270 lines
9.7 KiB
Python
270 lines
9.7 KiB
Python
from bs4 import BeautifulSoup
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import requests
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from urllib.parse import urljoin
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from openai import OpenAI , APIError
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import os
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from langchain.embeddings import OpenAIEmbeddings
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from vectData import (insert_data ,is_similar_data ,get_similar, get_specific_data,get_all_links,cleansing ,modify_similar_data)
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import json
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from dotenv import load_dotenv
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import tiktoken
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load_dotenv()
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cleansing()
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OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
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client = OpenAI()
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embeddings = OpenAIEmbeddings()
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dlinks = ['https://klix.ba', 'https://srpskainfo.com', 'https://bljesak.info']
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headers = {'User-Agent': 'Mozilla/5.0 (Linux; Android 5.1.1; SM-G928X Build/LMY47X) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/47.0.2526.83 Mobile Safari/537.36'}
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def num_tokens_from_string(string: str, model="gpt-3.5-turbo") -> int:
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encoding = tiktoken.encoding_for_model(model)
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return len(encoding.encode(string))
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def slice_text_at_2k_tokens(text):
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encoding_name = "gpt-3.5-turbo"
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max_tokens = 2000
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encoding = tiktoken.encoding_for_model(encoding_name)
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tokens = encoding.encode(text)
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if len(tokens) <= max_tokens:
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return [text]
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sliced_tokens = tokens[:max_tokens]
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sliced_text = encoding.decode(sliced_tokens)
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return sliced_text
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def replace_with_spaces(text):
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allowed_chars = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyzČčĆćDždžĐ𩹮ž0123456789 "
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cleaned_text = ''.join(char if char in allowed_chars else ' ' for char in text)
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return cleaned_text
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def fix_links(links_set):
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modified_links = set()
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for link in links_set:
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if "www" in link:
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modified_link = link.replace("www.", "")
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modified_links.add(modified_link)
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else:
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modified_links.add(link)
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return modified_links
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total_links = set()
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collected_news = set()
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def get_article_links(url, already_checked):
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response = requests.get(url,headers)
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if response.status_code == 200:
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soup = BeautifulSoup(response.text, 'html.parser')
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articles = soup.find_all('article')
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link_store = []
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for article in articles:
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links = article.find_all('a', href=True)
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for link in links:
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link_value = urljoin(url, link['href'])
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if link_value not in already_checked:
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link_store.append(link_value)
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already_checked.add(link_value)
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return link_store
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already_checked = set()
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for dlink in dlinks:
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temp_links = get_article_links(dlink, already_checked)
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if temp_links:
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total_links.update(temp_links)
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final_links = {item for item in total_links if item}
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db_links = set(get_all_links())
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new_links = final_links - db_links
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final_links = new_links
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final_links = set(final_links)
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final_links = fix_links(final_links)
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if __name__ == '__main__':
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for link in final_links:
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response = requests.get(link,headers)
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soup = BeautifulSoup(response.text, 'html.parser')
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titles = soup.find_all(['h2', 'h1','h3'])
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title_text = ' '.join([title.get_text(strip=True) for title in titles])
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texts = soup.find_all(['p'])
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text_text = ' '.join([text.get_text(strip=True) for text in texts])
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text_text = text_text
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title_text = title_text
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title_text = replace_with_spaces(title_text)
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print(f"Tokens usage: {num_tokens_from_string(text_text, 'gpt-3.5-turbo')}")
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text_text = slice_text_at_2k_tokens(text_text)
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text_text = replace_with_spaces(str(text_text))
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try:
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completion = client.chat.completions.create(
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model="gpt-3.5-turbo",
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messages=[
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{"role": "system", "content": "Data analytic, Journalist and News reporter"},
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{"role": "user", "content": rf"Extract relevant information from the following input: Title: {title_text}, Text: {text_text}. Remove any non-news element related to the current text and title, and provide the cleaned data as a JSON object with 'title' and 'content' fields."}
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]
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)
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generated_text = completion.choices[0].message.content
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generated_text = generated_text
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response_data = json.loads(generated_text)
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title = response_data["title"]
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text = response_data["content"]
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#print("*********************************")
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#print(f"Title: {title}")
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#print("---------------------------------")
