Update app.py

app.py

@@ -78,6 +78,22 @@ from sklearn.metrics import accuracy_score, precision_recall_fscore_support, con
 data = pd.read_excel("ResponseOpenPredicted.xlsx")
 st.title("Resume-based Personality Prediction by Serikov Ayanbek")
 
+enneagram_types = {
+    "Peacemaker": "Peacemaker",
+    "Loyalist": "Loyalist",
+    "Achiever": "Achiever",
+    "Reformer": "Reformer",
+    "Individualist": "Individualist",
+    "Helper": "Helper",
+    "Challenger": "Challenger",
+    "Investigator": "Investigator",
+    "Enthusiast": "Enthusiast"
+}
+
+# Replace numeric or generic labels with descriptive Enneagram types
+data['Predicted_F'] = data['Predicted_F'].map(enneagram_types)
+data['Predicted_M'] = data['Predicted_M'].map(enneagram_types)
+
 # Function to calculate metrics
 def calculate_metrics(true_labels, predicted_labels):
     accuracy = accuracy_score(true_labels, predicted_labels)
@@ -100,10 +116,14 @@ def plot_confusion_matrix(conf_matrix, title):
 # Plotting function for distribution of predictions
 def plot_predictions_distribution(data, column, title):
     fig, ax = plt.subplots()
-    sns.countplot(x=column, data=data, palette="viridis")
+    sns.countplot(x=column, data=data, palette="viridis", ax=ax)
     plt.title(title)
     plt.xlabel('Predicted Labels')
     plt.ylabel('Count')
+    plt.xticks(rotation=45)  # Rotate labels for better readability
+    ax.xaxis.label.set_size(12)
+    ax.yaxis.label.set_size(12)
+    plt.subplots_adjust(bottom=0.15)  # Adjust spacing to accommodate label rotation
     st.pyplot(fig)
 
 # Streamlit app structure