Update app.py

app.py

@@ -13,6 +13,7 @@ from imblearn.over_sampling import SMOTE
 import plotly.express as px
 import plotly.graph_objects as go
 import warnings
+from sklearn.metrics import precision_score, recall_score, f1_score
 
 warnings.filterwarnings("ignore")
 
@@ -20,6 +21,10 @@ warnings.filterwarnings("ignore")
 print("Loading dataset...")
 ds = load_dataset("uhoui/text-tone-classifier")
 
+# Optional: download csv (colab)
+# df = ds['train'].to_pandas()
+# df.to_csv("text_tone_classifier.csv", index=False)
+
 df = pd.DataFrame(ds["train"])
 
 # Console Log dataset and class
@@ -45,7 +50,6 @@ X_train, X_test, y_train, y_test = train_test_split(
 )
 
 # TFIDF Feature extraction
-print("Creating TF-IDF features...")
 tfidf = TfidfVectorizer(max_features=5000)
 X_train_tfidf = tfidf.fit_transform(X_train)
 X_test_tfidf = tfidf.transform(X_test)
@@ -68,14 +72,20 @@ except ValueError as e:
     X_train_resampled, y_train_resampled = X_train_tfidf, y_train
 
 # Logistic Regression Model
-print("Training model...")
 model = LogisticRegression(C=10, max_iter=1000, n_jobs=-1, solver='lbfgs', multi_class='multinomial')
 model.fit(X_train_resampled, y_train_resampled)
 
 # Evaluate Model
 y_pred = model.predict(X_test_tfidf)
 accuracy = accuracy_score(y_test, y_pred)
+precision = precision_score(y_test, y_pred, average='weighted')
+recall = recall_score(y_test, y_pred, average='weighted')
+f1 = f1_score(y_test, y_pred, average='weighted')
+
 print(f"Model accuracy: {accuracy:.4f}")
+print(f"Precision: {precision:.4f}")
+print(f"Recall:    {recall:.4f}")
+print(f"F1 Score:  {f1:.4f}")
 
 def predict_tone(text):
     # Vectorize