import docx
import requests
from openai import OpenAI
import os
import openai
from Levenshtein import ratio
from docx.shared import Pt
from bs4 import BeautifulSoup
import io
from docx.oxml.shared import OxmlElement, qn
from docx.shared import Pt
from docx.enum.text import WD_ALIGN_PARAGRAPH
WEBZ_API_KEY = os.getenv(“WEBZ_API_KEY”)
openai.api_key = os.getenv(“OPENAI_API_KEY”)
NUM_OF_REPORTS = 5
client = OpenAI()
def are_similar(str1, str2, threshold=1):
“””
Check if two strings are similar based on Levenshtein ratio.
“””
return ratio(str1, str2) > threshold
def remove_similar_strings(articles):
unique_articles = []
for article in articles:
if not any(are_similar(article[‘text’], existing[‘text’], 0.7) for existing in unique_articles):
unique_articles.append(article)
return unique_articles
def trim_string(string, max_length):
if len(string) > max_length:
return string[:max_length]
else:
return string
# Function to get news articles from Webz.io API
def fetch_articles(query, api_key, total):
endpoint = f“https://api.webz.io/filterWebContent?token={api_key}&format=json&q={query}&size=100&ts=0”
all_posts = []
while total > 0:
response = requests.get(endpoint)
data = response.json()
posts = data[“posts”]
if len(posts) == 0:
break
all_posts.extend(posts)
total -= len(posts)
if total > 0 and “next” in data:
endpoint = f“https://api.webz.io{data[‘next’]}”
else:
break
articles = []
for article in all_posts:
article = {‘title’: article[“title”],
‘text’: trim_string(trim_title(article[“title”]) + “\n\n” + article[“text”], 10000),
‘link’: article[‘url’],
‘published’: article[‘published’]}
articles.append(article)
return articles
def trim_title(input_string):
words = input_string.split()
if “|” in input_string:
return input_string.split(“|”)[0]
last_dash_index = input_string.rfind(“-“)
if last_dash_index != –1:
right_of_dash = input_string[last_dash_index + 1:]
right_words = right_of_dash.split()
if len(right_words) <= 3 and len(words) > 10:
return input_string[:last_dash_index]
return input_string
def add_image_from_base64(doc, image_url):
response = requests.get(image_url)
# Check if the request was successful
if response.status_code == 200:
image_stream = io.BytesIO(response.content)
doc.add_picture(image_stream, width=docx.shared.Inches(6))
else:
print(f“Failed to download image. Status code: {response.status_code}”)
def html_to_word(doc, html_content):
soup = BeautifulSoup(html_content, ‘html.parser’)
for element in soup.find_all([‘b’, ‘ul’]):
if element.name == ‘b’:
# Add bold text as a heading
doc.add_paragraph(element.get_text(), style=‘Heading 2’)
elif element.name == ‘ul’:
for item in element.find_all(‘li’):
# Add list items
doc.add_paragraph(item.get_text(), style=‘List Bullet’)
def add_hyperlink(paragraph, url, text):
“””
Add a hyperlink to a paragraph.
“””
part = paragraph.part
r_id = part.relate_to(url, docx.opc.constants.RELATIONSHIP_TYPE.HYPERLINK, is_external=True)
hyperlink = OxmlElement(‘w:hyperlink’)
hyperlink.set(qn(‘r:id’), r_id,)
new_run = OxmlElement(‘w:r’)
rPr = OxmlElement(‘w:rPr’)
u = OxmlElement(‘w:u’)
u.set(qn(‘w:val’), ‘single’)
rPr.append(u)
u = OxmlElement(‘w:u’)
u.set(qn(‘w:val’), ‘single’)
rPr.append(u)
new_run.append(rPr)
new_run.text = text
hyperlink.append(new_run)
paragraph._p.append(hyperlink)
return hyperlink
def insert_titles_in_text(text, reports):
# Placeholder for inserting the titles
placeholder = “[]”
# Extracting the titles from the reports and formatting them with new lines
titles = “\n”.join([report[‘title’] for report in reports])
# Replacing the placeholder with the titles
updated_text = text.replace(placeholder, titles)
return updated_text
def generate_article_image():
print(“Generating post image”)
image_url = “”
try:
response = client.images.generate(
model=“dall-e-3”,
prompt=“Create an image for a cover of a Mergers and Acquisitions report. The scene is set in a modern, spacious corporate boardroom with a large glass table. On the table, neatly arranged, are documents, pens, and digital tablets showing graphs and financial data. In the foreground, two pairs of hands, one male and one female, are in the middle of a handshake, symbolizing a successful deal. In the background, through a large window, we see a city skyline with skyscrapers, implying a high-powered business environment. The color scheme should be a mix of cool blues and warm golds to give a sense of professionalism and success. Incorporate subtle M&A related icons like pie charts, upward arrows, and company logos in a tasteful manner. Ensure the image is suitable for a formal report, with a focus on clarity and corporate aesthetics.”,
n=1,
size=“1024×1024”
)
image_url = response.data[0].url
except Exception as e:
print(“An error occurred generating the image:”, str(e))
return image_url
def get_unique_posts_from_webz(query):
print(“Fetch posts from Webz.io”)
articles = fetch_articles(query, WEBZ_API_KEY, 100)
filtered_articles = remove_similar_strings(articles)
return filtered_articles
def call_gpt_completion(prompt):
return client.chat.completions.create(
model=“gpt-4-1106-preview”,
max_tokens=4096,
messages=[
{“role”: “user”, “content”: prompt},
]
)
def generate_reports(filtered_articles):
print(“Generating Reports”)
reports = []
for article in filtered_articles:
print(f“Creating report about: {article[‘title’]}”)
