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Improved, refactored, added options, prompts, jekyll website

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This commit is contained in:
AjaySi
2023-10-24 19:49:31 +05:30
parent 2860345aaf
commit eb87847124
78 changed files with 14455 additions and 106 deletions
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284
lib/get_text_response.py
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@@ -8,16 +8,19 @@
########################################################################
import json
import os
import datetime #I wish
import openai
from tqdm import tqdm, trange
import time
import re
from textwrap import dedent
from .gpt_providers.openai_gpt_provider import openai_chatgpt
def generate_detailed_blog(num_blogs, blog_keywords, niche):
def generate_detailed_blog(num_blogs, blog_keywords, niche, num_subtopics):
"""
This function will take a blog Topic to first generate sections for it
and then generate content for each section.
@@ -34,34 +37,44 @@ def generate_detailed_blog(num_blogs, blog_keywords, niche):
# Use to store the blog in a string, to save in a *.md file.
blog_markdown_str = ""
# TBD: Check if the generated topics are equal to what user asked.
blog_topic_arr = generate_blog_topics(blog_keywords, num_blogs, niche)
print(f"Generated Blog Topics:---- {blog_topic_arr}")
print(f"Generated Blog Topics:---- {blog_topic_arr}\n")
# For each of blog topic, generate content.
for a_blog_topic in blog_topic_arr:
# if md/html
blog_markdown_str = "# " + a_blog_topic + "\n"
blog_markdown_str = "# " + a_blog_topic.replace('"', '') + "\n\n"
# Get the introduction specific to blog title and sub topics.
tpc_outlines = generate_topic_outline(a_blog_topic)
tpc_outlines = generate_topic_outline(a_blog_topic, num_subtopics)
blog_intro = get_blog_intro(a_blog_topic, tpc_outlines)
blog_markdown_str = blog_markdown_str + "### Introduction" + "\n" + f"{blog_intro}" + "\n"
print(f"The intro is:\n {blog_intro}")
blog_markdown_str = blog_markdown_str + "### Introduction" + "\n\n" + f"{blog_intro}" + "\n\n"
# Now, for each blog we have sub topic. Generate content for each of the sub topic.
for a_outline in tpc_outlines:
sub_topic_content = generate_topic_content(blog_keywords, a_outline)
blog_markdown_str = blog_markdown_str + "\n" + f"\n{sub_topic_content}" + "\n"
print(f"Generating content for sub-topic: {a_outline}")
# a_outline is sub topic heading, hence part ToC also.
blog_markdown_str = blog_markdown_str + "\n\n" + f"### {a_outline}" + "\n\n"
blog_markdown_str = blog_markdown_str + "\n" + f"\n {sub_topic_content}" + "\n\n"
blog_markdown_str = blog_markdown_str + "\n" + "-------------------------" + "\n"
# Get the Conclusion of the blog, by passing the generated blog.
blog_conclusion = get_blog_conclusion(blog_markdown_str)
blog_markdown_str = blog_markdown_str + "# Conclusion" + "\n" + f"{blog_conclusion}" + "\n"
blog_markdown_str = blog_markdown_str + "### Conclusion" + "\n" + f"{blog_conclusion}" + "\n"
# print/check the final blog content.
print(f"Final blog content: {blog_markdown_str}")
# Save the blog content as a .md file. Markdown or HTML ?
save_blog_to_file(blog_markdown_str)
blog_meta_desc = generate_blog_description(blog_markdown_str)
print(f"\nGet the blog meta description:{blog_meta_desc}")
blog_tags = get_blog_tags(blog_markdown_str)
print(f"\nBlog tags for generated content: {blog_tags}")
blog_categories = get_blog_categories(blog_markdown_str)
print(f"Generated blog categories: {blog_categories}")
# TBD: Save the blog content as a .md file. Markdown or HTML ?
save_blog_to_file(blog_markdown_str, a_blog_topic, blog_meta_desc, blog_tags, blog_categories)
exit(1)
@@ -82,24 +95,25 @@ def generate_blog_topics(blog_keywords, num_blogs, niche):
one for generating unique blog content.
