mentions_dashboard.py

import time
from datetime import datetime
from collections import defaultdict
from urllib.parse import quote

import matplotlib.pyplot as plt
from transformers import pipeline

from sessions import create_session

import logging

logging.basicConfig(level=logging.DEBUG, format='%(asctime)s - %(levelname)s - %(message)s')


# Load transformer-based sentiment model globally
sentiment_pipeline = pipeline("text-classification", model="tabularisai/multilingual-sentiment-analysis")

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

def classify_sentiment(text):
    try:
        result = sentiment_pipeline(text[:512])  # Limit to 512 chars for transformers
        label = result[0]['label'].upper()
        if label in ['POSITIVE', 'VERY POSITIVE']:
            return 'Positive 👍'
        elif label in ['NEGATIVE', 'VERY NEGATIVE']:
            return 'Negative 👎'
        elif label == 'NEUTRAL':
            return 'Neutral 😐'
        else:
            return 'Unknown'
    except Exception as e:
        return 'Error'

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:
            logging.error(f"❌ Error fetching notifications: {resp.status_code} - {resp.text}")
            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)

    mention_shares = [e.get("generatedActivity") for e in all_notifications if e.get("action") == "SHARE_MENTION"]
    mention_data = []

    logging.info(f"Fetched {len(all_notifications)} total notifications.")



    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_label = classify_sentiment(commentary)
        timestamp = post.get("createdAt", 0)
        dt = datetime.fromtimestamp(timestamp / 1000.0)

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

    # --- HTML rendering ---
    html_parts = [
        "<h2 style='text-align:center;'>📣 Mentions Sentiment Dashboard</h2>"
    ]

    for mention in mention_data:
        short_text = (mention["text"][:200] + "…") if len(mention["text"]) > 200 else mention["text"]
        html_parts.append(f"""
            <div style='border:1px solid #ddd; border-radius:12px; padding:15px; margin:15px; box-shadow:2px 2px 8px rgba(0,0,0,0.05); background:#fafafa;'>
                <p><strong>📅 Date:</strong> {mention["date"].strftime('%Y-%m-%d')}</p>
                <p style='color:#333;'>{short_text}</p>
                <p><strong>Sentiment:</strong> {mention["sentiment"]}</p>
            </div>
        """)

    html_content = "\n".join(html_parts)

    # --- Plotting ---
    from matplotlib.figure import Figure
    fig = Figure(figsize=(12, 6))
    ax = fig.subplots()
    fig.subplots_adjust(bottom=0.2)

    if mention_data:
        # Sort by date
        mention_data.sort(key=lambda x: x["date"])

        date_labels = [m["date"].strftime('%Y-%m-%d') for m in mention_data]
        sentiment_scores = [1 if m["sentiment"] == "Positive 👍" else
                            -1 if m["sentiment"] == "Negative 👎" else
                            0 for m in mention_data]

        ax.plot(date_labels, sentiment_scores, marker='o', linestyle='-', color='#0073b1')
        ax.set_title("📊 Mention Sentiment Over Time")
        ax.set_xlabel("Date")
        ax.set_ylabel("Sentiment Score (1=👍, 0=😐, -1=👎)")
        ax.tick_params(axis='x', rotation=45)
        ax.grid(True, linestyle='--', alpha=0.6)
        ax.set_ylim([-1.2, 1.2])
    else:
        ax.text(0.5, 0.5, "No mention sentiment data available.", 
                ha='center', va='center', transform=ax.transAxes, fontsize=12, color='grey')
        ax.set_xticks([])
        ax.set_yticks([])
        ax.set_title("📊 Mention Sentiment Over Time")

    return html_content, fig, mention_data