Update app.py

app.py

@@ -3,14 +3,19 @@
 import gradio as gr
 import pandas as pd
 import numpy as np
-from transformers import pipeline
+from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification
 from sklearn.ensemble import RandomForestClassifier
 import joblib
 
-# Load fine-tuned Hugging Face model for anomaly detection
-anomaly_detection = pipeline("text-classification", model="./fine_tuned_anomaly_model")
+# Using the Hugging Face model "huggingface-course/distilbert-base-uncased-finetuned-imdb"
+# Load the tokenizer and model separately
+tokenizer = AutoTokenizer.from_pretrained("huggingface-course/distilbert-base-uncased-finetuned-imdb")
+model = AutoModelForSequenceClassification.from_pretrained("huggingface-course/distilbert-base-uncased-finetuned-imdb")
 
-# Load the Random Forest model for failure prediction
+# 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')
 
 # Function to preprocess logs for anomaly detection
@@ -25,7 +30,7 @@ def detect_anomaly(logs):
     results = []
     for log in preprocessed_logs['log_message']:
         anomaly_result = anomaly_detection(log)
-        results.append(anomaly_result[0]['label'])
+        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