import functions_framework
from firebase_functions import https_fn
# import firebase_admin
from firebase_admin import initialize_app, firestore
import pandas as pd
import numpy as np
import datetime
import sys
import platform
import os
import io
from flask import send_file
from datetime import datetime
import re
import xlsxwriter
import math
import matplotlib.pyplot as plt

initialize_app()

@functions_framework.http
def performance(request):
    """HTTP Cloud Function.
    Args:
        request (flask.Request): The request object.
        <https://flask.palletsprojects.com/en/1.1.x/api/#incoming-request-data>
    Returns:
        The response text, or any set of values that can be turned into a
        Response object using `make_response`
        <https://flask.palletsprojects.com/en/1.1.x/api/#flask.make_response>.
    """

    if request.method == 'OPTIONS':
        headers = {
            'Access-Control-Allow-Origin': '*',
            'Access-Control-Allow-Methods': 'GET',
            'Access-Control-Allow-Headers': 'Content-Type',
            # 'Access-Control-Max-Age': '3600'
        }

        return ('', 204, headers)

    headers = {
        'Access-Control-Allow-Origin': '*'
    }

    request_json = request.get_json(silent=True)
    request_args = request.args

    if request_json and 'start_date' in request_json and 'end_date' in request_json:
        start_date = request_json['start_date']
        end_date = request_json['end_date']
    elif request_args and 'start_date' in request_args and 'end_date' in request_args:
        start_date = request_args['start_date']
        end_date = request_args['end_date']
    else:
        start_date = "2023-12-04"
        end_date = "2023-12-05"
      
    path_delim = "/"

    db = firestore.client()

    test_collection = db.collection("testData")

    data = []

    test_docs = test_collection.stream()
    for test_doc in test_docs:
        test_data = test_doc.to_dict()
        data.append(test_data)

    df = pd.DataFrame(data)

    df = df[(df['testTime'] > start_date) & (df['testTime'] <= end_date)]
    df = df.sort_values(by=['testTime'], ascending=False)
    # df = df.drop(['resultData', "reportUploadTime", "userImageURL", "testType", "birthYear", "testStatus", "reportPath", "createdBy", "csvPath", "result", "mobileId", "resultRatio", "localFlag", "led2", "led1"], axis=1)
    df = df[["_id", "classificationResult",  "prdClassification", "predictedDenovixRatio", "calculatedRatio", "deviceRatio", "kitSerial", "abs1", "led1Average", "led1Buffer", "led1Sample", "abs2", "led2Average", "led2Buffer", "led2Sample", "abs3", "led3Average", "led3Buffer", "led3Sample", "abs4", "led4Average", "led4Buffer", "led4Sample", "deviceId", "deviceSerialNumber", "name", "testTime"]]
    df.rename(columns={'deviceSerialNumber': "loginId"}, inplace = True)
    # df = df.reindex(sorted(df.columns), axis=1)

    df = df.drop_duplicates()

    curdir = os.getcwd()
    path_delim = '/'

    precision_threshold = 0.009
    accuracy_threshold = 0.02

    denovix_reference_values = {
        "Tartrazine": {
            "30": 0.187191676,
            "50": 0.187447015,
            "60": 0.187191676,
            "75": 0.294739334,
            "90": 0.187191676,
            "100": 0.410113264,
            "120": 0.187191676,
            "125": 0.512526022,
            "150": 0.622883833,
            "175": 0.721095085,
            "180": 0.187191676,
            "200": 0.824065454,
            "210": 0.187191676,
            "225": 0.931385567,
            "250": 1.002224884,
            "275": 1.091996882,
            "300": 1.16558307,
        },
        "KMnO4": {
            "145": 0.187191676,
            "250": 0.077846397,
            "290": 0.359288533,
            "350": 0.111884606,
            "435": 0.187191676,
            "450": 0.138031952,
            "550": 0.175111257,
            "580": 0.628039375,
            "650": 0.208337398,
            "720": 0.187191676,
            "750": 0.238773222,
            "850": 0.267574903,
            "950": 0.299568449,
            "1050": 0.329271864,
            "1150": 0.365827844,
            "1160": 0.388015579,
            "1250": 0.388015579,
            "1350": 0.417304075,
            "1450": 0.447634285,
            "1550": 0.478220085,
        },
    }
    df_reference_device = pd.DataFrame(denovix_reference_values).T
    df_reference_device.index.name = 'Solution'
    df_reference_device.columns.name = 'Wavelength'

    texts_to_check = ['Tar', 'KM']
    df['solution'] = df['name'].str.extract(f"({'|'.join(texts_to_check)})", flags=re.IGNORECASE)
    df['solution'] = df['solution'].replace({'Tar': 'Tartrazine', 'KM': 'KMnO4'}, regex=True)

    def extract_numbers(s):
        match = re.match(r'\d+', s)
        if match:
            return int(match.group())
        else:
            return None

    df["concentration"] =  df['name'].apply(extract_numbers)
    df['absorbance'] = df.apply(lambda row: row['led1Average'] if row['solution'] == 'Tartrazine' else row['led3Average'], axis=1)

    fig, ax = plt.subplots()

    unique_solutions = df['solution'].unique()

    for solution in unique_solutions:
        solution_data = df[df['solution'] == solution]
        
        concentration_means = solution_data.groupby('concentration')['absorbance'].mean()
        
        ax.plot(concentration_means.index, concentration_means.values, marker='o', linestyle='-', label=f'Mean Absorbance for {solution}')

