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WIP - Streamlit UI, firecrawl - V0.5

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ajaysi
2024-06-12 16:01:46 +05:30
parent ccbaa0e4fa
commit f2aa79264e
12 changed files with 201 additions and 261 deletions
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29
lib/blog_metadata/get_blog_category.py
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@@ -1,29 +0,0 @@
import sys
import os
from loguru import logger
logger.remove()
logger.add(sys.stdout,
colorize=True,
format="<level>{level}</level>|<green>{file}:{line}:{function}</green>| {message}"
)
from ..gpt_providers.text_generation.main_text_generation import llm_text_gen
def get_blog_categories(blog_article):
"""
Function to generate blog categories for given blog content.
"""
prompt = f"""As an expert SEO and content writer, I will provide you with blog content.
Suggest only 2 blog categories which are most relevant to provided blog content,
by identifying the main topic. Also consider the target audience and the
blog's category taxonomy. Only reply with comma separated values.
The blog content is: '{blog_article}'"
"""
logger.info("Generating blog categories for the given blog.")
try:
response = llm_text_gen(prompt)
return response
except Exception as err:
logger.error(f"get_blog_categories:Failed to get response from LLM: {err}")

30
lib/blog_metadata/get_blog_meta_desc.py
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@@ -1,30 +0,0 @@
import sys
import os
from loguru import logger
logger.remove()
logger.add(sys.stdout,
colorize=True,
format="<level>{level}</level>|<green>{file}:{line}:{function}</green>| {message}"
)
from ..gpt_providers.text_generation.main_text_generation import llm_text_gen
def generate_blog_description(blog_content):
"""
Prompt designed to give SEO optimized blog descripton
"""
logger.info("Generating Blog Meta Description for the given blog.")
prompt = f"""As an expert SEO and blog writer, Compose a compelling meta description for the given blog content,
adhering to SEO best practices. Keep it between 150-160 characters.
Provide a glimpse of the content's value to entice readers.
Respond with only one of your best effort and do not include your explanations.
Blog Content: '{blog_content}'"""
try:
response = llm_text_gen(prompt)
return response
except Exception as err:
logger.error(f"Failed to get response from LLM:{err}")
raise err

119
lib/blog_metadata/get_blog_metadata.py
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@@ -1,6 +1,4 @@
import sys
import configparser
import json
import streamlit as st
from loguru import logger
@@ -17,43 +15,84 @@ def blog_metadata(blog_article):
""" Common function to get blog metadata """
logger.info(f"Generating Content MetaData\n")
blog_metadata_prompt = """
As an expert SEO and content writer, I will provide you with blog content.
1. Suggest only 2 blog categories which are most relevant to the provided blog content, by identifying the main topic.
Also consider the target audience and the blog's category taxonomy. Only reply with comma-separated values.
2. Compose a compelling meta description for the given blog content, adhering to SEO best practices.
Keep it between 150-160 characters. Provide a glimpse of the content's value to entice readers.
Respond with only one of your best efforts and do not include your explanations.
3. Write 1 blog title following SEO best practices. Please keep the title concise, not exceeding 60 words.
Respond with only 1 title and no explanations. Negative Keywords: Unveiling, unleash, power of. Don't use such words in your title.
4. Suggest only 2 relevant and specific blog tags for the given blog content. Only reply with comma-separated values.
The blog content is: '{blog_article}'
Please provide the result in the following JSON format:
{
"title": "Your generated blog title",
"meta_description": "Your generated meta description",
"tags": ["tag1", "tag2"],
"categories": ["category1", "category2"]
}
"""
try:
response = llm_text_gen(blog_metadata_prompt)
""" Cleans the response by removing ``` and 'json' strings """
result_json = response.replace("```", "").replace("json", "").strip()
# Convert the cleaned response to JSON
result_json = json.loads(result_json)
except Exception as err:
logger.error(f"Failed to get response from LLM: {err}")
st.error(f"Failed to get response from LLM: {err}")
# Extract the data from the JSON response
blog_title = result_json.get("title")
blog_meta_desc = result_json.get("meta_description")
blog_tags = result_json.get("tags")
blog_categories = result_json.get("categories")
blog_title = generate_blog_title(blog_article)
blog_meta_desc = generate_blog_description(blog_article)
blog_tags = get_blog_tags(blog_article)
blog_categories = get_blog_categories(blog_article)
return blog_title, blog_meta_desc, blog_tags, blog_categories
def generate_blog_title(blog_article):
"""
Given a blog title generate an outline for it
"""
logger.info("Generating blog title.")
prompt = f"""As a SEO expert, I will provide you with a blog content.
Your task is write a SEO optimized and call to action, blog title for given blog content.
Follow SEO best practises to suggest the blog title.
Please keep the titles concise, not exceeding 60 words.
Respond with only the title and no explanations.
Negative Keywords: Unvieling, unleash, power of. Dont use such words in your title.
\nGenerate blog title for this given blog content:\n '{blog_article}' """
try:
response = llm_text_gen(prompt)
return response
except Exception as err:
logger.error(f"Failed to get response from LLM: {err}")
raise err
def generate_blog_description(blog_content):
"""
Prompt designed to give SEO optimized blog descripton
"""
logger.info("Generating Blog Meta Description for the given blog.")
prompt = f"""As an expert SEO and blog writer, Compose a compelling meta description for the given blog content,
adhering to SEO best practices. Keep it between 150-160 characters.
Provide a glimpse of the content's value to entice readers.
Respond with only one of your best effort and do not include your explanations.
Blog Content: '{blog_content}'"""
try:
response = llm_text_gen(prompt)
return response
except Exception as err:
logger.error(f"Failed to get response from LLM:{err}")
raise err
def get_blog_categories(blog_article):
"""
Function to generate blog categories for given blog content.
"""
prompt = f"""As an expert SEO and content writer, I will provide you with blog content.
Suggest only 2 blog categories which are most relevant to provided blog content,
by identifying the main topic. Also consider the target audience and the
blog's category taxonomy. Only reply with comma separated values.
The blog content is: '{blog_article}'"
"""
logger.info("Generating blog categories for the given blog.")
try:
response = llm_text_gen(prompt)
return response
except Exception as err:
logger.error(f"get_blog_categories:Failed to get response from LLM: {err}")
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].
prompt = f"""As an expert SEO and blog writer, suggest only 2 relevant and specific blog tags
for the given blog content. Only reply with comma separated values.
Blog content: {blog_article}."""
logger.info("Generating Blog tags for the given blog post.")
try:
response = llm_text_gen(prompt)
return response
except Exception as err:
logger.error(f"Failed to get response from LLM: {err}")
raise err