prompt = f“””Carefully review the following news article between the [] brackets and determine if there is an explicit discussion about an M&A deal emerging from its content. The article is as follows:
[
{article[‘text’]}
]
If the article explicitly mentions or clearly discuss an M&A (merger & acquisition) deal, generate a detailed report in HTML format. Use <B> tags to highlight the titles of each section and <UL> and <LI> tags for listing items. The report should include the following sections:
<HTML>
<B>Executive Summary</B>
<UL><LI>Summarize the main points of the M&A deal: companies involved, deal size, key dates, high-level analysis of the deal’s rationale and expected outcomes.</LI></UL>
<B>Introduction</B>
<UL><LI>Provide a background information about the companies involved, industry context and market conditions leading up to the M&A.</LI></UL>
<B>Details of the Deal</B>
<UL><LI>Provide a description of the M&A transaction: type, structure, financial terms, timeline, information on deal financing.</LI></UL>
<B>Strategic Rationale</B>
<UL><LI>Provide a strategic reasons behind the M&A, expected benefits for both companies.</LI></UL>
<B>Market Reaction and Analysis</B>
<UL><LI>Discuss the market reaction to the announcement, comparison with similar industry deals.</LI></UL>
<B>Regulatory and Legal Considerations</B>
<UL><LI>Mention any regulatory approvals and antitrust concerns, legal implications and compliance requirements.</LI></UL>
<B>Risk Analysis</B>
<UL><LI>Discuss any potential risks associated with the deal, risk mitigation strategies.</LI></UL>
<B>Financial Analysis</B>
<UL><LI>Review the financial metrics and performance indicators, impact on financial statements.</LI></UL>
<B>Industry and Competitor Impact</B>
<UL><LI>Discuss the effect on the broader industry and market dynamics, impact on competitors.</LI></UL>
<B>Conclusion and Recommendations</B>
<UL><LI>Provide a summary of key findings and insights, recommendations for stakeholders.</LI></UL>
</HTML>
If the article does not explicitly mention or discuss an M&A deal, please respond with: can’t produce report.
“””
try:
response = call_gpt_completion(prompt)
report = {‘text’: ”}
for choice in response.choices:
report[‘text’] += choice.message.content
if “Executive Summary” in report[‘text’]:
report[‘link’] = article[‘link’]
report[‘title’] = article[‘title’]
report[‘published’] = article[‘published’]
reports.append(report)
print(f“Created a report about: {article[‘title’]}”)
else:
print(f“Can’t product report for: {article[‘title’]}”)
if len(reports) == NUM_OF_REPORTS:
break
except Exception as e:
print(“An error occurred:”, str(e))
return reports
def generate_intro(reports):
print(“Generate post intro”)
prompt = “””
Write a paragraph introducing a digest that contains M&A reports about the following titles, don’t elaborate on these titles:
[]
The reports are created automatically by using Webz.io news api and ChatGPT. The reports are generated by calling the Webz.io news API for news articles categorized as “Economy, Business and Finance” related to M&A deals. The matching news articles are then run through a ChatGPT prompt to analyze if the article is indeed about an M&A deal. If so it create a structured report.
“””
prompt = insert_titles_in_text(prompt, reports)
intro = “”
try:
response = call_gpt_completion(prompt)
for choice in response.choices:
intro += choice.message.content
except Exception as e:
print(“An error occurred:”, str(e))
return intro
def generate_title(intro):
print(“Creating a title”)
prompt = “Create a title using the following text as a context:\n” + intro
title_text = “”
try:
response = call_gpt_completion(prompt)
for choice in response.choices:
title_text += choice.message.content
except Exception as e:
print(“An error occurred:”, str(e))
title_text = title_text.strip(” “).strip(‘\”‘)
if title_text.startswith(“Title:”): # Sometimes ChatGPT prefix the title with Title:
return title_text[len(“Title:”):]
return title_text
def create_word_doc(file_name, title_text, image_url, intro, reports):
print(“Saving to word document”)
doc = docx.Document()
# Add a title
title = doc.add_paragraph()
title.style = ‘Title’
title_run = title.add_run(title_text)
title_run.font.size = Pt(24) # Set the font size
title_run.font.name = ‘Arial (Body)’ # Set the font
title.alignment = WD_ALIGN_PARAGRAPH.CENTER # Center align the title
if len(image_url) > 0:
add_image_from_base64(doc, image_url)
doc.add_paragraph(intro)
# Add each report
for report in reports:
p = doc.add_paragraph(style=‘Heading 1’)
add_hyperlink(p, report[‘link’], report[‘title’])
doc.add_paragraph(f“Published on: {report[‘published’]}”)
html_to_word(doc, report[‘text’])
doc.add_paragraph(“””
“””)
for paragraph in doc.paragraphs:
for run in paragraph.runs:
run.font.name = ‘Arial (Body)’
# Save the document
doc.save(file_name)
def main():
image_url = generate_article_image()
filtered_articles = get_unique_posts_from_webz(“””category:”Economy, Business and Finance” num_chars:>1000 language:english published:>now-7d social.facebook.likes:>0 title:acquire”””)
reports = generate_reports(filtered_articles)
intro = generate_intro(reports)
title_text = generate_title(intro)
create_word_doc(“M&A digest.docx”, title_text, image_url, intro, reports)
if __name__ == “__main__”:
main()