    ax.set_title('Mean Absorbance for Each Solution')
    ax.set_xlabel('Concentration')
    ax.set_ylabel('Mean Absorbance')
    ax.legend()

    df_precision_acc = df[["deviceId", "solution", "concentration", "led1Average", "led3Average", "absorbance"]].groupby(["deviceId", "solution", "concentration"]).describe()["absorbance"][["count", "min", "max", "mean"]]
    df_precision_acc['precision'] = df_precision_acc['max'] - df_precision_acc['min']

    df_precision_acc['precision_result'] = ['Fail' if diff > precision_threshold else 'Pass' for diff in df_precision_acc['precision']]

    df_precision_acc.reset_index(inplace=True)
    df_precision_acc.set_index(["deviceId", "solution", "concentration"], inplace=True)

    device_precision_results = {}
    for index, group_df in df_precision_acc.groupby(level=[0, 1, 2]):
        soln = index[1]
        concen = index[2]
        result_values = group_df['precision_result'].values

        if index[0] not in device_precision_results:
            device_precision_results[index[0]] = "Pass"
        else:
            result = 'Fail' if 'Fail' in result_values else 'Pass'
            if result == 'Fail':
                device_precision_results[index[0]] = 'Fail'
        
    df_device_precicion_results = pd.DataFrame(list(device_precision_results.items()), columns=['Device', 'Result'])

    df_precision_acc = df_precision_acc.assign(accuracy='', accuracy_result='')

    device_accuracy_results = {}

    for idx, row in df_precision_acc.iterrows():
        device_id = row.name[0]
        solution = row.name[1]
        concentration = str(int(row.name[2]))
        reference_value = denovix_reference_values[solution][concentration]
        
        try:
            reference_value = denovix_reference_values[solution][concentration]
            accuracy = math.fabs(row['mean'] - reference_value)
            df_precision_acc.at[idx, 'accuracy'] = accuracy
            
            accuracy_result = 'Pass' if accuracy < accuracy_threshold else 'Fail'
            df_precision_acc.at[idx, 'accuracy_result'] = accuracy_result
            if device_id not in device_accuracy_results:
                device_accuracy_results[device_id] = accuracy_result
            else:
                if accuracy_result == 'Fail':
                    device_accuracy_results[device_id] = 'Fail'
        except KeyError:
            df_precision_acc.at[idx, 'accuracy'] = np.nan
            df_precision_acc.at[idx, 'accuracy_result'] = 'Fail'  # Assume 'Fail' for missing data

    df_device_accuracy_results = pd.DataFrame(list(device_accuracy_results.items()), columns=['Device', 'Result'])

    device_results = {}

    df_device_results = pd.merge(df_device_precicion_results, df_device_accuracy_results, on='Device', how='outer', suffixes=('_precision', '_accuracy'))
    df_device_results['Result'] = np.where((df_device_results['Result_precision'] == 'Fail') | (df_device_results['Result_accuracy'] == 'Fail'), 'Fail', 'Pass')

    output_filename = f'/tmp/accuracy_{datetime.today().strftime("%d_%m_%Y_%H_%M")}.xlsx'
    output_path = os.getcwd() + path_delim + output_filename
    print('output_path', output_path)
    writer = pd.ExcelWriter(output_filename, engine = 'xlsxwriter')
    df_device_results.to_excel(writer, sheet_name="device_results")
    # df_device_precicion_results.to_excel(writer, sheet_name="device_precision")
    # df_device_accuracy_results.to_excel(writer, sheet_name="device_accuracy")
    df_precision_acc.to_excel(writer, sheet_name="concentration")
    df.to_excel(writer, sheet_name="in")
    df_reference_device.to_excel(writer, sheet_name="reference_device")

    workbook = writer.book
    worksheet_device_results = writer.sheets['device_results']

    red_format = workbook.add_format({'bg_color': '#FFC7CE', 'font_color': '#9C0006'})
    green_format = workbook.add_format({'bg_color': '#C6EFCE', 'font_color': '#006100'})

    worksheet_device_results.conditional_format('E2:E{}'.format(df_device_results.shape[0] + 1), {'type': 'text',
                                                                                                'criteria': 'containing',
                                                                                                'value': 'Fail',
                                                                                                'format': red_format})

    worksheet_device_results.conditional_format('E2:E{}'.format(df_device_results.shape[0] + 1), {'type': 'text',
                                                                                                'criteria': 'containing',
                                                                                                'value': 'Pass',
                                                                                                'format': green_format})

    wks1 = workbook.add_worksheet('abs_plot')
    wks1.write(0,0,'Abs Plot')

    imgdata=io.BytesIO()
    fig.savefig(imgdata, format='png')
    wks1.insert_image(2,2, '', {'image_data': imgdata})


    ### debug each row
    # # Merge df and df_device_precicion_results on deviceId
    # df_merged = pd.merge(df, df_device_precicion_results, left_on='deviceId', right_on='Device', how='left')

    # # Drop the duplicate "Device" column
    # df_merged.drop(columns=['Device'], inplace=True)

    # # Save the merged DataFrame to the Excel file
    # df_merged.to_excel(writer, sheet_name="merged_results")

    writer.close()
    
    # firebase_admin.delete_app(firebase_admin.get_app())

    with open(output_filename,'rb') as f:
        file_data = io.BytesIO(f.read())  

    # application/vnd.ms-excel
    return send_file(file_data, download_name= output_filename, mimetype="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet")