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 
import json
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)
    
    response = client.chat.completions.create(
        messages=[
            {
                "role": "user",
                "content": msg,
            }
        ],
        model="azureai",
        max_tokens = 1000
        ) 
    
    return response.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]
    long_text = long_text.lstrip()
    long_text = long_text.rstrip()

    long_text = long_text[:6000]
    
    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 = "You are an financial expert " + callAzure(promptShort[id], long_text) + "Do not exceed over 500 characters"
    try:
        idx = answer.index("\n")
    except:
        idx = -2
    output['Short Summary'] = answer[idx+2:]

    prompt = "Answer in 1 word only. Financial SEO tag for this news article. Nothing more than that"
    answer = callAzure(prompt, output['Short Summary'])
    output['Tag'] = answer

    prompt = "Answer in single sentence. A headline for this News Article. Nothing more than that"
    answer = callAzure(prompt, output['Short Summary'])
    output['Headline'] = answer

    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'])

    completion = client.chat.completions.create(
    model="gpt-4o",
    messages=[
    {"role": "system", "content": "You are a financial expert. Help the client with summarizing the financial newsletter. Write the summary in max 500 words"},
    {"role": "user", "content": "{} {}".format(promptLong[id], long_text)}
    ],
    temperature=0,
    max_tokens=2000,
    )

    output['Long summary'] = completion.choices[0].message.content

    # 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