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#print(f"Content : {text}")
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#print("*********************************")
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vector = embeddings.embed_query(generated_text)
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if not is_similar_data(title, text, link, vector, threshold=0.98):
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similar_d = "NO"
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insert_data(title, text, link, vector,similar_d)
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except Exception as e:
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print(f"Error in completion: {e}")
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continue
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def comb_similar():
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print("Checking similar")
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similar_article = get_similar()
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grouped_data = {}
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for sa in similar_article:
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if similar_article:
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first_t = get_specific_data(sa[0])
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second_t = get_specific_data(sa[1])
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link_f = first_t[0][2]
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link_s = second_t[0][2]
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f_text = first_t[0][1]
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s_text = second_t[0][1]
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f_title = first_t[0][0]
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s_title = second_t[0][0]
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if f_title in grouped_data:
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grouped_data[f_title].append((f_text, link_f))
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else:
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grouped_data[f_title] = [(f_text, link_f)]
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if s_title in grouped_data:
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grouped_data[s_title].append((s_text, link_s))
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else:
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grouped_data[s_title] = [(s_text, link_s)]
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for title, tuples in grouped_data.items():
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if len(tuples) == 3:
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text1, link1 = tuples[0]
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text2, link2 = tuples[1]
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text3, link3 = tuples[2]
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t1check = num_tokens_from_string(text1)
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t2check = num_tokens_from_string(text2)
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t3check = num_tokens_from_string(text3)
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slice_if_more = t1check,t2check,t3check
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if slice_if_more < 2000:
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combined_text = f"{text1}{text2}{text3}"
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combined_text = slice_text_at_2k_tokens(combined_text)
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user_message = rf"Here is text {combined_text}, combined from 3 sources, filter text, and make news content, return as JSON only with 'content' field"
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if link1 != link2 and link1 != link3 and link2 != link3:
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link = f"{link1} {link2} {link3}"
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else:
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link = link1
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else:
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user_message = rf"Here are 3 texts {text1} {text2} and {text3}, combine the following texts into a cohesive news remove any non-news related to both texts and provide the cleaned data as a JSON only with 'content' field."
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if link1 != link2 and link1 != link3 and link2 != link3:
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link = f"{link1} {link2} {link3}"
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else:
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link = link1
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else:
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ftcheck = num_tokens_from_string(f_text)
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stcheck = num_tokens_from_string(s_text)
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fscomb = ftcheck + stcheck
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if fscomb <2000:
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combined_text = f"{f_text}{s_text}"
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user_message = rf"Here is text {combined_text}, combined from 2 sources, filter text, and make news content, return as JSON only with 'content' field"
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if link_f != link_s:
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link = f"{link_f} {link_s}"
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else:
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link = link_f
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else:
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user_message = rf"Here are 2 texts {f_text} and {s_text}, combine the following texts into a cohesive news remove any non-news related to both texts and provide the cleaned data as a JSON only with 'content' field."
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if link_f != link_s:
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link = f"{link_f} {link_s}"
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else:
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link = link_f
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try:
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completion = client.chat.completions.create(
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model="gpt-3.5-turbo",
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messages=[
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{"role": "system", "content": "Data analytic, Journalist and News reporter"},
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{"role": "user", "content": user_message}
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]
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)
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generated_text = completion.choices[0].message.content
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if similar_article:
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if f_title == s_title:
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print(f_title)
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modify_similar_data(first_t,"SOURCE")
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similar_article.remove(sa)
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print("Modified")
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else:
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print(f"First: {f_title}")
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print(f"Second: {s_title}")
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modify_similar_data(first_t,"SOURCE")
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modify_similar_data(second_t,"SOURCE")
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similar_article.remove(sa)
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print("Modified")
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else:
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print("Similar list is empty")
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response_data = json.loads(generated_text)
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title = f_title
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text = response_data["content"]
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vector = embeddings.embed_query(generated_text)
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if not is_similar_data(title, text, link, vector, threshold=0.98):
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similar_d = "NO"
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insert_data(title, text, link, vector, similar_d)
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except Exception as e:
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print(f"Error in completion: {e}")
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continue
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