Update analytics_fetch_and_rendering.py

analytics_fetch_and_rendering.py

@@ -10,6 +10,7 @@ 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
 
@@ -311,6 +312,79 @@ def plot_eb_content_ratio(data):
     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)
@@ -326,6 +400,9 @@ def fetch_and_render_analytics(client_id, token):
         engagement_data = compute_monthly_avg_engagement_rate(posts)
         interaction_data = compute_post_interaction_metrics(posts)
         eb_data = compute_eb_content_ratio(posts)
+        html, fig, mention_data = generate_mentions_dashboard(client_id, token_dict)
+        volume_data, sentiment_data = compute_mention_metrics(mention_data)
+
 
  
 
@@ -337,7 +414,7 @@ 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), gr.update(value=plot_interaction_metrics(interaction_data), visible=True), gr.update(value=plot_eb_content_ratio(eb_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), 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>")