import sys

import os

sys.path.append(os.path.abspath("../"))

import argparse

from enum import Enum

import io

from gcloud import storage

from google.cloud import vision

from google.cloud.vision import types

from PIL import Image, ImageDraw

import os

import tempfile

from pdf2image import convert_from_path, convert_from_bytes

from request_handling_aws import get_text, get_all_text

from patient import Patient

import re

import pandas as pd

import nltk

block_markers = ['<START>', 'NAME AND ADDRESS', 'EMERGENCY CONTACT NAME AND ADDRESS', 'PRIMARY INSURANCE', 'GUARANTOR', 'ADMITTING PHYSICIAN']

breaking_phrase = 'SAINT ANTHONY'

def get_patients(text_block, form_data, block_markers, breaking_phrase):

    patient_list = []

    text_block = ['<START>'] + text_block

    blocks = {x:[] for x in block_markers}

    curr_marker = ''

    curr_patient = Patient(block_markers=block_markers)

    i = 0

    for line in text_block:

        nxt_ln = 0

        if nltk.edit_distance(breaking_phrase, line.strip())<3:

            for key,val in blocks.items():

                print("\n\n" + key)

                print(val)

            if len(blocks['<START>']) == 0:

                continue

            curr_patient.process_gen_info(blocks, form_data[i])

            i += 1

            patient_list.append(curr_patient)

            curr_marker = '<START>'

            blocks = {x:[] for x in block_markers}

            curr_patient = Patient(block_markers=block_markers)

        for block_marker in block_markers:

            if nltk.edit_distance(block_marker, line.strip())<3:

                curr_marker = block_marker

                nxt_ln = 1

                break

        if nxt_ln == 1:

            continue

        blocks[curr_marker].append(line)

    for key,val in blocks.items():

        print("\n\n" + key)

        print(val)

    curr_patient.process_gen_info(blocks, form_data[i])

    patient_list.append(curr_patient)

    return patient_list

def compile_dataframe(patient_list):

    pat_df = pd.DataFrame()

    for patient in patient_list:

        pat_df = pd.concat([pat_df,pd.DataFrame([patient.csv_rep()], columns=patient.csv_rep().keys())],axis=0,join='outer').reset_index(drop=True)

    pat_df = pat_df.dropna(axis=1, how='all')

    dob_cols = [col for col in pat_df.columns if 'dob' in col]

    print("DOB COLS:", dob_cols)

    print(pat_df)

    for col in dob_cols:

        pat_df[col] = pat_df[col].astype(str).apply(lambda x:  x.split(" ")[0] if (x != None) else x)

    return pat_df

def run_pipeline(full_body, form_data=None):

    print("RUNNING SAG PIPELINE........")

    block_markers = ['<START>', 'NAME AND ADDRESS', 'EMERGENCY CONTACT NAME AND ADDRESS', 'PRIMARY INSURANCE', 'GUARANTOR', 'ADMITTING PHYSICIAN']

    breaking_phrase = 'SAINT ANTHONY'

    patient_list = get_patients(full_body, form_data, block_markers, breaking_phrase)

    fin_df = compile_dataframe(patient_list)

    return fin_df

if __name__ == "__main__":

    full_body, ids, form_data = get_all_text("facesheet-ap","facesheet_sag/", require_form=True)

    record = []

    pat_fl = 0

    nam_nl = 0

    block_markers = ['<START>', 'NAME AND ADDRESS', 'EMERGENCY CONTACT NAME AND ADDRESS', 'PRIMARY INSURANCE', 'GUARANTOR', 'ADMITTING PHYSICIAN']

    breaking_phrase = 'SAINT ANTHONY'

    patient_list = get_patients(full_body, form_data, block_markers, breaking_phrase)

    fin_df = compile_dataframe(patient_list)

    fin_df.to_excel("./output_fin.xlsx")

    with pd.option_context('display.max_rows', None, 'display.max_columns', None):  # more options can be specified also

        print(fin_df)