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 = callAzure("You are an financial expert. Wherever possible, mention the name of the company " + promptShort[id] + " Do not exceed over 400 characters", long_text) 
    try:
        idx = answer.index("\n")
    except:
        idx = -2
    output['Short Summary'] = answer[idx+2:]

    answer = callAzure("Make sure the following summary of a news article is not more than 80 words. Rewrite it and make it below 80 words ", output['Short Summary'])
    try:
        idx = answer.index("\n")
    except:
        idx = -2
    output['Short Summary'] = answer[idx+2:]

    prompt = "Provide the main topic of the news article strictly as a tag, using only one or two words, with only the first word capitalized and the rest in lowercase. No additional text or explanation."
    answer = callAzure(prompt, output['Short Summary'])
    output['Tag'] = answer

    prompt = "Generate a precise headline for the news article that includes the name of the company. Be very careful about correctly representing any financial figures mentioned in lakhs and crores. Provide only the headline, with no additional text or explanation."
    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. Do not truncate."},
    {"role": "user", "content": "{} {}".format(promptLong[id], long_text)}
    ],
    temperature=0,
    max_tokens=4000,
    )

    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