Ex: Generate SEO optimized blog topics on given keywords
"""
# Get more keywords, based on user given keywords.
prompt = f"""As an SEO specialist and blog content writer, please write {num_blogs} catchy
and SEO-friendly blog topics on {blog_keywords}. The blog title must be less than 80 characters.
"""
# Beware of keywords stuffing, clustering, semantic should help avoid.
more_keywords = get_related_keywords(num_blogs, blog_keywords, niche)
# f"including the following keywords: {more_keywords}."
prompt = ("As an SEO specialist and blog content writer, "
f"please write {num_blogs} catchy and SEO-friendly blog topics on {blog_keywords},"
f"including the following keywords: {more_keywords}."
)
print(f"prompt used for blog titles: {prompt}")
if num_blogs > 5:
# Get more keywords, based on user given keywords.
more_keywords = get_related_keywords(num_blogs, blog_keywords, niche)
prompt = prompt + """Use the following keywords wisely, without keyword stuffing: {more_keywords}"""
print(f"prompt used for blog titles: {prompt}\n")
# Calculate the max tokens based on the number of blogs
max_tokens = min(1000, num_blogs * 100)
try:
response = openai_chatgpt(
prompt,
model="text-davinci-003",
temperature=0.9,
temperature=0.1,
max_tokens=max_tokens,
top_p=0.9,
top_p=1,
n=1
)
topic_list = extract_key_text(response)
@@ -108,23 +122,23 @@ def generate_blog_topics(blog_keywords, num_blogs, niche):
SystemError(f"Error in generating blog topics: {err}")
def generate_topic_outline(blog_title):
def generate_topic_outline(blog_title, num_subtopics):
"""
Given a blog title generate an outline for it
"""
# TBD: Remove hardcoding, make dynamic
prompt = ("As a technical writer and SEO expert, suggest 7 beginner-friendly and helpful sub-topics"
f"for the blog title '{blog_title}',"
"Include 2 sub topics on related long-tailed keywords and "
"2 sub topics on most popular questions."
)
prompt = f"""As a SEO expert, suggest only {num_subtopics}
beginner-friendly and insightful sub topic for the blog title: {blog_title}.
"""
# The suggested {num_subtopics} outline should include few long-tailed keywords and most popular questions.
# TBD: Include --niche
print(f"prompt used for blog title Outline :{prompt}")
# TBD: Add logic for which_provider and which_model
response = openai_chatgpt(
prompt,
model="text-davinci-003",
temperature=0.7,
max_tokens=1000,
temperature=0.1,
max_tokens=100,
top_p=0.9,
n=1
)
@@ -139,9 +153,13 @@ def generate_topic_content(blog_keywords, sub_topic):
For each of given topic generate content for it.
"""
# The outline should contain various subheadings and include the starting sentence for each section.
prompt = (f"As a professional writer and topic authority on '{blog_keywords}',"
f"craft a captivating, inviting and factual (no more than 700 characters) blog content on {sub_topic}."
f"Use bulleit points and other readibility enhancers."
# TBD: Depending on the usecase 'Voice and style' will change to professional etc.
prompt = (f"As a professional blogger and topic authority on '{blog_keywords}',"
f"craft factual (no more than 700 characters) blog content on {sub_topic}."
"Your response should reflect Experience, Expertise, Authoritativeness, and Trustworthiness from content."
"Voice and style guide: Write in a professional manner, giving enlightening details and reasons."
"Use natural language and phrases that a real person would use: in normal conversations."
"Format your response using markdown. Use headings, subheadings, bullet points, and bold to organize the information."
)
try:
response = openai_chatgpt(prompt)
@@ -167,16 +185,16 @@ def get_blog_intro(blog_title, blog_topics):
"""
Generate blog introduction as per title and sub topics
"""
prompt = (f"As a professional writer, craft a captivating, inviting, and concise (no more than 550 characters)"
f"introduction for the blog titled '{blog_title}' with the following sub-topics: '{blog_topics}'"
f"The introduction should compel readers to delve deeper into the blog post."
)
prompt = f"""As a professional writer, craft a captivating, inviting, and concise (no more than 550 characters).
