f"First 5 rows of dataSourceDict[{key}]:\n{value.head(5)}")

    # set outputDf same as inputDf
    inputDf = dataSourceDict["1"]
    outputDf = inputDf.copy()

    # setup timezone
    import datetime
    # Chicago Timezone is GMT-5. Adjust as needed.
    CLIENT_TIMEZONE = datetime.timezone(datetime.timedelta(hours=-5))

    # import pandas
    import pandas as pd
    import numpy as np

    # other imports
    import re
    import urllib

    # import Marin util functions
    from marin_scripts_utils import tableize, select_changed

    # pandas settings
    pd.set_option('display.max_columns', None)  # Display all columns
    pd.set_option('display.max_colwidth', None)  # Display full content of each column

else:
    print("Running locally but no pickle path defined. dataSourceDict not loaded.")
    exit(1)
########### END - Local Mode Setup ###########

today = datetime.datetime.now(CLIENT_TIMEZONE).date()

# primary data source and columns
inputDf = dataSourceDict["1"]
FEED_COL_RAW_REV_FILE_NAME = 'raw_rev_file_name'
FEED_COL_LINE_INDEX = 'line_index'
FEED_COL_RESULT = 'Result'
FEED_COL_CUST_ID = 'cust_id'
FEED_COL_CLIENT_ID = 'client_id'
FEED_COL_REVENUE_SOURCE = 'revenue_source'
FEED_COL_JOB_NOMINAL_DATE = 'job_nominal_date'
FEED_COL_CLICK_MAPPING_TIMESTAMP = 'click_mapping_timestamp'

COL_FILE_LINE_COUNT = 'file_line_count'
COL_FILE_ERROR_COUNT = 'file_error_count'
COL_FILE_ERROR_RATIO = 'file_error_ratio'
COL_FILE_ERROR_PERCENTAGE = 'file_error_pct'
COL_FILE_PROCESSED_TIME = 'file_processed_time'

#### User Code Starts Here

print("inputDf.shape", inputDf.shape)
print("inputDf head", tableize(inputDf.head()))

# convert timestamp
inputDf[COL_FILE_PROCESSED_TIME] = pd.to_datetime(inputDf[FEED_COL_CLICK_MAPPING_TIMESTAMP], unit='ms').dt.tz_localize('UTC').dt.tz_convert('America/Los_Angeles')

# Calculate the required columns for each revenue filename
agg_funcs = {
    FEED_COL_JOB_NOMINAL_DATE: 'first',
    COL_FILE_PROCESSED_TIME: 'first',
    FEED_COL_REVENUE_SOURCE: 'first',
    FEED_COL_LINE_INDEX: 'count',
    FEED_COL_RESULT: lambda x: x.str.startswith('ERROR').sum()
}

agg_df = inputDf.groupby(FEED_COL_RAW_REV_FILE_NAME) \
                .agg(agg_funcs) \
                .reset_index()

agg_df.columns = [FEED_COL_RAW_REV_FILE_NAME, FEED_COL_JOB_NOMINAL_DATE, COL_FILE_PROCESSED_TIME, FEED_COL_REVENUE_SOURCE, COL_FILE_LINE_COUNT, COL_FILE_ERROR_COUNT]

# Calculate error ratio and percentage
agg_df[COL_FILE_ERROR_RATIO] = np.round(agg_df[COL_FILE_ERROR_COUNT] / agg_df[COL_FILE_LINE_COUNT], 4)
agg_df[COL_FILE_ERROR_PERCENTAGE] = (agg_df[COL_FILE_ERROR_RATIO] * 100).round(2).astype(str) + '%'

debugDf = agg_df

# select high error rate files for output
out_cols = [FEED_COL_JOB_NOMINAL_DATE, COL_FILE_PROCESSED_TIME, FEED_COL_REVENUE_SOURCE, COL_FILE_LINE_COUNT, COL_FILE_ERROR_PERCENTAGE, FEED_COL_RAW_REV_FILE_NAME]
outputDf = agg_df.loc[agg_df[COL_FILE_ERROR_RATIO] > ERROR_RATIO_THRESHOLD, out_cols]


print("outputDf.shape", outputDf.shape)
print("outputDf head", tableize(outputDf.head()))

### Construct Complete Prompt
    
## Build Prompt

# blank out prompt for now
emailSummaryPrompt = ''

### local debug

if local_dev:
    with open('prompt.txt', 'w') as file:
        file.write(emailSummaryPrompt)
        print(f"Local Dev: Prompt written to: {file.name}")

    output_filename = 'outputDf.csv'
    outputDf.to_csv(output_filename, index=False)
    print(f"Local Dev: Output written to: {output_filename}")

    debug_filename = 'debugDf.csv'
    debugDf.to_csv(debug_filename, index=False)
    print(f"Local Dev: Debug written to: {debug_filename}")

else:
    print("====== Prompt =====")
    print(emailSummaryPrompt)
    print("===========")