Update app.py

app.py

@@ -39,28 +39,8 @@ if st.button("Analyser le texte"):
         result = classifier(text, candidate_labels, hypothesis_template=hypothesis_template)
         
         if result['labels'][0] == 1:
-            st.info(f"Résults:good comments ,accuracy {result['scores'][0]*100:.2f}%")
+            st.info(f"Résults Good comments ,accuracy {result['scores'][0]*100:.2f}%")
         if result['labels'][0] == 0:
-            st.info(f"Résults: bad comments,accuracy= {result['scores'][0]*100:.2f}%")
+            st.info(f" Bad comments,accuracy= {result['scores'][0]*100:.2f}%")
     else:
-        st.write("Text Analysis.")
-
-    # Calculer les métriques de performance (vous devez ajuster ces lignes selon votre tâche)
-    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("Evaluation of our models")
-    metrics_df = pd.DataFrame({
-        "Métrique": ["Accuracy", "Precision", "Recall", "F1-score", "Balanced Accuracy"],
-        "Valeur": [accuracy, precision, recall, f1, balanced_accuracy]
-    })
-    st.table(metrics_df)
+        st.write("Text Analysis.")