Update app.py

app.py

@@ -6,19 +6,18 @@ 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
-# Assume we have two models: LSTM for anomaly detection and RandomForest for failure prediction
-
-# Hugging Face anomaly detection pipeline (custom fine-tuned model)
-anomaly_detection = pipeline("text-classification", model="your-huggingface-model-path")
+# 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 the Random Forest model for failure prediction (pre-trained model)
 failure_prediction_model = joblib.load('failure_prediction_model.pkl')
 
 # Function to preprocess logs for anomaly detection
 def preprocess_logs(logs):
-    # Assume logs come in as JSON and require some basic parsing and cleaning
     logs['timestamp'] = pd.to_datetime(logs['timestamp'])
     logs['log_message'] = logs['log_message'].str.lower()
     return logs
@@ -34,8 +33,7 @@ def detect_anomaly(logs):
 
 # Function to predict failures based on historical data and metrics
 def predict_failure(device_metrics):
-    # Metrics include CPU usage, memory usage, error rates
-    metrics_array = np.array([device_metrics['cpu_usage'], device_metrics['memory_usage'], device_metrics['error_rate']]).reshape(1, -3)
+    metrics_array = np.array([device_metrics['cpu_usage'], device_metrics['memory_usage'], device_metrics['error_rate']]).reshape(1, -1)
     failure_prediction = failure_prediction_model.predict(metrics_array)
     return failure_prediction