Create app.py

app.py

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+# app.py
+
+import gradio as gr
+import pandas as pd
+import numpy as np
+from transformers import pipeline
+from sklearn.ensemble import RandomForestClassifier
+import joblib
+
+# 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")
+
+# 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
+
+# Function to detect anomalies
+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
+    return results
+
+# 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)
+    failure_prediction = failure_prediction_model.predict(metrics_array)
+    return failure_prediction
+
+# Gradio interface to upload log files and check anomaly detection and failure prediction
+def process_logs_and_predict(log_file, metrics):
+    logs = pd.read_json(log_file)
+    anomalies = detect_anomaly(logs)
+    failure_pred = predict_failure(metrics)
+    
+    return f"Anomalies Detected: {anomalies}, Failure Prediction: {failure_pred}"
+
+# Set up Gradio interface for the dashboard
+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.")
+
+iface.launch()