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
import time 
from openai import OpenAI

#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()

def callAzure(prompt,text):
    
    url = "https://Meta-Llama-3-70B-Instruct-fkqip-serverless.eastus2.inference.ai.azure.com"
    api_key = "o5yaLhTIvg0s5zuYVInBpyneEZO8oonY"
    client = OpenAI(base_url=url, api_key=api_key)
    msg = "{} {}".format(prompt, text)
    msg = msg[:7000]
    
    response = client.chat.completions.create(
        messages=[
            {
                "role": "user",
                "content": msg,
            }
        ],
        model="azureai",
        ) 
    
    return response.choices[0].message.content
    
def call(prompt, text):
  client = Groq(api_key=os.getenv("key"),)

  prompt = prompt + " Answer only the summary, no instructions"
  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()

    output = {}
    filtering_results = filter(input_json)
    if filtering_results[0] == 0:
        #return 0
        return filtering_results[1]

    id = desc_list.index(input_json['Descriptor'])
    long_text = filtering_results[1]

    url = 'https://www.bseindia.com/xml-data/corpfiling/AttachLive/'+ input_json['FileURL'].split('Pname=')[-1]

    output["Link to BSE website"] = url

    output["Date of time of receiving data from BSE"] = input_json["newsdate"] + "Z"
    
    output["Stock Ticker"] = input_json['symbol']

    answer = callAzure(promptShort[id], long_text)
    try:
        idx = answer.index("\n")
    except:
        idx = -2
    output['Short Summary'] = answer[idx+2:]

    answer = callAzure(promptLong[id], long_text)
    try:
        idx = answer.index("\n")
    except:
        idx = -2
    output['Long summary'] = answer[idx+2:]

    prompt = "1 word Financial SEO tag for this news article"
    answer = callAzure(prompt, output['Short Summary'])
    try:
        idx = answer.index("\n")
    except:
        idx = -2
    output['Tag'] = answer[idx+2:]

    prompt = "Give a single headline for this News Article"
    answer = callAzure(prompt, output['Short Summary'])
    try:
        idx = answer.index("\n")
    except:
        idx = -2
    output['Headline'] = answer[idx+2:]

    utc_now = datetime.datetime.utcnow()
    ist_now = utc_now.astimezone(datetime.timezone(datetime.timedelta(hours=5, minutes=30)))
    
    Date = ist_now.strftime("%Y-%m-%d")
    time = ist_now.strftime("%X")
    output['Date and time of data delivery from Skylark'] = Date+"T"+time+"Z"
    
    prompt = "Answer in one word the sentiment of this News out of Positive, Negative or Neutral {}"
    output['Sentiment'] = callAzure(prompt, output['Short Summary'])

    #time.sleep(60)
    # response = client.images.generate(
    # model="dall-e-3",
    # prompt=headline.text,
    # size="1024x1024",
    # quality="standard",
    # n=1
    # ) 
    # output["Link to Infographic (data visualization only)] = response.data[0].url
    
    return output