global emotion color coding

app.py

@@ -31,6 +31,15 @@ id2label = {
     4: "Joy",
     5: "None of Them"
 }
+
+emotion_colors = {
+    "Anger": "#D96459",
+    "Fear": "#6A8EAE",
+    "Disgust": "#A4C639",
+    "Sadness": "#9DBCD4",
+    "Joy": "#F3E9A8",
+    "None of Them": "#C0C0C0"
+}
 def load_spacy_model(model_name="xx_sent_ud_sm"):
     try:
         model = spacy.load(model_name)
@@ -117,40 +126,6 @@ def plot_emotion_heatmap(heatmap_data):
 
     return fig
 
-def plot_sunburst_chart(heatmap_data):
-    data = []
-    for item in heatmap_data:
-        sentence = item['sentence']
-        emotions = item['emotions']
-
-        sentence_wrapped = "\n".join([sentence[i:i + 50] for i in range(0, len(sentence), 50)])
-
-        for i, score in enumerate(emotions):
-            data.append({
-                'sentence': sentence_wrapped,
-                'emotion': id2label[i],
-                'score': float(score)
-            })
-
-    df = pd.DataFrame(data)
-
-    fig = px.sunburst(
-        df,
-        path=['sentence', 'emotion'],
-        values='score',
-        color='emotion',
-        hover_data={'score': ':.3f'},
-        title='Sentence-Level Emotion Confidence'
-    )
-
-    fig.update_layout(
-        width=800,
-        height=800,
-        margin=dict(t=50, l=0, r=0, b=0)
-    )
-
-    return fig
-
 
 def plot_average_emotion_pie(heatmap_data):
     all_emotion_scores = np.array([item['emotions'] for item in heatmap_data])
@@ -173,7 +148,8 @@ def plot_average_emotion_pie(heatmap_data):
         labels=labels_filtered,
         autopct='%1.1f%%',
         startangle=140,
-        textprops={'fontsize': 12}
+        textprops={'fontsize': 12},
+        colors=[emotion_colors[l] for l in labels_filtered]
     )
 
     ax.axis('equal')  # Equal aspect ratio ensures a circle
@@ -186,8 +162,9 @@ def plot_emotion_barplot(heatmap_data):
     emotion_counts = most_probable_emotions.value_counts()
     all_emotions = heatmap_data.index
     emotion_frequencies = (emotion_counts.reindex(all_emotions, fill_value=0) / emotion_counts.sum()).sort_values(ascending=False)
+    palette = [emotion_colors[emotion] for emotion in emotion_frequencies.index]
     fig, ax = plt.subplots(figsize=(8, 6))
-    sns.barplot(x=emotion_frequencies.values, y=emotion_frequencies.index, palette="coolwarm", ax=ax)
+    sns.barplot(x=emotion_frequencies.values, y=emotion_frequencies.index, palette=palette, ax=ax)
     ax.set_title("Relative Frequencies of Predicted Emotions")
     ax.set_xlabel("Relative Frequency")
     ax.set_ylabel("Emotions")