53
lib/blog_metadata/get_blog_title.py
View File

@@ -1,53 +0,0 @@
import os
import sys
from loguru import logger
logger.remove()
logger.add(sys.stdout,
colorize=True,
format="<level>{level}</level>|<green>{file}:{line}:{function}</green>| {message}"
)
from ..gpt_providers.text_generation.main_text_generation import llm_text_gen
def generate_blog_title(blog_article, keywords=None, example_titles=None, num_titles=1):
"""
Given a blog title generate an outline for it
"""
prompt = ''
logger.info("Generating blog title.")
if not keywords and not example_titles:
prompt = f"""As a SEO expert, I will provide you with a blog content.
Your task is write a SEO optimized and call to action, blog title for given blog content.
Follow SEO best practises to suggest the blog title.
Please keep the titles concise, not exceeding 60 words.
Respond with only {num_titles} title and no explanations.
Negative Keywords: Unvieling, unleash, power of. Dont use such words in your title.
Generate {num_titles} blog title for this given blog content:\n '{blog_article}' """
elif keywords and example_titles:
prompt = f"""As a SEO expert, I will provide you with my blog keywords and example titles.
Your task is to write {num_titles} blog title.
Ensure that your blog titles will help in competing against given example titles.
Follow SEO best practises to suggest the blog title.
Please keep the titles concise, not exceeding 60 words.
Respond with only {num_titles} title and no explanations.
Negative Keywords: Unvieling, unleash, power of. Dont use such words in your title.
Blog Keywords: '{keywords}'
Example Titles: '{example_titles}'
"""
elif not example_titles:
prompt = prompt = f"""As a SEO expert, I will provide you with my blog article.
Your task is to write {num_titles} blog title.
Follow SEO best practises to suggest the blog title.
Please keep the titles concise, not exceeding 60 words.
Respond with only {num_titles} title and no explanations.
Negative Keywords: Unvieling, unleash, power of. Dont use such words in your title.
Blog Article: '{keywords}'
"""
try:
response = llm_text_gen(prompt)
return response
except Exception as err:
logger.error(f"Failed to get response from LLM: {err}")
raise err

29
lib/blog_metadata/get_tags.py
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@@ -1,29 +0,0 @@
import sys
import os
from loguru import logger
logger.remove()
logger.add(sys.stdout,
colorize=True,
format="<level>{level}</level>|<green>{file}:{line}:{function}</green>| {message}"
)
from ..gpt_providers.text_generation.main_text_generation import llm_text_gen
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].
gpt_providers = os.environ["GPT_PROVIDER"]
prompt = f"""As an expert SEO and blog writer, suggest only 2 relevant and specific blog tags
for the given blog content. Only reply with comma separated values.
Blog content: {blog_article}."""
logger.info("Generating Blog tags for the given blog post.")
try:
response = llm_text_gen(prompt)
return response
except Exception as err:
logger.error(f"Failed to get response from LLM: {err}")
raise err