Python Code/pdf to summary using OpenAI APIs

import PyPDF2
import re
import openai

# Step 1: Convert the PDF file into a text file using a Python script
def pdf_to_text(pdf_file):
    # Open the PDF file of your choice
    with open(pdf_file, 'rb') as pdf:
        reader = PyPDF2.PdfReader(pdf, strict=False)
        # no_pages = len(reader.pages)
        pdf_text = ""

        for page in reader.pages:
            content = page.extract_text()
            pdf_text += content

    #return the text
    return pdf_text


# Step 2: Slice the 70,000 + words into chunks
def slice_text(text, chunk_size):
  # Split the text into a list of words
  words = re.split('\W+', text)

  # Initialize a list to hold the chunks
  chunks = []

  # Iterate over the list of words
  for i in range(0, len(words), chunk_size):
    # Append a chunk of the specified size to the list
    chunks.append(words[i:i+chunk_size])

  # Return the list of chunks
  return chunks

# Step 3: Summarize each of the chunks
def summarize_chunks(chunks):
  # Initialize a list to hold the summaries
  summaries = []

  # Set the OpenAI API key
  openai.api_key = "API KEY"

  # Iterate over the chunks
  for chunk in chunks:
    i=0
    # Use the OpenAI API to summarize the chunk
    summary = openai.Completion.create(
        engine="text-davinci-002",
        prompt=f"Summarize this text:\n{chunk}",
        max_tokens=1024,
        temperature=0.5,
        top_p=1,
        frequency_penalty=1,
        presence_penalty=1
    )
    i += 1
    # Append the summary to the list
    summaries.append(summary)

  # Return the list of summaries
  return summaries

# Step 4: Merge all of the chunks into one text file
def merge_summaries(summaries):
  # Initialize a string to hold the merged summaries
  merged_summaries = ""

  # Iterate over the summaries
  for summary in summaries:
    # Add the summary to the merged summaries
    merged_summaries += summary #!!!! I get an error here

  # Return the merged summaries
  return merged_summaries

# Step 5: Write a new summary from the merged chunks of text
def write_summary(summary_text, output_file):
  # Use the OpenAI API to generate a summary from the merged text
  summary = openai.Completion.create(
      engine="text-davinci-002",
      prompt=f"Write a summary of this text:\n{summary_text}",
      max_tokens=1024,
      temperature=0.5,
      top_p=1,
      frequency_penalty=1,
      presence_penalty=1
  )

  # Open the output file in write mode
  with open(output_file, 'w') as f:
    # Write the summary to the file
    f.write(summary.text)

# Step 6: Generate key notes from the summary
def generate_key_notes(summary_text):
  # Use the OpenAI API to generate key notes from the summary text
  key_notes = openai.Completion.create(
      engine="text-davinci-002",
      prompt=f"Generate key notes from this text:\n{summary_text}",
      max_tokens=1024,
      temperature=0.5,
      top_p=1,
      frequency_penalty=1,
      presence_penalty=1
  )

  # Return the key notes
  return key_notes.text

# Step 7: Create a step-by-step guide from the key notes
def create_step_by_step_guide(key_notes):
  # Use the OpenAI API to generate a step-by-step guide from the key notes
  step_by_step_guide = openai.Completion.create(
      engine="text-davinci-002",
      prompt=f"Create a step-by-step guide from these key notes:\n{key_notes}",
      max_tokens=1024,
      temperature=0.5,
      top_p=1,
      frequency_penalty=1,
      presence_penalty=1
  )

  # Return the step-by-step guide
  return step_by_step_guide.text

# Step 8: Summarize the notes into the bare essentials of the book
def summarize_to_bare_essentials(summary_text):
  # Use the OpenAI API to summarize the text to the bare essentials
  bare_essentials = openai.Completion.create(
      engine="text-davinci-002",
      prompt=f"Summarize this text to the bare essentials:\n{summary_text}",
      max_tokens=1024,
      temperature=0.5,
      top_p=1,
      frequency_penalty=1,
      presence_penalty=1
  )

  # Return the bare essentials
  return bare_essentials.text

# Step 9: Write a blog post from the notes
def write_blog_post(key_notes, output_file):
  # Use the OpenAI API to generate a blog post from the key notes
  blog_post = openai.Completion.create(
      engine="text-davinci-002",
      prompt=f"Write a blog post from these key notes:\n{key_notes}",
      max_tokens=1024,
      temperature=0.5,
      top_p=1,
      frequency_penalty=1,
      presence_penalty=1
  )

  # Open the output file in write mode
  with open(output_file, 'w') as f:
    # Write the blog post to the file
    f.write(blog_post.text)

# Step 10: Generate some mid-journey prompts from the notes
def generate_mid_journey_prompts(key_notes):
  # Use the OpenAI API to generate mid-journey prompts from the key notes
  prompts = openai.Completion.create(
      engine="text-davinci-002",
      prompt=f"Generate some mid-journey prompts from these key notes:\n{key_notes}",
      max_tokens=1024,
      temperature=0.5,
      top_p=1,
      frequency_penalty=1,
      presence_penalty=1
  )

  # Return the prompts
  return prompts.text

# Read the PDF file
pdf_file = "mlops.pdf"
text = pdf_to_text(pdf_file)

# Slice the text into chunks
chunks = slice_text(text, 500)
print ("Slicing completed. Summarizing chunks...")

# Summarize the chunks
summaries = summarize_chunks(chunks)
print ("Summarizing completed. Merging summaries...")

# Merge the summaries
summary_text = merge_summaries(summaries)

# Write the summary to a file
output_file = "summary.txt"
write_summary(summary_text, output_file)

# Generate key notes from the summary
key_notes = generate_key_notes(summary_text)

# Create a step-by-step guide from the key notes
step_by_step_guide = create_step_by_step_guide(key_notes)

# Generate key notes from the summary
key_notes = generate_key_notes(summary_text)

# Create a step-by-step guide from the key notes
step_by_step_guide = create_step_by_step_guide(key_notes)

# Summarize the notes to the bare essentials
bare_essentials = summarize_to_bare_essentials(summary_text)

# Write a blog post from the notes
output_file = "blog_post.txt"
write_blog_post(key_notes, output_file)

# Generate some mid-journey prompts from the notes
prompts = generate_mid_journey_prompts(key_notes)

# Print the step-by-step guide, bare essentials, and prompts
print(step_by_step_guide)
print(bare_essentials)
print(prompts)