import json
import time
import re
import os
from datetime import datetime

from urllib.parse import quote
from requests_oauthlib import OAuth2Session
from textblob import TextBlob
import matplotlib.pyplot as plt

from sessions import create_session


def extract_text_from_commentary(commentary):
    return re.sub(r"{.*?}", "", commentary).strip()


def analyze_sentiment(text):
    return TextBlob(text).sentiment.polarity


def generate_mentions_dashboard(comm_client_id, comm_token_dict):

    org_urn = "urn:li:organization:19010008"
    encoded_urn = quote(org_urn, safe='')

    session = create_session(comm_client_id, token=comm_token_dict)
    session.headers.update({
        "X-Restli-Protocol-Version": "2.0.0"
    })

    base_url = (
        "https://api.linkedin.com/rest/organizationalEntityNotifications"
        "?q=criteria"
        "&actions=List(COMMENT,SHARE_MENTION)"
        f"&organizationalEntity={encoded_urn}"
        "&count=20"
    )

    all_notifications = []
    start = 0

    while True:
        url = f"{base_url}&start={start}"
        resp = session.get(url)
        if resp.status_code != 200:
            break

        data = resp.json()
        elements = data.get("elements", [])
        all_notifications.extend(elements)

        if len(elements) < data.get("paging", {}).get("count", 0):
            break

        start += len(elements)
        time.sleep(0.5)

    # Extract mentions and their share URNs
    mention_shares = [e.get("generatedActivity") for e in all_notifications if e.get("action") == "SHARE_MENTION"]
    mention_data = []

    for share_urn in mention_shares:
        if not share_urn:
            continue
        encoded_share_urn = quote(share_urn, safe='')
        share_url = f"https://api.linkedin.com/rest/posts/{encoded_share_urn}"
        response = session.get(share_url)

        if response.status_code != 200:
            continue

        post = response.json()
        commentary_raw = post.get("commentary", "")
        if not commentary_raw:
            continue

        commentary = extract_text_from_commentary(commentary_raw)
        sentiment = analyze_sentiment(commentary)
        timestamp = post.get("createdAt", 0)
        dt = datetime.fromtimestamp(timestamp / 1000.0)

        mention_data.append({
            "date": dt,
            "text": commentary,
            "sentiment": sentiment
        })

    # Save HTML
    html_parts = ["<h2 style='text-align:center;'>📣 Mentions Sentiment Dashboard</h2>"]
    for mention in mention_data:
        html_parts.append(f"""
            <div style='border:1px solid #ccc; border-radius:10px; padding:15px; margin:10px;'>
                <p><strong>Date:</strong> {mention["date"].strftime('%Y-%m-%d')}</p>
                <p>{mention["text"]}</p>
                <p><strong>Sentiment:</strong> {mention["sentiment"]:.2f}</p>
            </div>
        """)

    html_path = "mentions_dashboard.html"
    with open(html_path, "w", encoding="utf-8") as f:
        f.write("\n".join(html_parts))

    # Plot
    if mention_data:
        dates = [m["date"] for m in mention_data]
        sentiments = [m["sentiment"] for m in mention_data]

        plt.figure(figsize=(10, 5))
        plt.plot(dates, sentiments, marker='o', linestyle='-', color='blue')
        plt.title("Sentiment Over Time")
        plt.xlabel("Date")
        plt.ylabel("Sentiment")
        plt.grid(True)
        plt.tight_layout()
        plt.savefig("mentions_sentiment_plot.png")
        plt.close()

    return html_path