analytics_fetch_and_rendering.py

import json
import requests
from datetime import datetime, timezone, timedelta
import matplotlib.pyplot as plt
import gradio as gr
import traceback
import html

from sessions import create_session
from error_handling import display_error

from Data_Fetching_and_Rendering import fetch_posts_and_stats
from mentions_dashboard import generate_mentions_dashboard

import logging

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

API_V2_BASE = 'https://api.linkedin.com/v2'
API_REST_BASE = 'https://api.linkedin.com/rest'

def extract_follower_gains(data):
    elements = data.get("elements", [])
    if not elements:
        return []

    results = []
    for item in elements:
        start_timestamp = item.get("timeRange", {}).get("start")
        if not start_timestamp:
            continue

        try:
            date_str = datetime.fromtimestamp(start_timestamp / 1000, tz=timezone.utc).strftime('%Y-%m')
        except Exception:
            continue

        gains = item.get("followerGains", {})
        results.append({
            "date": date_str,
            "organic": gains.get("organicFollowerGain", 0) or 0,
            "paid": gains.get("paidFollowerGain", 0) or 0
        })

    return sorted(results, key=lambda x: x['date'])

def fetch_analytics_data(client_id, token):
    if not token:
        raise ValueError("comm_token is missing.")

    token_dict = token if isinstance(token, dict) else {'access_token': token, 'token_type': 'Bearer'}
    session = create_session(client_id, token=token_dict)

    try:
        org_urn, org_name = "urn:li:organization:19010008", "GRLS"

        count_url = f"{API_V2_BASE}/networkSizes/{org_urn}?edgeType=CompanyFollowedByMember"
        follower_count = session.get(count_url).json().get("firstDegreeSize", 0)

        start = datetime.now(timezone.utc) - timedelta(days=365)
        start = start.replace(day=1, hour=0, minute=0, second=0, microsecond=0)
        start_ms = int(start.timestamp() * 1000)

        gains_url = (
            f"{API_REST_BASE}/organizationalEntityFollowerStatistics"
            f"?q=organizationalEntity&organizationalEntity={org_urn}"
            f"&timeIntervals.timeGranularityType=MONTH"
            f"&timeIntervals.timeRange.start={start_ms}"
        )
        gains_data = session.get(gains_url).json()
        gains = extract_follower_gains(gains_data)

        return org_name, follower_count, gains

    except requests.exceptions.RequestException as e:
        status = getattr(e.response, 'status_code', 'N/A')
        msg = f"Failed to fetch LinkedIn analytics (Status: {status})."
        raise ValueError(msg) from e
    except Exception as e:
        raise ValueError("Unexpected error during LinkedIn analytics fetch.") from e

def plot_follower_gains(data):
    plt.style.use('seaborn-v0_8-whitegrid')

    if not data:
        fig, ax = plt.subplots(figsize=(10, 5))
        ax.text(0.5, 0.5, 'No follower gains data.', ha='center', va='center', transform=ax.transAxes)
        ax.set_title('Monthly Follower Gains')
        ax.set_xticks([]); ax.set_yticks([])
        return fig

    dates = [d['date'] for d in data]
    organic = [d['organic'] for d in data]
    paid = [d['paid'] for d in data]

    fig, ax = plt.subplots(figsize=(12, 6))
    ax.plot(dates, organic, label='Organic', marker='o', color='#0073b1')
    ax.plot(dates, paid, label='Paid', marker='x', linestyle='--', color='#d9534f')
    ax.set(title='Monthly Follower Gains', xlabel='Month', ylabel='New Followers')
    ax.tick_params(axis='x', rotation=45)
    ax.legend()
    plt.tight_layout()
    return fig

def plot_growth_rate(data, total):
    if not data:
        fig, ax = plt.subplots(figsize=(10, 5))
        ax.text(0.5, 0.5, 'No data for growth rate.', ha='center', va='center', transform=ax.transAxes)
        ax.set_title('Growth Rate (%)')
        ax.set_xticks([]); ax.set_yticks([])
        return fig

    dates = [d['date'] for d in data]
    gains = [d['organic'] + d['paid'] for d in data]

    history = []
    current = total
    for g in reversed(gains):
        history.insert(0, current)
        current -= g

