import psycopg2
from psycopg2 import sql
from pgvector.psycopg2 import register_vector
from sklearn.metrics.pairwise import cosine_similarity
import numpy as np
import os
from dotenv import load_dotenv
from datetime import datetime ,timedelta


load_dotenv()

host = os.getenv("DB_HOST")
port = os.getenv("DB_PORT")
user = os.getenv("DB_USER")
password = os.getenv("DB_PASSWORD")
dbname = os.getenv("DB_NAME")

conn = psycopg2.connect(
        host=host,
        port=port,
        user=user,
        password=password,
        dbname=dbname
    )

def calculate_cosine_similarity(v1, v2):
    v1_normalized = v1 / np.linalg.norm(v1)
    v2_normalized = v2 / np.linalg.norm(v2)

    similarity = cosine_similarity([v1_normalized], [v2_normalized])[0][0]
    return similarity

def is_similar_data(title, text, link, embedding, threshold=0.98):
    conn = psycopg2.connect(
        host=host,
        port=port,
        user=user,
        password=password,
        dbname=dbname
    )
    cursor = conn.cursor()

    cursor.execute('SELECT title,embedding,link FROM vectorsvevijesti;')
    existing_embeddings = cursor.fetchall()

    for existing_embedding_tuple in existing_embeddings:
        existing_title = existing_embedding_tuple[0]
        existing_embedding = np.array(existing_embedding_tuple[1]).flatten()
        existing_link = existing_embedding_tuple[2]
        similarity = calculate_cosine_similarity(existing_embedding, embedding)
        if similarity > threshold:
            if link != existing_link:
                similar_d = existing_title
                insert_data(title,text,link,embedding,similar_d)
                print(f"Similar data found: \n #{title} \n #{existing_title}")
                print(f"Inserting: #{title} \n")
                similar_d = "NO"
                cursor.close()
                return True
            else:
                print(f"Same source of same article!")
                cursor.close()
                return True

    print(f"Inserting: #{title}")
    cursor.close()
    return False

def get_similar():
    conn = psycopg2.connect(
        host=host,
        port=port,
        user=user,
        password=password,
        dbname=dbname
    )
    cursor = conn.cursor()
    query = '''SELECT title,similar_d FROM vectorsvevijesti WHERE similar_d NOT IN ('NO', 'SOURCE')'''
    cursor.execute(query)
    similar_data = cursor.fetchall()
    cursor.close()
    return similar_data


def insert_data(title, text, link, embedding, similar_d):
    conn = psycopg2.connect(
        host=host,
        port=port,
        user=user,
        password=password,
        dbname=dbname
    )
    c_time = datetime.now()


    cursor = conn.cursor()

    cursor.execute('''
        INSERT INTO vectorsvevijesti (title, text, link, embedding, similar_d, time)
        VALUES (%s, %s, %s, %s, %s ,%s);
    ''', (title, text, link, embedding , similar_d, c_time))

    conn.commit()

    cursor.close()

def get_data():
    conn = psycopg2.connect(
        host=host,
        port=port,
        user=user,
        password=password,
        dbname=dbname
    )
    cursor = conn.cursor()
    query = '''SELECT title,text,link FROM vectorsvevijesti;'''

    cursor.execute(query)
    data = cursor.fetchall()
    cursor.close()
    return data

def modify_similar_data(new_value ,title):

    conn = psycopg2.connect(
        host=host,
        port=port,
        user=user,
        password=password,
        dbname=dbname
    ) 
    cursor = conn.cursor()

    query = '''UPDATE vectorsvevijesti SET similar_d = %s WHERE title = %s '''

    cursor.execute(query, (new_value, title))

    conn.commit()

def get_specific_data(title):
    conn = psycopg2.connect(
        host=host,
        port=port,
        user=user,
        password=password,
        dbname=dbname
    )
    cursor = conn.cursor()
    query = '''SELECT title, text, link, similar_d, embedding FROM vectorsvevijesti WHERE title = %s'''
    cursor.execute(query, (title,))
    
    specific_post = cursor.fetchall()
    cursor.close()
    return specific_post

def get_all_links():
    conn = psycopg2.connect(
        host=host,
        port=port,
        user=user,
        password=password,
        dbname=dbname
    )
    cursor = conn.cursor()
    query = '''SELECT link FROM vectorsvevijesti'''
    cursor.execute(query)

    db_links = {link[0] for link in cursor.fetchall()}
    cursor.close()
    return db_links

def delete_specific(title):
    conn = psycopg2.connect(
        host=host,
        port=port,
        user=user,
        password=password,
        dbname=dbname
    )

    cursor = conn.cursor()
    query = '''DELETE FROM vectorsvevijesti WHERE title = %s'''

    cursor.execute(query,(title,))
    cursor.close()

def cleansing():

    conn = psycopg2.connect(
        host=host,
        port=port,
        user=user,
        password=password,
        dbname=dbname
    )

    day_long = datetime.now() - timedelta(days=1)

    cursor = conn.cursor()

    query = '''DELETE FROM vectorsvevijesti WHERE time < %s'''
    cursor.execute(query,(day_long,))

    conn.commit()
    cursor.close()

def drop_table():
    conn = psycopg2.connect(
        host=host,
        port=port,
        user=user,
        password=password,
        dbname=dbname
    )

    cursor = conn.cursor()

    query = '''DROP TABLE IF EXISTS vectorsvevijesti;'''
    cursor.execute(query)

    conn.commit()
    cursor.close()

def create_db(conn):
    conn = psycopg2.connect(
        host=host,
        port=port,
        user=user,
        password=password,
        dbname=dbname
    )
    cursor = conn.cursor()

    cursor.execute("CREATE EXTENSION IF NOT EXISTS vector")

    register_vector(conn)

    cursor.execute('''
        CREATE TABLE IF NOT EXISTS vectorsvevijesti (
            id bigserial PRIMARY KEY,
            title VARCHAR,
            text VARCHAR,
            link VARCHAR,
            embedding vector(1536),
            similar_d VARCHAR,
            time TIMESTAMP DEFAULT CURRENT_TIMESTAMP
        );
    ''')

    conn.commit()
    cursor.close()
create_db(conn)