Update app.py

app.py

@@ -68,11 +68,33 @@ def process_zip(zip_file):
     shutil.rmtree(temp_dir)
     return evaluate_model(labels, preds)
 
+def format_classification_report(report_dict):
+    headers = ["precision", "recall", "f1-score", "support"]
+    row_format = "{:>12}" * (len(headers) + 1)
+    
+    formatted_report = row_format.format("", *headers) + "\n\n"
+    
+    for label, metrics in report_dict.items():
+        if label in ["accuracy", "macro avg", "weighted avg"]:
+            formatted_report += "\n"
+        row = [label]
+        for header in headers:
+            value = metrics.get(header, "")
+            if isinstance(value, float):
+                value = f"{value:.2f}"
+            elif isinstance(value, int):
+                value = str(value)
+            row.append(value)
+        formatted_report += row_format.format(*row) + "\n"
+    
+    return formatted_report
+
 def evaluate_model(labels, preds):
     cm = confusion_matrix(labels, preds)
     accuracy = accuracy_score(labels, preds)
     roc_score = roc_auc_score(labels, preds)
-    report = classification_report(labels, preds)
+    report_dict = classification_report(labels, preds, target_names=LABELS, output_dict=True)
+    formatted_report = format_classification_report(report_dict)
     fpr, tpr, _ = roc_curve(labels, preds)
     roc_auc = auc(fpr, tpr)
 
@@ -92,7 +114,7 @@ def evaluate_model(labels, preds):
     
     plt.tight_layout()
 
-    return accuracy, roc_score, report, fig
+    return accuracy, roc_score, formatted_report, fig
 
 def load_url(url):
     try: