Update analytics_fetch_and_rendering.py

analytics_fetch_and_rendering.py

@@ -9,6 +9,8 @@ import html
 from sessions import create_session
 from error_handling import display_error
 
+from Update Data_Fetching_and_Rendering import fetch_posts_and_stats
+
 API_V2_BASE = 'https://api.linkedin.com/v2'
 API_REST_BASE = 'https://api.linkedin.com/rest'
 
@@ -122,6 +124,65 @@ def plot_growth_rate(data, total):
     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 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)
@@ -132,6 +193,9 @@ def fetch_and_render_analytics(client_id, token):
 
     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)
+ 
 
         count_html = f"""
         <div style='text-align:center; padding:20px; background:#e7f3ff; border:1px solid #bce8f1; border-radius:8px;'>
@@ -141,7 +205,7 @@ def fetch_and_render_analytics(client_id, token):
             <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)
+        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)
 
     except Exception as e:
         error = display_error("Analytics load failed.", e).get('value', "<p style='color:red;'>Error loading data.</p>")