text_simplifier.py

import openai
import os
import time
import json
from dotenv import load_dotenv
load_dotenv()

OPENAI_API_KEY = os.getenv('OPENAI_KEY_NEW_2')
print(OPENAI_API_KEY)
openai.api_key = OPENAI_API_KEY

def summarize(prompt):
    reduced_prompt = ' '.join(prompt.replace('\n', ' ').split(' ')[:1600])
    augmented_prompt = "summarize this text to 500 words: " + reduced_prompt
    messages=[
                {"role": "system", "content": "You are a helpful assistant that summarizes and simplifies Investopedia articles through your complete knowledge of finance and investing. You will also assist the user by answering questions about the article. If the user asks a question that is not relevant to the article or finance in general, you are REJECT THE REQUEST and state `As a personal financial educator, I cannot answer that question.`."},
                {"role": "user", "content": augmented_prompt}, 
    ]
    return ask(messages)

def ask(messages):
    ### STREAM CHATGPT API RESPONSES
    delay_time = 0.01 #  faster
    max_response_length = 1500
    start_time = time.time()

    response = openai.ChatCompletion.create(
        model="gpt-3.5-turbo",
        messages=messages,
        max_tokens=1500,
        stream=True
    )
    whole_answer = ''
    for event in response:         
        # RETRIEVE THE TEXT FROM THE RESPONSE
        event_time = time.time() - start_time  # CALCULATE TIME DELAY BY THE EVENT
        event_text = event['choices'][0]['delta'] # type: ignore # EVENT DELTA RESPONSE
        answer = event_text.get('content', '') # RETRIEVE CONTENT

        # STREAM THE ANSWER
        if answer:
            whole_answer += answer

            # Convert string to byte string
            answer = answer.encode('utf-8')
            yield answer # Yield the response
        time.sleep(delay_time)
    
    yield json.dumps(messages + [{"role": "system", "content": whole_answer}])

if __name__ == '__main__':
    text = '''Pete Rathburn is a copy editor and fact-checker with expertise in economics and personal finance and over twenty years of experience in the classroom. Investopedia / Laura Porter 
    The Altman Z-score is the output of a credit-strength test that gauges a publicly traded manufacturing company's likelihood of bankruptcy. The Altman Z-score, a variation of the traditional z-score in statistics, is based on five financial ratios that can be calculated from data found on a company's annual 10-K report. It uses profitability, leverage, liquidity, solvency, and activity to predict whether a company has a high probability of becoming insolvent.
    
    NYU Stern Finance Professor Edward Altman developed the Altman Z-score formula in 1967, and it was published in 1968. Over the years, Altman has continued to reevaluate his Z-score. From 1969 until 1975, Altman looked at 86 companies in distress, then 110 from 1976 to 1995, and finally 120 from 1996 to 1999, finding that the Z-score had an accuracy of between 82% and 94%.
    
    In 2012, he released an updated version called the Altman Z-score Plus that one can use to evaluate public and private companies, manufacturing and non-manufacturing companies, and U.S. and non-U.S. companies. One can use Altman Z-score Plus to evaluate corporate credit risk. The Altman Z-score has become a reliable measure of calculating credit risk.
    
    One can calculate the Altman Z-score as follows:
    
    Altman Z-Score = 1.2A + 1.4B + 3.3C + 0.6D + 1.0E
    
    A score below 1.8 means it's likely the company is headed for bankruptcy, while companies with scores above 3 are not likely to go bankrupt. Investors can use Altman Z-scores to determine whether they should buy or sell a stock if they're concerned about the company's underlying financial strength. Investors may consider purchasing a stock if its Altman Z-Score value is closer to 3 and selling or shorting a stock if the value is closer to 1.8.
    
    In more recent years, however, a Z-Score closer to 0 indicates a company may be in financial trouble. In a lecture given in 2019 titled "50 Years of the Altman Score," Professor Altman himself noted that recent data has shown that 0—not 1.8—is the figure at which investors should worry about a company's financial strength. The two-hour lecture is available to view for free on YouTube.
    
    In 2007, the credit ratings of specific asset-related securities had been rated higher than they should have been. The Altman Z-score indicated that the companies' risks were increasing significantly and may have been heading for bankruptcy.

    Altman calculated that the median Altman Z-score of companies in 2007 was 1.81. These companies' credit ratings were equivalent to a B. This indicated that 50% of the firms should have had lower ratings, were highly distressed and had a high probability of becoming bankrupt.

