Files
old-svevijesti/pyth/tests/test_scrapingsingle.py
2024-01-29 14:55:20 +01:00

61 lines
2.1 KiB
Python

import unittest
from unittest.mock import patch
import requests
from bs4 import BeautifulSoup
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores.pgvector import PGVector
from openai import OpenAI
import json
from dotenv import load_dotenv
from pyth.get_articles import get_article_links, insert_data, is_similar_data
import os
load_dotenv()
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
client = OpenAI()
embeddings = OpenAIEmbeddings()
already_checked = set()
total_links = set()
collected_news = set()
dlinks = 'http://127.0.0.1:5000/'
class TestIntegration(unittest.TestCase):
def test_integration(self):
link = get_article_links(dlinks,already_checked)
self.assertEqual(len(already_checked), 2)
for link in total_links:
response = requests.get(link)
soup = BeautifulSoup(response.text, 'html.parser')
titles = soup.find_all(['h2', 'h1', 'h3'])
title_text = ' '.join([title.get_text(strip=True) for title in titles])
texts = soup.find_all(['p'])
text_text = ' '.join([text.get_text(strip=True) for text in texts])
completion = client.chat.completions.create(
model="gpt-3.5-turbo",
messages=[
{"role": "system", "content": "Data analytic, Journalist and News reporter"},
{"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."}
]
)
generated_text = completion.choices[0].message.content
response_data = json.loads(generated_text)
title = response_data["title"]
text = response_data["content"]
vector = embeddings.embed_query(generated_text)
self.assertIn("Test Title", title)
self.assertIn("Test Text", text)
self.assertEqual(len(total_links), 2)