    rates = [((history[i] - history[i-1]) / history[i-1] * 100 if history[i-1] else 0) for i in range(1, len(history))]

    fig, ax = plt.subplots(figsize=(12, 6))
    ax.plot(dates[1:], rates, label='Growth Rate (%)', marker='o', color='green')
    ax.set(title='Monthly Growth Rate (%)', xlabel='Month', ylabel='Growth %')
    ax.tick_params(axis='x', rotation=45)
    ax.legend()
    plt.tight_layout()
    return fig

def compute_monthly_avg_engagement_rate(posts):
    from collections import defaultdict
    import statistics

    if not posts:
        return []

    monthly_data = defaultdict(lambda: {"engagement_sum": 0, "post_count": 0, "impression_total": 0})

    for post in posts:
        try:
            month = post["when"][:7]  # Format: YYYY-MM
            likes = post.get("likes", 0)
            comments = post.get("comments", 0)
            shares = post.get("shares", 0)
            clicks = post.get("clicks", 0)
            impressions = post.get("impressions", 0)

            engagement = likes + comments + shares + clicks
            monthly_data[month]["engagement_sum"] += engagement
            monthly_data[month]["post_count"] += 1
            monthly_data[month]["impression_total"] += impressions
        except Exception:
            continue

    results = []
    for month in sorted(monthly_data.keys()):
        data = monthly_data[month]
        if data["post_count"] == 0 or data["impression_total"] == 0:
            rate = 0
        else:
            avg_impressions = data["impression_total"] / data["post_count"]
            rate = (data["engagement_sum"] / (data["post_count"] * avg_impressions)) * 100
        results.append({"month": month, "engagement_rate": round(rate, 2)})

    return results

def plot_avg_engagement_rate(data):
    import matplotlib.pyplot as plt

    if not data:
        fig, ax = plt.subplots(figsize=(10, 5))
        ax.text(0.5, 0.5, 'No engagement data.', ha='center', va='center', transform=ax.transAxes)
        ax.set_title('Average Post Engagement Rate (%)')
        ax.set_xticks([]); ax.set_yticks([])
        return fig

    months = [d["month"] for d in data]
    rates = [d["engagement_rate"] for d in data]

    fig, ax = plt.subplots(figsize=(12, 6))
    ax.plot(months, rates, label="Engagement Rate (%)", marker="s", color="#ff7f0e")
    ax.set(title="Average Post Engagement Rate (%)", xlabel="Month", ylabel="Engagement Rate %")
    ax.tick_params(axis='x', rotation=45)
    ax.legend()
    plt.tight_layout()
    return fig

def compute_post_interaction_metrics(posts):
    from collections import defaultdict

    if not posts:
        return []

    monthly_stats = defaultdict(lambda: {
        "comments": 0,
        "shares": 0,
        "clicks": 0,
        "likes": 0,
        "posts": 0
    })

    for post in posts:
        try:
            month = post["when"][:7]  # YYYY-MM
            monthly_stats[month]["comments"] += post.get("comments", 0)
            monthly_stats[month]["shares"] += post.get("shares", 0)
            monthly_stats[month]["clicks"] += post.get("clicks", 0)
            monthly_stats[month]["likes"] += post.get("likes", 0)
            monthly_stats[month]["posts"] += 1
        except Exception:
            continue

    results = []
    for month in sorted(monthly_stats.keys()):
        stats = monthly_stats[month]
        total_engagement = stats["comments"] + stats["shares"] + stats["clicks"] + stats["likes"]
        posts_count = stats["posts"] or 1  # Avoid division by zero

        results.append({
            "month": month,
            "comments_per_post": round(stats["comments"] / posts_count, 2),
            "shares_per_post": round(stats["shares"] / posts_count, 2),
            "clicks_per_post": round(stats["clicks"] / posts_count, 2),
            "comment_share_of_engagement": round((stats["comments"] / total_engagement) * 100 if total_engagement else 0, 2)
        })
        logging.info(f"this are the inter<ction results {results}")
    return results

