Update app.py

app.py

@@ -41,23 +41,23 @@ else:
     st.write("Veuillez entrer du texte pour l'analyse.")
 
 # Calculer les métriques de performance (vous devez ajuster ces lignes selon votre tâche)
-if text and candidate_labels:
-    inputs = df["text"].tolist()
-    true_labels = df["label"].tolist()
-    predictions = classifier(inputs, candidate_labels, hypothesis_template=hypothesis_template)
-    predicted_labels = [result['labels'][0] for result in predictions]
+
+inputs = df["text"].tolist()
+true_labels = df["label"].tolist()
+predictions = classifier(inputs, candidate_labels, hypothesis_template=hypothesis_template)
+predicted_labels = [result['labels'][0] for result in predictions]
     
-    accuracy = accuracy_score(true_labels, predicted_labels)
-    precision = precision_score(true_labels, predicted_labels, average='binary')  
-    recall = recall_score(true_labels, predicted_labels, average='binary')  
-    f1 = f1_score(true_labels, predicted_labels, average='binary') 
-    balanced_accuracy = balanced_accuracy_score(true_labels, predicted_labels)
-
-    # Afficher les métriques sous forme de tableau
-    st.header("Métriques de Performance")
-    metrics_df = pd.DataFrame({
+accuracy = accuracy_score(true_labels, predicted_labels)
+precision = precision_score(true_labels, predicted_labels, average='binary')  
+recall = recall_score(true_labels, predicted_labels, average='binary')  
+f1 = f1_score(true_labels, predicted_labels, average='binary') 
+balanced_accuracy = balanced_accuracy_score(true_labels, predicted_labels)
+
+# Afficher les métriques sous forme de tableau
+st.header("Métriques de Performance")
+metrics_df = pd.DataFrame({
         "Métrique": ["Accuracy", "Precision", "Recall", "F1-score", "Balanced Accuracy"],
         "Valeur": [accuracy, precision, recall, f1, balanced_accuracy]
     })
-    st.table(metrics_df)
+st.table(metrics_df)