Update app.py

app.py

@@ -6,52 +6,74 @@ import numpy as np
 from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification
 from sklearn.ensemble import RandomForestClassifier
 import joblib
+import os
 
-# Using the Hugging Face model "huggingface-course/distilbert-base-uncased-finetuned-imdb"
-# Load the tokenizer and model separately
+# Step 1: Load Hugging Face model for anomaly detection
+# Using the "huggingface-course/distilbert-base-uncased-finetuned-imdb" model
 tokenizer = AutoTokenizer.from_pretrained("huggingface-course/distilbert-base-uncased-finetuned-imdb")
 model = AutoModelForSequenceClassification.from_pretrained("huggingface-course/distilbert-base-uncased-finetuned-imdb")
-
-# Define pipeline for anomaly detection using the loaded model and tokenizer
 anomaly_detection = pipeline("text-classification", model=model, tokenizer=tokenizer)
 
-# Load the Random Forest model for failure prediction (this should be trained and saved as failure_prediction_model.pkl)
-failure_prediction_model = joblib.load('failure_prediction_model.pkl')
+# Step 2: Train or Load the Random Forest model for failure prediction
+if not os.path.exists('failure_prediction_model.pkl'):
+    # Sample data (replace this with real Cisco device metrics data)
+    data = pd.DataFrame({
+        'cpu_usage': [10, 20, 15, 35, 55],
+        'memory_usage': [30, 60, 45, 50, 80],
+        'error_rate': [0, 1, 0, 2, 5],
+        'failure': [0, 1, 0, 1, 1]  # 0 = no failure, 1 = failure
+    })
+
+    # Features and target
+    X = data[['cpu_usage', 'memory_usage', 'error_rate']]
+    y = data['failure']
+
+    # Train the Random Forest model
+    failure_prediction_model = RandomForestClassifier(n_estimators=100, random_state=42)
+    failure_prediction_model.fit(X, y)
 
-# Function to preprocess logs for anomaly detection
+    # Save the model for future use
+    joblib.dump(failure_prediction_model, 'failure_prediction_model.pkl')
+else:
+    # Load the trained model from file
+    failure_prediction_model = joblib.load('failure_prediction_model.pkl')
+
+# Step 3: Define function to preprocess logs for anomaly detection
 def preprocess_logs(logs):
     logs['timestamp'] = pd.to_datetime(logs['timestamp'])
-    logs['log_message'] = logs['log_message'].str.lower()
+    logs['log_message'] = logs['log_message'].str.lower()  # Convert log messages to lowercase for uniformity
     return logs
 
-# Function to detect anomalies
+# Step 4: Function to detect anomalies in logs
 def detect_anomaly(logs):
     preprocessed_logs = preprocess_logs(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
+        anomaly_result = anomaly_detection(log)  # Use Hugging Face pipeline for anomaly detection
+        results.append(anomaly_result[0]['label'])  # Append label (e.g., "POSITIVE" or "NEGATIVE")
     return results
 
-# Function to predict failures based on historical data and metrics
+# Step 5: Function to predict failures based on device metrics
 def predict_failure(device_metrics):
+    # Convert device metrics into a numpy array for prediction
     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)
+    failure_prediction = failure_prediction_model.predict(metrics_array)  # Use the Random Forest model for failure prediction
     return failure_prediction
 
-# Gradio interface to upload log files and check anomaly detection and failure prediction
+# Step 6: Function to process logs and predict both anomalies and failures
 def process_logs_and_predict(log_file, metrics):
-    logs = pd.read_json(log_file)
-    anomalies = detect_anomaly(logs)
-    failure_pred = predict_failure(metrics)
-    
+    logs = pd.read_json(log_file)  # Load logs from the uploaded JSON file
+    anomalies = detect_anomaly(logs)  # Detect anomalies in logs
+    failure_pred = predict_failure(metrics)  # Predict failures using device metrics
+
     return f"Anomalies Detected: {anomalies}, Failure Prediction: {failure_pred}"
 
-# Set up Gradio interface for the dashboard
+# Step 7: Set up Gradio interface for uploading logs and metrics for prediction
 iface = gr.Interface(fn=process_logs_and_predict, 
                      inputs=["file", "json"], 
                      outputs="text", 
                      title="Cisco Device Monitoring",
                      description="Upload log files to detect anomalies and predict potential device failures.")
 
+# Launch the Gradio interface
 iface.launch()