Pateel H S pateelhs

## Plan
Conduct an Audit of Existing Alerts
Gather a comprehensive list of all alerts currently being generated across different systems and applications.
Identify the source, trigger conditions, severity levels, and any additional metadata associated with each alert.
Analyze the alerts to identify redundancies, overlaps, or inconsistencies in how they are defined or categorized.
Establish Alert Classification Taxonomy
Define a standardized taxonomy for classifying alerts based on factors such as:
System/Application: Categorize alerts by the system or application they originate from.
Alert Type: Classify alerts based on their nature (e.g., performance, security, availability, etc.).
Severity Level: Establish consistent severity levels (e.g., critical, high, medium, low) based on the impact and urgency.
Business Impact: Categorize alerts based on their potential impact on business operations, revenue, or customer experience.

## streamlit_link_search.py
import pandas as pd
from transformers import pipeline
import streamlit as st

# Initialize the pipeline
classifier = pipeline("zero-shot-classification", model="knowledgator/comprehend_it-base")

# Define your candidate labels
labels = ["Access", "Build", "Enhancement", "Decommission", "Backup and Restore", "Optimization", "Patching"]

## 4*4.html
<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>4x4 Grid Template</title>
    <style>
        table {
            width: 100%;
            border-collapse: collapse;

## comp_group.py
import pandas as pd
from transformers import pipeline

# Initialize the pipeline
classifier = pipeline("zero-shot-classification", model="knowledgator/comprehend_it-base")

# Define your candidate labels
labels = ["Access", "Build", "Enhancement","Decommission","Backup and Restore","Optimization","Patching"]  # Replace these with your labels

# Load data

## zero.py
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.cluster import KMeans
from collections import Counter
from transformers import pipeline

# Initialize the zero-shot classifier
classifier = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")

# Define the possible labels

## kmeans.py
import pandas as pd
import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
from transformers import BertTokenizer, BertForSequenceClassification
from sklearn.cluster import KMeans
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
import logging
import torch

## bert_keyword.py
import pandas as pd
import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
from transformers import BertTokenizer, BertForSequenceClassification
from sklearn.cluster import DBSCAN
from nltk.tokenize import word_tokenize,sent_tokenize
from nltk.corpus import stopwords
import logging
import torch

## bert_labels.py
import pandas as pd
import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
from transformers import BertTokenizer, BertForSequenceClassification
from sklearn.cluster import DBSCAN
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
import logging
import torch

## clustering.py
# clustering.py
from sklearn.cluster import DBSCAN
import logging

logging.basicConfig(level=logging.DEBUG)
logger = logging.getLogger(__name__)

def cluster_tickets_advanced(tfidf_matrix, min_samples=5, eps=0.5):
    dbscan = DBSCAN(eps=eps, min_samples=min_samples, metric='cosine')
    clusters = dbscan.fit_predict(tfidf_matrix)

## OnlyPlot.py
import pandas as pd
import numpy as np
from sklearn.cluster import KMeans
from transformers import BertTokenizer, BertModel
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
import matplotlib.pyplot as plt
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
	Conduct an Audit of Existing Alerts
	Gather a comprehensive list of all alerts currently being generated across different systems and applications.
	Identify the source, trigger conditions, severity levels, and any additional metadata associated with each alert.
	Analyze the alerts to identify redundancies, overlaps, or inconsistencies in how they are defined or categorized.
	Establish Alert Classification Taxonomy
	Define a standardized taxonomy for classifying alerts based on factors such as:
	System/Application: Categorize alerts by the system or application they originate from.
	Alert Type: Classify alerts based on their nature (e.g., performance, security, availability, etc.).
	Severity Level: Establish consistent severity levels (e.g., critical, high, medium, low) based on the impact and urgency.
	Business Impact: Categorize alerts based on their potential impact on business operations, revenue, or customer experience.
	import pandas as pd
	from transformers import pipeline
	import streamlit as st

	# Initialize the pipeline
	classifier = pipeline("zero-shot-classification", model="knowledgator/comprehend_it-base")

	# Define your candidate labels
	labels = ["Access", "Build", "Enhancement", "Decommission", "Backup and Restore", "Optimization", "Patching"]
	<!DOCTYPE html>
	<html lang="en">
	<head>
	<meta charset="UTF-8">
	<meta name="viewport" content="width=device-width, initial-scale=1.0">
	<title>4x4 Grid Template</title>
	<style>
	table {
	width: 100%;
	border-collapse: collapse;
	import pandas as pd
	from sklearn.feature_extraction.text import TfidfVectorizer
	from sklearn.cluster import KMeans
	from collections import Counter
	from transformers import pipeline

	# Initialize the zero-shot classifier
	classifier = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")

	# Define the possible labels
	import pandas as pd
	import numpy as np
	from sklearn.feature_extraction.text import TfidfVectorizer
	from sklearn.metrics.pairwise import cosine_similarity
	from transformers import BertTokenizer, BertForSequenceClassification
	from sklearn.cluster import KMeans
	from nltk.tokenize import word_tokenize
	from nltk.corpus import stopwords
	import logging
	import torch
	# clustering.py
	from sklearn.cluster import DBSCAN
	import logging

	logging.basicConfig(level=logging.DEBUG)
	logger = logging.getLogger(__name__)

	def cluster_tickets_advanced(tfidf_matrix, min_samples=5, eps=0.5):
	dbscan = DBSCAN(eps=eps, min_samples=min_samples, metric='cosine')
	clusters = dbscan.fit_predict(tfidf_matrix)