replaced sunburst chart with piechart

app.py

@@ -151,6 +151,36 @@ def plot_sunburst_chart(heatmap_data):
 
     return fig
 
+
+def plot_average_emotion_pie(heatmap_data):
+    all_emotion_scores = np.array([item['emotions'] for item in heatmap_data])
+    mean_scores = all_emotion_scores.mean(axis=0)
+
+    labels = [id2label[i] for i in range(len(mean_scores))]
+    sizes = mean_scores
+
+    # optional: remove emotions with near-zero average
+    labels_filtered = []
+    sizes_filtered = []
+    for l, s in zip(labels, sizes):
+        if s > 0.01:  # You can change this threshold
+            labels_filtered.append(l)
+            sizes_filtered.append(s)
+
+    fig, ax = plt.subplots(figsize=(6, 6))
+    wedges, texts, autotexts = ax.pie(
+        sizes_filtered,
+        labels=labels_filtered,
+        autopct='%1.1f%%',
+        startangle=140,
+        textprops={'fontsize': 12}
+    )
+
+    ax.axis('equal')  # Equal aspect ratio ensures a circle
+    plt.title("Average Emotion Confidence Across Sentences", fontsize=14)
+
+    return fig
+
 def plot_emotion_barplot(heatmap_data):
     most_probable_emotions = heatmap_data.idxmax(axis=0)
     emotion_counts = most_probable_emotions.value_counts()
@@ -185,8 +215,9 @@ def predict_wrapper(text, language):
 
     figure = plot_emotion_barplot(prepare_heatmap_data(results_heatmap))
     heatmap = plot_emotion_heatmap(prepare_heatmap_data(results_heatmap))
+    piechart = plot_average_emotion_pie(prepare_heatmap_data(results_heatmap))
     output_info = f'Prediction was made using the <a href="https://huggingface.co/{model_id}">{model_id}</a> model.'
-    return results, figure, sunburst_chart, heatmap, output_info
+    return results, figure, piechart, heatmap, output_info
 
 
 with gr.Blocks() as demo:
@@ -210,7 +241,7 @@ with gr.Blocks() as demo:
         plot = gr.Plot()
 
     with gr.Row():
-        sunburst_chart = gr.Plot()
+        piechart = gr.Plot()
 
     with gr.Row():
         heatmap = gr.Plot()
@@ -221,7 +252,7 @@ with gr.Blocks() as demo:
     predict_button.click(
         fn=predict_wrapper,
         inputs=[input_text, language_choice],
-        outputs=[result_table, plot, sunburst_chart, heatmap, model_info]
+        outputs=[result_table, plot, piechart, heatmap, model_info]
     )
 
 if __name__ == "__main__":