Update analytics_fetch_and_rendering.py

analytics_fetch_and_rendering.py

@@ -182,6 +182,74 @@ def plot_avg_engagement_rate(data):
     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)
+        })
+
+    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, ax = plt.subplots(figsize=(12, 6))
+    ax.plot(months, comments_pp, label="Comments/Post", marker="o", color="#1f77b4")
+    ax.plot(months, shares_pp, label="Shares/Post", marker="s", color="#ff7f0e")
+    ax.plot(months, clicks_pp, label="Clicks/Post", marker="^", color="#2ca02c")
+    ax.plot(months, comment_share, label="Comment Share of Engagement (%)", marker="x", linestyle="--", color="#d62728")
+
+    ax.set(title="Post Interaction Metrics", xlabel="Month", ylabel="Values")
+    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)
@@ -195,6 +263,7 @@ def fetch_and_render_analytics(client_id, token):
         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)
  
 
         count_html = f"""
@@ -205,7 +274,7 @@ 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), gr.update(value=plot_avg_engagement_rate(engagement_data), 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), gr.update(value=plot_interaction_metrics(interaction_data), visible=True)
 
     except Exception as e:
         error = display_error("Analytics load failed.", e).get('value', "<p style='color:red;'>Error loading data.</p>")