Delete mentions_dashboard.py

mentions_dashboard.py

@@ -1,155 +0,0 @@
-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
-