import psycopg2
from dotenv import load_dotenv
import os
from openai import OpenAI
from langchain_openai import OpenAIEmbeddings
from db_management import get_specific_data, modify_similar_data, insert_data, preparing_articles, calculate_cosine_similarity, get_titles_links_embeddings
from get_articles import  slice_text_at_2k_tokens
import json
from json_repair import repair_json
from publishing_finals import publish_articles

load_dotenv()

OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
client = OpenAI()
embeddings = OpenAIEmbeddings()

print("Checking for similar!")


def find_and_group_similar_articles(eps=0.5, min_samples=2, threshold=0.95):
    try:
        titles, links, embeddings = get_titles_links_embeddings()

        processed_articles = set()
        grouped_similar_articles = []

        for i, (title1, link1, embedding1) in enumerate(zip(titles, links, embeddings)):
            if (title1, link1) not in processed_articles:
                processed_articles.add((title1, link1))
                group = [(title1, link1)]

                for j, (title2, link2, embedding2) in enumerate(zip(titles, links, embeddings)):
                    if i != j and (title2, link2) not in processed_articles:
                        similarity = calculate_cosine_similarity(embedding1, embedding2)

                        if similarity > threshold:
                            if link1 != link2:
                                processed_articles.add((title2, link2))
                                group.append((title2, link2, embedding2))

                grouped_similar_articles.append(group)
        return grouped_similar_articles

    except psycopg2.Error as e:
        print(f"Error: {e}")
        return []


def processing_articles(articles):
    unique_links = set()
    
    for article in articles:
        a_title, a_link = article[:2] 
        get_data = get_specific_data(a_title)
        text = get_data[0][1]
        link = a_link

        modify_similar_data(f"C: {', '.join(art[0] for art in articles)}", a_title)
        preparing_articles(False, a_title)

        if link not in unique_links:
            unique_links.add(link)

    combined_text = ' '.join(get_specific_data(art[0])[0][1] for art in articles)
    combined_text = slice_text_at_2k_tokens(combined_text)

    if len(unique_links) == 1:
        link = next(iter(unique_links))
    else:
        link = ', '.join(unique_links)
    return combined_text, link


def processing_similar():
    grouped_similar_articles_result = find_and_group_similar_articles()

    if grouped_similar_articles_result:
        for group in grouped_similar_articles_result:
            articles = group

            if len(articles) > 1:
                combined_text, link = processing_articles(articles)
                user_message = (
                    rf"Here are {len(articles)} texts {combined_text}, combine the following texts into a cohesive news, "
                    rf"remove any non-news related to all texts, and provide the cleaned data on Bosnian languageas and return as JSON only with a single 'content' field."
                )

                try:
                    completion = client.chat.completions.create(
                        model="gpt-3.5-turbo",
                        messages=[
                            {"role": "system", "content": "Data analytic, Journalist and News reporter"},
                            {"role": "user", "content": user_message}
                        ])
                    generated_text = repair_json(completion.choices[0].message.content)

                    response_data = json.loads(generated_text)
                    title = articles[0][0]
                    text = response_data["content"]
                    vector = embeddings.embed_query(generated_text)
                    tmpCategory = get_specific_data(title)
                    category = tmpCategory[0][5]

                    

                    insert_data(title, text, link, vector, f"C: {', '.join(art[0] for art in articles)}", category)
                    print(f"Inserting combined: {title} and Category: {category}")

                except Exception as e:
                    print(f"Error: {e}")
                    print(articles[0][0])
                    continue
        else:
            print("Done!.")
    else:
        print("No similar articles found.")


if __name__ == "__main__":
    processing_similar()
    publish_articles()