The introduction should compel readers to delve deeper into the blog post,
titled: {blog_title} with the following sub-topics: {blog_topics}
"""
try:
# TBD: Add logic for which_provider and which_model
response = openai_chatgpt(
prompt,
model="text-davinci-003",
temperature=0.7,
temperature=0.1,
max_tokens=1000,
top_p=0.9,
n=1
@@ -193,15 +211,16 @@ def get_blog_conclusion(blog_content):
"""
Accepts a blog content and concludes it.
"""
prompt = ("As an expert SEO and blog writer, please conclude the given blog providing vital take aways,"
"summarise key points (no more than 300 characters). The blog content: '{blog_content}'"
)
prompt = f"""As an expert SEO and blog writer, please conclude the given blog providing vital take aways,
summarise key points (no more than 300 characters) in bullet points. The blog content: {blog_content}
"""
print(f"Generating blog conclusion iwth prompt: {prompt}")
try:
# TBD: Add logic for which_provider and which_model
response = openai_chatgpt(
prompt,
model="text-davinci-003",
temperature=0.9,
temperature=0.1,
max_tokens=450,
top_p=0.7,
n=1
@@ -214,15 +233,37 @@ def get_blog_conclusion(blog_content):
SystemError(f"Error in generating blog conclusion: {err}")
def generate_blog_description():
def generate_blog_description(blog_content):
"""
Prompt designed to give SEO optimized blog descripton
"""
# Suggest keywords that I should include in my meta description for my blog post on [topic]
# I want to generate high CTR meta and keyword rich meta title and meta descriptions in text format.
# My keywords are – [keyword 1], [keyword 2], [keyword 3]
# Suggest a meta description for the content above, make it user-friendly
# and with a call to action, include the keyword [keyword].
prompt = f"""As an expert SEO and blog writer, write meta description for given blog content.
The description should be between 150 and 160 characters long, uses strong, active verbs,
avoids using all caps or excessive punctuation, and is relevant to the blog content.
It should be engaging and encourages users to click on the link.
The blog content: {blog_content}"""
pass
try:
# TBD: Add logic for which_provider and which_model
response = openai_chatgpt(
prompt,
model="text-davinci-003",
temperature=0.1,
max_tokens=450,
top_p=0.7,
n=1
)
text_values = []
for choice in response["choices"]:
text_values.extend(choice["text"].split("\n"))
return (' '.join([element for element in text_values if element]))
except Exception as err:
SystemError(f"Error in generating blog description: {err}")
def get_blog_tags(blog_article):
@@ -230,33 +271,101 @@ def get_blog_tags(blog_article):
Function to suggest tags for the given blog content
"""
# Suggest at least 5 tags for the following blog post [Enter your blog post text here].
pass
prompt = f"""As an expert SEO and blog writer, suggest 3 to 5 relevant, specific,
and popular tags that are unique and consistent to improve the visibility
and discoverability of following blog content: {blog_article}"
"""
try:
# TBD: Add logic for which_provider and which_model
response = openai_chatgpt(
prompt,
model="text-davinci-003",
temperature=0.1,
max_tokens=450,
top_p=0.7,
n=1
)
text_values = []
for choice in response["choices"]:
text_values.extend(choice["text"].split("\n"))
return (' '.join([element for element in text_values if element]))
except Exception as err:
SystemError(f"Error in generating blog tags: {err}")
def get_long_tailed_keywords(blog_article):
def get_blog_categories(blog_article):
"""
Function to get long tailed keywords for the blog article.
Function to generate blog categories for given blog content.
"""
# Want you to generate a list of long-tail keywords that are related
# to the following blog post [Enter blog post text here]
pass
prompt = f"""As an expert SEO and content writer, Suggest 2-3 blog categories by identifying
the main topic, most relevant categories, considering the target
audience and the blog's category taxonomy for the following blog content: {blog_article}"
"""
try:
# TBD: Add logic for which_provider and which_model
response = openai_chatgpt(
prompt,
model="text-davinci-003",
temperature=0.1,
max_tokens=100,
top_p=0.7,
n=1
)
text_values = []
for choice in response["choices"]:
text_values.extend(choice["text"].split("\n"))
return (' '.join([element for element in text_values if element]))
except Exception as err:
SystemError(f"Error in generating blog categories: {err}")
def save_blog_to_file(blog_content, file_type="md"):
def save_blog_to_file(blog_content, blog_title,
blog_meta_desc, blog_tags, blog_categories, file_type="md"
):
""" Common function to save the generated blog to a file.