def plot_interaction_metrics(data):
    if not data:
        fig, ax = plt.subplots(figsize=(10, 5))
        ax.text(0.5, 0.5, 'No interaction data.', ha='center', va='center', transform=ax.transAxes)
        ax.set_title('Post Interaction Metrics')
        ax.set_xticks([]); ax.set_yticks([])
        return fig

    months = [d["month"] for d in data]
    comments_pp = [d["comments_per_post"] for d in data]
    shares_pp = [d["shares_per_post"] for d in data]
    clicks_pp = [d["clicks_per_post"] for d in data]
    comment_share = [d["comment_share_of_engagement"] for d in data]

    fig, axes = plt.subplots(nrows=4, ncols=1, figsize=(12, 10), sharex=True)
    fig.suptitle("Post Interaction Metrics", fontsize=16)

    axes[0].plot(months, comments_pp, marker="o", color="#1f77b4")
    axes[0].set_ylabel("Comments/Post")
    axes[0].grid(True)

    axes[1].plot(months, shares_pp, marker="s", color="#ff7f0e")
    axes[1].set_ylabel("Shares/Post")
    axes[1].grid(True)

    axes[2].plot(months, clicks_pp, marker="^", color="#2ca02c")
    axes[2].set_ylabel("Clicks/Post")
    axes[2].grid(True)

    axes[3].plot(months, comment_share, marker="x", linestyle="--", color="#d62728")
    axes[3].set_ylabel("Comment Share (%)")
    axes[3].set_xlabel("Month")
    axes[3].grid(True)

    plt.xticks(rotation=45)
    plt.tight_layout(rect=[0, 0, 1, 0.96])  # Leave space for suptitle
    return fig

from collections import defaultdict
import matplotlib.pyplot as plt

def compute_eb_content_ratio(posts):
    if not posts:
        return []

    monthly_counts = defaultdict(lambda: {"eb_count": 0, "total": 0})

    for post in posts:
        try:
            month = post["when"][:7]  # YYYY-MM
            category = post.get("category", "None")
            monthly_counts[month]["total"] += 1
            if category and category.strip() != "None":
                monthly_counts[month]["eb_count"] += 1
        except Exception:
            continue

    results = []
    for month in sorted(monthly_counts.keys()):
        data = monthly_counts[month]
        ratio = (data["eb_count"] / data["total"]) * 100 if data["total"] else 0
        results.append({"month": month, "eb_ratio": round(ratio, 2)})

    return results

def plot_eb_content_ratio(data):
    if not data:
        fig, ax = plt.subplots(figsize=(10, 5))
        ax.text(0.5, 0.5, 'No EB content data.', ha='center', va='center', transform=ax.transAxes)
        ax.set_title('EB Content Ratio (%)')
        ax.set_xticks([]); ax.set_yticks([])
        return fig

    months = [d["month"] for d in data]
    ratios = [d["eb_ratio"] for d in data]

    fig, ax = plt.subplots(figsize=(12, 6))
    ax.plot(months, ratios, label="EB Content Ratio (%)", marker="o", color="#2ca02c")
    ax.set(title="Monthly EB Content Ratio (%)", xlabel="Month", ylabel="EB Content %")
    ax.tick_params(axis='x', rotation=45)
    ax.legend()
    plt.tight_layout()
    return fig

def compute_mention_metrics(mention_data):
    if not mention_data:
        return [], []

    monthly_stats = defaultdict(lambda: {"positive": 0, "negative": 0, "neutral": 0, "total": 0})

    for m in mention_data:
        month = m["date"].strftime("%Y-%m")
        sentiment = m["sentiment"]
        monthly_stats[month]["total"] += 1
        if "Positive" in sentiment:
            monthly_stats[month]["positive"] += 1
        elif "Negative" in sentiment:
            monthly_stats[month]["negative"] += 1
        elif "Neutral" in sentiment:
            monthly_stats[month]["neutral"] += 1

    volume_data = []
    sentiment_data = []
    sorted_months = sorted(monthly_stats.keys())

    for i, month in enumerate(sorted_months):
        stats = monthly_stats[month]
        positive = stats["positive"]
        negative = stats["negative"]
        total = stats["total"]

        sentiment_score = ((positive / total) * 100 - (negative / total) * 100) if total else 0
        sentiment_ratio = (positive / negative) if negative else float('inf')

