"""Utilis Functions"""
import os
import time
from datetime import datetime
from decimal import Decimal
import requests
import boto3
from lxml import etree
from googletrans import Translator
from transformers import pipeline
from PyPDF2 import PdfReader

AWS_ACCESS_KEY_ID = os.environ['AWS_ACCESS_KEY_ID']
AWS_SECRET_ACCESS_KEY = os.environ['AWS_SECRET_ACCESS_KEY']
# AWS_ACCESS_KEY_ID="AKIAQFXZMGHQYXKWUDWR"
# AWS_SECRET_ACCESS_KEY="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"

analyzer = pipeline("sentiment-analysis", model="ProsusAI/finbert")

translator = Translator()

def translate(text):
    return translator.translate(text, dest='en').text

def datemodifier(date_string, date_format):
    """Date Modifier Function"""
    try:
        to_date = time.strptime(date_string,date_format)
        return time.strftime("%Y-%m-%d",to_date)
    except:
        return False

def fetch_url(url):
    response = requests.get(url)
    if response.status_code == 200:
        return response.text
    else:
        return None

def translist(infolist):
    """Translist Function"""
    out = list(filter(lambda s: s and
                      (isinstance (s,str) or len(s.strip()) > 0), [i.strip() for i in infolist]))
    return out

def encode(content):
    """Encode Function"""
    text = ''
    for element in content[:1]:
        if isinstance(element, etree._Element):
            subelement = etree.tostring(element).decode()
            subpage = etree.HTML(subelement)
            tree = subpage.xpath('//text()')
            line = ''.join(translist(tree)).\
                replace('\n','').replace('\t','').replace('\r','').replace('  ','').strip()
        else:
            line = element
        text += line
        index = text.find('打印本页')
        if index != -1:
          text = text[:index]
        
    return text

def extract_from_pdf(url):
    # Send a GET request to the URL and retrieve the PDF content
    response = requests.get(url)
    pdf_content = response.content

    # Save the PDF content to a local file
    with open("downloaded_file.pdf", "wb") as f:
        f.write(pdf_content)

    # Open the downloaded PDF file and extract the text
    with open("downloaded_file.pdf", "rb") as f:
        pdf_reader = PdfReader(f)
        num_pages = len(pdf_reader.pages)
        extracted_text = ""
        extracted_text_eng = ""
        for page in range(num_pages):
            text = pdf_reader.pages[page].extract_text()
            if text and text[0].isdigit():
                text = text[1:]
            first_newline_index = text.find('\n')
            text = text[:first_newline_index+1].replace('\n', ' ') + text[first_newline_index+1:].replace('\n', '')
            extracted_text_eng += translator.translate(text, dest='en').text
            extracted_text += text
    return extracted_text, extracted_text_eng

def get_db_connection():
    """Get dynamoDB connection"""
    dynamodb = boto3.resource(
    service_name='dynamodb',
    region_name='us-east-1',
    aws_access_key_id=AWS_ACCESS_KEY_ID,
    aws_secret_access_key=AWS_SECRET_ACCESS_KEY
    )
    return dynamodb

def sentiment_computation(content):
    label_dict = {
        "positive": "+",
        "negative": "-",
        "neutral": "0",
    }
    sentiment_score = 0
    maximum_value = 0
    raw_sentiment = analyzer(content[:512], return_all_scores=True)
    sentiment_label = None
    for sentiment_dict in raw_sentiment[0]:
        value = sentiment_dict["score"]
        if value > maximum_value:
            sentiment_label = sentiment_dict["label"]
            maximum_value = value
        if sentiment_dict["label"] == "positive":
            sentiment_score = sentiment_score + value
        if sentiment_dict["label"] == "negative":
            sentiment_score = sentiment_score - value
        else:
            sentiment_score = sentiment_score + 0
    return sentiment_score, label_dict[sentiment_label]

def upsert_content(report):
    """Upsert the content records"""
    dynamodb = get_db_connection()
    table = dynamodb.Table('article_china')
        # Define the item data
    item = {
        'id': str(report['id']),
        'site': report['site'],
        'title': report['title'],
        # 'originalSite': report['originalSite'],
        # 'originalTitle': report['originalTitle'],
        # 'originalContent': report['originalContent'],
        'category': report['category'],
        # 'author': report['author'],
        'content': report['content'],
        'publishDate': report['publishDate'],
        'link': report['url'],
        # 'attachment': report['reporturl'],
        # 'authorID': str(report['authorid']),
        'sentimentScore': str(Decimal(report['sentimentScore']).quantize(Decimal('0.01'))),
        'sentimentLabel': report['sentimentLabel'],
        'LastModifiedDate': datetime.now().strftime("%Y-%m-%dT%H:%M:%S")
    }
    response = table.put_item(Item=item)
    print(response)