arg: file_type can be md or html
"""
output_path = "../generated_blogs"
# TBD: This can come from config file.
output_path = "pseo_website/_posts/"
output_path = os.path.join(os.getcwd(), output_path)
# Convert the spaces in blog_title with dash
regex = re.compile('[^a-zA-Z0-9- ]')
blog_title = regex.sub('', blog_title)
blog_title = re.sub('--+', '-', blog_title)
blog_title = blog_title.replace(' ', '-')
if not os.path.exists(output_path):
# If the directory does not exist, create it
os.makedirs(output_path)
#os.makedirs(output_path)
print("Error: Blog output directory is set to {output_path}, which Does Not Exist.")
output_today = os.path.join(output_path, f'{datetime.date.today().strftime("%d-%m-%y")}')
if not os.path.exists(output_today):
os.makedirs(output_today)
else:
with open(f"{output_today}/{blog_title}.md", "w") as f:
f.write(blog_content)
if file_type in "md":
# fill the Front Matter as below at the top of the post: https://jekyllrb.com/docs/front-matter/
# date: YYYY-MM-DD HH:MM:SS +/-TTTT
formatted_date = f"{datetime.datetime.now():%Y-%m-%d %H:%M:%S %z}"
blog_frontmatter = """---
title: {blog_title}
date: {formatted_date}
categories: [{blog_categories}]
tags: [{blog_tags}]
description: {blog_meta_desc}
img_path: '/posts/20180809'
---\n\n"""
# Create a new file named YYYY-MM-DD-TITLE.EXTENSION and put it in the _posts of the root directory.
# Please note that the EXTENSION must be one of md or markdown
blog_output_path = os.path.join(
output_path,
f"{datetime.date.today().strftime('%Y-%m-%d')}-{blog_title}.md"
)
# Save the generated blog content to a file.
try:
with open(blog_output_path, "w") as f:
f.write(dedent(blog_frontmatter))
f.write(blog_content)
except Exception as e:
raise Exception(f"Failed to write blog content: {e}")
print(f"\nSuccessfully saved and Posted blog at: {blog_output_path,}\n")
def extract_key_text(json_data):
@@ -311,36 +420,39 @@ def get_related_keywords(num_blogs, keywords, niche):
f" semantically related keywords and entities for the topic of {keywords} that are used"
" in high-quality content and relevant to my competitors."
)
# TBD: Add logic for which_provider and which_model
response = openai_chatgpt(
prompt,
model="text-davinci-003",
temperature=0.7,
max_tokens=100,
top_p=0.9,
n=10
)
try:
# TBD: Add logic for which_provider and which_model
response = openai_chatgpt(
prompt,
model="text-davinci-003",
temperature=0.7,
max_tokens=100,
top_p=0.9,
n=10
)
# Extract the keywords from the response
keywords = []
for choice in response.choices:
# Split the response into words
words = choice.text.split(" ")
# Extract the keywords from the response
keywords = []
for choice in response.choices:
# Split the response into words
words = choice.text.split(" ")
# Add the words to the list of keywords
for text in words:
# Remove digits
text = re.sub(r'\d', '', text)
# Add the words to the list of keywords
for text in words:
# Remove digits
text = re.sub(r'\d', '', text)
# Remove special characters
text = re.sub(r'[^\w\s]', '', text)
# Remove newline characters
text = text.replace('\n', '')
# Remove special characters
text = re.sub(r'[^\w\s]', '', text)
# Remove newline characters
text = text.replace('\n', '')
keywords.append(text)
keywords.append(text)
# Remove any duplicate keywords
keywords = set(keywords)
# Remove any duplicate keywords
keywords = set(keywords)
# Return the list of keywords
return (' '.join(keywords))
# Return the list of keywords
return (' '.join(keywords))
except Exception as err:
SystemError(f"Error in getting related keywords.")