    Altman's calculations led him to believe a crisis would occur and there would be a meltdown in the credit market. He believed the crisis would stem from corporate defaults, but the meltdown, which brought about the 2008 financial crisis, began with mortgage-backed securities (MBS). However, corporations soon defaulted in 2009 at the second-highest rate in history.
    The Altman Z-score, a variation of the traditional z-score in statistics, is based on five financial ratios that can be calculated from data found on a company's annual 10-K report. The formula for Altman Z-Score is 1.2*(working capital / total assets) + 1.4*(retained earnings / total assets) + 3.3*(earnings before interest and tax / total assets) + 0.6*(market value of equity / total liabilities) + 1.0*(sales / total assets). Investors can use Altman Z-score Plus to evaluate corporate credit risk. A score below 1.8 signals the company is likely headed for bankruptcy, while companies with scores above 3 are not likely to go bankrupt. Investors may consider purchasing a stock if its Altman Z-Score value is closer to 3 and selling, or shorting, a stock if the value is closer to 1.8. In more recent years, Altman has stated a score closer to 0 rather than 1.8 indicates a company is closer to bankruptcy. In 2007, Altman's Z-score indicated that the companies' risks were increasing significantly. The median Altman Z-score of companies in 2007 was 1.81, which is very close to the threshold that would indicate a high probability of bankruptcy. Altman's calculations led him to believe a crisis would occur that would stem from corporate defaults, but the meltdown, which brought about the 2008 financial crisis, began with mortgage-backed securities (MBS); however, corporations soon defaulted in 2009 at the second-highest rate in history. NYU Stern. "Predicting Financial Distress of Companies: Revisiting the Z-Score and Zeta Models," Page 18. Accessed Nov. 19, 2021. NYU Stern. "Professor Edward Altman Launches Digital App for Renowned Z-Score, "Altman Z-Score Plus." Accessed Nov. 19, 2021. NYU Stern. "Predicting Financial Distress of Companies: Revisiting the Z-Score and Zeta Models," Page 26. Accessed Nov. 19, 2021. NYU Stern. "A 50-Year Retrospective on Credit Risk Models, the Altman Z-Score Family of Models and Their Applications to Financial Markets and Managerial Strategies," Page 20. Accessed Nov. 19, 2021. NYU Stern. "Special Report on Defaults and Returns in the High-Yield Bond Market: The Year 2007 in Review and Outlook," Pages 9-13 and 27. Accessed Nov. 19, 2021 NYU Stern. "Special Report on Defaults and Returns in the High-Yield Bond Market: The Year 2007 in Review and Outlook," Pages 9-13 and 26. Accessed Nov. 19, 2021.  NYU Stern. "Special Report On Defaults and Returns in the High-Yield Bond and Distressed Debt Market: The Year 2009 in Review and Outlook," Page 3. Accessed Nov. 19, 2021. By clicking “Accept All Cookies”, you agree to the storing of cookies on your device to enhance site navigation, analyze site usage, and assist in our marketing efforts.'''
    # summary = summarize(text)
    # print(summary)

    reduced_prompt = ' '.join(text.replace('\n', ' ').split(' ')[:1600])
    messages=[
                {"role": "system", "content": "You are a helpful assistant that summarizes and simplifies Investopedia articles through your complete knowledge of finance and investing. You will also assist the user by answering questions about the article."},
                {"role": "user", "content": "summarize this text to 500 words: " + reduced_prompt}, 
                {"role": "system", "content": """The Altman Z-score is a credit-strength test that measures a publicly traded manufacturing company's likelihood of bankruptcy. It is based on five financial ratios that can be calculated from data found on a company's annual 10-K report, and uses profitability, leverage, liquidity, solvency, and activity to predict whether a company has a high probability of becoming insolvent. The Altman Z-score was created by NYU Stern Finance Professor Edward Altman in 1967, and its accuracy was found to be between 82% and 94% when it was originally researched. The formula for the Altman Z-score is 1.2A + 1.4B + 3.3C + 0.6D + 1.0E, and a score below 1.8 means that the company is at risk of bankruptcy, while companies with a score above 3 are not likely to go bankrupt.

The Altman Z-Score Plus is an updated version of the Altman Z-Score and was released in 2012. It allows investors to evaluate both public and private manufacturing and non-manufacturing companies, as well as U.S. and non-U.S. companies, and can be used to evaluate corporate credit risk. If an investor is concerned about a company's underlying financial strength, they can use the Altman Z-score to determine whether they should buy or sell a stock. In more recent years, Professor Altman has stated that a score closer to 0 rather than 1.8 indicates a company is closer to bankruptcy.

In 2007, the credit ratings of certain asset-related securities were rated higher than they should have been. The Altman Z-score indicated that these companies' risks were significantly increasing and that they may have been heading for bankruptcy. The median Altman Z-score of companies in 2007 was 1.81, which is very close to the threshold that would indicate a high probability of bankruptcy. Altman's calculations led him to believe that a crisis would occur, stemming from corporate defaults, but the 2008 financial crisis began with mortgage-backed securities (MBS). However, corporations soon defaulted in 2009 at the second-highest rate in history.

Investors may consider purchasing a stock if its Altman Z-Score value is closer to 3 and selling, or shorting, a stock if the value is closer to 1.8. The Altman Z-Score has become a reliable measure of calculating credit risk, and the Altman Z-Score Plus provides investors with a more inclusive analysis."""},
                {"role": "user", "content": "How can the Altman Z-Score be used to assess a company's financial health and predict its risk of bankruptcy?"},
    ]
    g = ask(messages)
    for m in g:
        print(m, end='', flush=True)