import psycopg2
from openai import OpenAI
from datetime import datetime, timedelta, timezone


client = OpenAI(api_key='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')
import spacy


nlp = spacy.load("en_core_web_sm")
twenty_minutes_ago_utc = datetime.now(timezone.utc) - timedelta(minutes=20)



db_params = {
    'host': 'localhost',
    'port': '5432',
    'database': 'svevijestiweb',
    'user': 'svevijesti',
    'password': 'salmonela pljusti 221 hamo'
}


conn = psycopg2.connect(**db_params)
cursor = conn.cursor()

def convert_text_to_vector(text):
    return nlp(text).vector

def check_similarity_with_gpt3(text1, text2):
    completion = client.chat.completions.create(
        model="gpt-3.5-turbo",
        messages=[
            {"role": "system", "content": "You are a text similarity assistant."},
            {"role": "user", "content": f"Compare the similarity between the following two texts:\n\nText 1: {text1}\nText 2: {text2}\n\nSimilarity:"}
        ]
    )
    similarity_score = completion.choices[0].message.content
    print("Analiza")
    return similarity_score

cursor.execute("SELECT title FROM articles WHERE articles.created_at < %s", (twenty_minutes_ago_utc,))
data_from_database = cursor.fetchall()


for i in range(len(data_from_database)):
    for j in range(i + 1, len(data_from_database)):
        text1 = data_from_database[i][0]
        text2 = data_from_database[j][0]

        vector1 = convert_text_to_vector(text1)
        vector2 = convert_text_to_vector(text2)

        similarity_score = check_similarity_with_gpt3(vector1, vector2 )
        print(similarity_score)
        print("T1",text1)
        print("T2", text2)


        similarity_threshold = 0.8

    if similarity_score > similarity_threshold:
       try:
        cursor.execute("DELETE FROM articles WHERE content = %s", (text2,))
        conn.commit()
        print(f"Deleted rows where title is {text2}")
       except Exception as e:
        conn.rollback()  # Roll back changes if an error occurs
        print(f"Error deleting rows: {e}")



cursor.close()
conn.close()