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from langchain.prompts import ChatPromptTemplate
from langchain.output_parsers import ResponseSchema
from langchain.output_parsers import StructuredOutputParser
from langchain_core.output_parsers import StrOutputParser
from scrap_post import scrappost
import requests
def is_shortened_url(url): # It is checking whether it is a shorten url or regular website url
try:
response = requests.head(url, allow_redirects=True)
final_url = response.url
if final_url != url:
return True
return False
except requests.exceptions.RequestException as e:
print("Error:", e)
return False
def expand_short_url(short_url): # It is converting shorten url to regular url
try:
response = requests.head(short_url, allow_redirects=True)
if response.status_code == 200:
return response.url
else:
print("Error: Short URL couldn't be expanded.")
return None
except requests.exceptions.RequestException as e:
print("Error:", e)
return None
def get_original_url(url):
if is_shortened_url(url):
return expand_short_url(url)
else:
return url
# Below function extract the post only content from complete web page content and parraphrase the extracted post
def paraphrased_post(url,model):
post=scrappost(url)
template=""" Create a paraphrased version of a given LinkedIn post while preserving the core message and tone. Ensure the paraphrased content is clear, engaging, and suitable for professional communication on LinkedIn. Focus on rephrasing the post to enhance readability and broaden its appeal to a diverse audience.
LinkedIn post: {data}
The output should only the paraphrased post.
"""
prompt = ChatPromptTemplate.from_template(template)
chain = prompt | model | StrOutputParser()
phrased_post=chain.invoke({"data":post})
return phrased_post
def generate_details(post_data,model):
template=""" Extract the top three keywords , take aways and highlights from a LinkedIn post in descending order of relevance. Provide only the three most significant keywords that encapsulate the main topic or message of the post.
LinkedIn post: {data}
Keywords:\n\n
Output should only include keywords , take aways and highlights.
"""
prompt = ChatPromptTemplate.from_template(template)
chain = prompt | model | StrOutputParser()
keywords=chain.invoke({"data":post_data})
return keywords
# # Below function extract the details such as keywords , Take aways , highlights and questions
# def extract_data(post_data ,model):
# keywords = ResponseSchema(name="Keywords",
# description="These are the keywords extracted from LinkedIn post",type="list")
# # Take_aways = ResponseSchema(name="Take Aways",
# # description="These are the take aways extracted from LinkedIn post", type= "list")
# # Highlights=ResponseSchema(name="Highlights",
# # description="These are the highlights extracted from LinkedIn post", type= "list")
# response_schema = [
# keywords,
# # Take_aways,
# # Highlights
# ]
# output_parser = StructuredOutputParser.from_response_schemas(response_schema)
# format_instructions = output_parser.get_format_instructions()
# template = """
# You are a helpful keywords extractor from the post of LinkedIn Bot. Your task is to extract relevant keywords in descending order of their scores in a list, means high relevant should be on the top .
# From the following text message, extract the following information:
# text message: {content}
# {format_instructions}
# """
# prompt_template = ChatPromptTemplate.from_template(template)
# messages = prompt_template.format_messages(content=post_data, format_instructions=format_instructions)
# response = model(messages)
# output_dict= output_parser.parse(response.content)
# keywords=output_dict['Keywords'][:3]
# # take_aways=output_dict['Take Aways'][:3]
# # highlights=output_dict['Highlights'][:3]
# return keywords |