tranpose heatmap

app.py

@@ -101,35 +101,39 @@ def prepare_heatmap_data(data):
     return heatmap_data
 
 def plot_emotion_heatmap(heatmap_data):
-    # Normalize values to [0, 1] per row (emotion)
+    # Transpose: now rows = sentences, columns = emotions
+    heatmap_data = heatmap_data.T
+
+    # Normalize each row (sentence-wise)
     normalized_data = heatmap_data.copy()
-    for row in heatmap_data.index:
-        max_val = heatmap_data.loc[row].max()
-        normalized_data.loc[row] = heatmap_data.loc[row] / max_val if max_val > 0 else 0
+    for row in normalized_data.index:
+        max_val = normalized_data.loc[row].max()
+        normalized_data.loc[row] = normalized_data.loc[row] / max_val if max_val > 0 else 0
 
-    # Build custom RGB color matrix
+    # Create color matrix
     color_matrix = np.empty((len(normalized_data.index), len(normalized_data.columns), 3))
-    for i, emotion in enumerate(normalized_data.index):
-        base_rgb = mcolors.to_rgb(emotion_colors[emotion])
-        for j, val in enumerate(normalized_data.loc[emotion]):
-            # Linear interpolation from white to base color
-            color = tuple(1 - val * (1 - c) for c in base_rgb)
-            color_matrix[i, j] = color
-
-    fig, ax = plt.subplots(figsize=(len(normalized_data.columns) * 0.5 + 4, len(normalized_data.index) * 0.5 + 2))
-
+    for i, sentence in enumerate(normalized_data.index):
+        for j, emotion in enumerate(normalized_data.columns):
+            val = normalized_data.loc[sentence, emotion]
+            base_rgb = mcolors.to_rgb(emotion_colors[emotion])
+            # Blend from white to base color
+            blended = tuple(1 - val * (1 - c) for c in base_rgb)
+            color_matrix[i, j] = blended
+
+    fig, ax = plt.subplots(figsize=(len(normalized_data.columns) * 0.8 + 2, len(normalized_data.index) * 0.5 + 2))
     ax.imshow(color_matrix, aspect='auto')
 
-    # Ticks and labels
+    # Set ticks and labels
     ax.set_xticks(np.arange(len(normalized_data.columns)))
-    ax.set_xticklabels(normalized_data.columns, rotation=0, ha='center')
+    ax.set_xticklabels(normalized_data.columns, rotation=45, ha='right', fontsize=10)
+
     ax.set_yticks(np.arange(len(normalized_data.index)))
-    ax.set_yticklabels(normalized_data.index, rotation=0, ha='right')
+    ax.set_yticklabels(normalized_data.index, rotation=0, fontsize=10)
 
-    ax.set_xlabel("Sentences")
-    ax.set_ylabel("Emotions")
-    plt.tight_layout()
+    ax.set_xlabel("Emotions")
+    ax.set_ylabel("Sentences")
 
+    plt.tight_layout()
     return fig
 
 def plot_average_emotion_pie(heatmap_data):