Update app.py

app.py

@@ -7,7 +7,6 @@ from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassifica
 from sklearn.ensemble import RandomForestClassifier
 import joblib
 import os
-import json
 
 # Load Hugging Face model for anomaly detection
 tokenizer = AutoTokenizer.from_pretrained("huggingface-course/distilbert-base-uncased-finetuned-imdb")
@@ -36,13 +35,18 @@ def preprocess_logs(logs):
     logs['log_message'] = logs['log_message'].str.lower()
     return logs
 
-# Detect anomalies in logs
+# Detect anomalies in logs with label mapping
 def detect_anomaly(logs):
     preprocessed_logs = preprocess_logs(logs)
+    label_map = {  # Map Hugging Face output labels to meaningful labels
+        "LABEL_0": "Normal",
+        "LABEL_1": "Anomaly"
+    }
     results = []
     for log in preprocessed_logs['log_message']:
         anomaly_result = anomaly_detection(log)
-        results.append(anomaly_result[0]['label'])
+        label = anomaly_result[0]['label']
+        results.append(label_map.get(label, label))  # Map the label or return the original label
     return results
 
 # Predict failures based on device metrics