import datetime
from urllib.request import Request, urlopen
from pypdf import PdfReader
from io import StringIO
import io
import pandas as pd
import os
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
from transformers import pipeline
from openai import OpenAI
from groq import Groq

#openai_key = "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"
#client = OpenAI(api_key = openai_key)
desc = pd.read_excel('Descriptor.xlsx',header = None)
desc_list  = desc.iloc[:,0].to_list()
  
model = "llama3-70b-8192"
pipe = pipeline("text-classification", model="mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis")

def call(prompt, text):
  client = Groq(api_key=os.getenv("key"),)

  chat_completion = client.chat.completions.create(
  messages=[
      {
          "role": "user",
          "content": "{} {}".format(prompt, text),
      }
  ],
  model=model,
  )

  return chat_completion.choices[0].message.content

def filter(input_json):

  sym = pd.read_excel('symbol.xlsx',header = None)
  sym_list  = sym.iloc[:,0].to_list()
  
  if input_json['FileURL']==None or input_json['FileURL'].lower()=='null':
    return [0,"File_URL"]
  if input_json['symbol']== 'null' or input_json['symbol'] not in sym_list:
    return [0,"symbol"]
  if input_json['TypeofAnnouncement'] not in ['General_Announcements','Outcome','General']:
    return [0,"Annoucement"]
  if input_json['Descriptor'] not in desc_list:
    return [0,"Desc"]
      
  url = 'https://www.bseindia.com/xml-data/corpfiling/AttachLive/'+ input_json['FileURL'].split('Pname=')[-1]
  req = Request(url, headers={'User-Agent': 'Mozilla/5.0'})
  html = urlopen(req)
  cont = html.read()
  reader = PdfReader(io.BytesIO(cont))
  content = ''
  for i in range(len(reader.pages)):
    content+= reader.pages[i].extract_text()
  document = content

  return [1, document]

def summary(input_json):
    
    prompt = pd.read_excel('DescriptorPrompt.xlsx')
    promptShort  = prompt.iloc[:,1].to_list()
    promptLong  = prompt.iloc[:,2].to_list()

    id = desc_list.index(input_json['Descriptor'])
    output = {}
    filtering_results = filter(input_json)
    if filtering_results[0] == 0:
        return 0
    #return filtering_results[1]
    

    long_text = filtering_results[1]

    output['mobile'] = call(promptShort[id], long_text)

    output['web'] = call(promptLong[id], long_text)

    prompt = "1 word Financial SEO tag for this news article"
    output['tag'] = call(prompt, output['mobile'])

    prompt = "Headline for this News Article"
    output['headline'] = call(prompt, output['web'])

    utc_now = datetime.datetime.utcnow()
    ist_now = utc_now.astimezone(datetime.timezone(datetime.timedelta(hours=5, minutes=30)))
    output['Time'] = ist_now.strftime("%I:%M %p")
  
    month_name = ist_now.strftime("%B")
    output['Date'] = f"{ist_now.day} {month_name}, {ist_now.year}"
    
    prompt = "Answer in one word the sentiment of this News out of Positive, Negative or Neutral {}"
    output['sentiment'] = call(prompt, output['web'])
    
    # response = client.images.generate(
    # model="dall-e-3",
    # prompt=headline.text,
    # size="1024x1024",
    # quality="standard",
    # n=1
    # ) 
    # output["image_url"] = response.data[0].url
    
    return output