        sentiment_data.append({
            "month": month,
            "score": round(sentiment_score, 2),
            "ratio": round(sentiment_ratio, 2) if sentiment_ratio != float('inf') else None
        })

        prev_total = monthly_stats[sorted_months[i - 1]]["total"] if i > 0 else 0
        change = (((total - prev_total) / prev_total) * 100) if prev_total else None
        volume_data.append({"month": month, "count": total, "change": round(change, 2) if change is not None else None})

    return volume_data, sentiment_data

def plot_mention_volume_trend(volume_data):
    fig, ax = plt.subplots(figsize=(12, 6))
    if not volume_data:
        ax.text(0.5, 0.5, 'No Mention Volume Data.', ha='center', va='center', transform=ax.transAxes)
        ax.set_title('Mention Volume Over Time')
        return fig

    months = [d["month"] for d in volume_data]
    counts = [d["count"] for d in volume_data]
    ax.plot(months, counts, marker='o', linestyle='-', color="#1f77b4")
    ax.set(title="Monthly Mention Volume", xlabel="Month", ylabel="Mentions")
    ax.tick_params(axis='x', rotation=45)
    plt.tight_layout()
    return fig

def plot_mention_sentiment_score(sentiment_data):
    fig, ax = plt.subplots(figsize=(12, 6))
    if not sentiment_data:
        ax.text(0.5, 0.5, 'No Sentiment Score Data.', ha='center', va='center', transform=ax.transAxes)
        ax.set_title('Mention Sentiment Score')
        return fig

    months = [d["month"] for d in sentiment_data]
    scores = [d["score"] for d in sentiment_data]
    ax.plot(months, scores, marker='o', linestyle='-', color="#ff7f0e")
    ax.set(title="Monthly Sentiment Score (% Positive - % Negative)", xlabel="Month", ylabel="Score")
    ax.axhline(0, color='gray', linestyle='--', linewidth=1)
    ax.tick_params(axis='x', rotation=45)
    plt.tight_layout()
    return fig


def fetch_and_render_analytics(client_id, token):
    loading = gr.update(value="<p>Loading follower count...</p>", visible=True)
    hidden = gr.update(value=None, visible=False)

    if not token:
        error = "<p style='color:red;'>❌ Missing token. Please log in.</p>"
        return gr.update(value=error, visible=True), hidden, hidden

    try:
        name, count, gains = fetch_analytics_data(client_id, token)
        posts, org_name, sentiments = fetch_posts_and_stats(client_id, token, count=30)
        engagement_data = compute_monthly_avg_engagement_rate(posts)
        interaction_data = compute_post_interaction_metrics(posts)
        eb_data = compute_eb_content_ratio(posts)
        html_mentions, fig, mention_data = generate_mentions_dashboard(client_id, token)
        volume_data, sentiment_data = compute_mention_metrics(mention_data)


 

        count_html = f"""
        <div style='text-align:center; padding:20px; background:#e7f3ff; border:1px solid #bce8f1; border-radius:8px;'>
            <p style='font-size:1.1em; color:#31708f;'>Total Followers for</p>
            <p style='font-size:1.4em; font-weight:bold; color:#005a9e;'>{html.escape(name)}</p>
            <p style='font-size:2.8em; font-weight:bold; color:#0073b1;'>{count:,}</p>
            <p style='font-size:0.9em; color:#777;'>(As of latest data)</p>
        </div>
        """
        return gr.update(value=count_html, visible=True), gr.update(value=plot_follower_gains(gains), visible=True), gr.update(value=plot_growth_rate(gains, count), visible=True), gr.update(value=plot_avg_engagement_rate(engagement_data), visible=True), gr.update(value=plot_interaction_metrics(interaction_data), visible=True), gr.update(value=plot_eb_content_ratio(eb_data), visible=True), gr.update(value=plot_mention_volume_trend(volume_data), visible=True), gr.update(value=plot_mention_sentiment_score(sentiment_data), visible=True)

    except Exception as e:
        error = display_error("Analytics load failed.", e).get('value', "<p style='color:red;'>Error loading data.</p>")
        return gr.update(value=error, visible=True), hidden, hidden