:rocket: fix convert id to class

pages/1_Review_Sentiment_Analysis.py

@@ -9,7 +9,13 @@ from hydralit_components import HyLoader, Loaders
 import pandas as pd
 import numpy as np
 from sklearn import metrics
-from utils import inference_from_pytorch, plot_confusion_matric, plot_donut_sentiment_percentage, create_classification_report, get_100_random_test_review
+from utils import (
+    inference_from_pytorch,
+    plot_confusion_matric,
+    plot_donut_sentiment_percentage,
+    create_classification_report,
+    get_100_random_test_review,
+)
 from PIL import Image
 
 
@@ -62,6 +68,9 @@ if "df_imdb_test" in st.session_state:
 if "is_df_test_100_loaded" not in st.session_state:
     st.session_state["is_df_test_100_loaded"] = False
 
+# create a map of the expected ids to their labels
+id2label = {0: "NEGATIVE", 1: "POSITIVE"}
+label2id = {"NEGATIVE": 0, "POSITIVE": 1}
 
 with HyLoader("", loader_name=Loaders.pulse_bars):
     if menu_id == "tab1":
@@ -98,11 +107,12 @@ with HyLoader("", loader_name=Loaders.pulse_bars):
                     st.dataframe(df_test_100_loaded, use_container_width=True)
 
                     # label prediction count
+                    class_count = df_test_100_loaded.predicted_class_id.value_counts()
+                    class_count_val = class_count.values.tolist()
+                    class_count_id = class_count.index.tolist()
                     pred_labels = {
-                        "label": ["positive", "negative"],
-                        "count": list(
-                            df_test_100_loaded.predicted_class_id.value_counts()
-                        ),
+                        "label": [id2label[x] for x in class_count_id],
+                        "count": class_count_val,
                     }
                     df_pred_labels = pd.DataFrame(pred_labels)