Update app.py

app.py

@@ -6,14 +6,11 @@ import numpy as np
 from transformers import pipeline
 from sklearn.ensemble import RandomForestClassifier
 import joblib
-import torch  # Import PyTorch for using the Hugging Face model
 
-# Load pre-trained models for Anomaly Detection and Failure Prediction
-# Using a public Hugging Face model as an example for anomaly detection
-# Replace 'distilbert-base-uncased' with your model path
-anomaly_detection = pipeline("text-classification", model="distilbert-base-uncased")
+# Load fine-tuned Hugging Face model for anomaly detection
+anomaly_detection = pipeline("text-classification", model="./fine_tuned_anomaly_model")
 
-# Load the Random Forest model for failure prediction (pre-trained model)
+# Load the Random Forest model for failure prediction
 failure_prediction_model = joblib.load('failure_prediction_model.pkl')
 
 # Function to preprocess logs for anomaly detection
@@ -28,7 +25,7 @@ def detect_anomaly(logs):
     results = []
     for log in preprocessed_logs['log_message']:
         anomaly_result = anomaly_detection(log)
-        results.append(anomaly_result[0]['label'])  # Assuming the Hugging Face model outputs label as result
+        results.append(anomaly_result[0]['label'])
     return results
 
 # Function to predict failures based on historical data and metrics