SAP EWA automated Analysis and tracker

[ManjunathBasapp]

My query to Microsoft Co-pilot

I have unstructured data with dynamic information on the SAP EWA Report. Each EWA Report contains Alert Overview and alert over view points contains critical issues and recommendations. Each Critical or recommendations under alert overview will have a hyperlinks which takes you to detailed information, where SAP provides issues, findings on tabular format and also recommendations. each week EWA REport contains different issues and different recommendations. Automated EWA report and summarise the issues, findings and recommendations on tabular format using any AI Tool like Azure GenAI, Python, Github Copilot etc., wanted to leverage GENAI.

Detailed Proposal for Automating SAP EWA Report Summarization

Objective
To automate the extraction, summarization, and presentation of critical issues, findings, and recommendations from SAP EWA Reports using AI tools like Azure GenAI, Python, and GitHub Copilot.

Tools and Technologies Required
1. Python Libraries:
o BeautifulSoup: For web scraping and parsing HTML content.
o PyPDF2 or pdfplumber: For extracting text from PDF documents.
o re (Regular Expressions): For pattern matching and text extraction.
o SpaCy or NLTK: For natural language processing tasks.
o Pandas: For data manipulation and creating tabular data.
o Matplotlib or Seaborn: For data visualization.

2. Azure Cognitive Services:
o Azure Text Analytics: For entity recognition, key phrase extraction, and sentiment analysis.
o Azure OpenAI: For leveraging pre-trained language models to summarize and generate insights.

3. Machine Learning Frameworks:
o TensorFlow or PyTorch: For developing custom machine learning models.

4. Automation and Integration Tools:
o GitHub Copilot: For code suggestions and assistance in writing scripts.
o Azure Functions: For automating the workflow and scheduling tasks.

5. Visualization and Reporting Tools:
o Power BI: For creating interactive dashboards and reports.

Step-by-Step Implementation Plan

1. Data Extraction:
o Web Scraping: Use BeautifulSoup to scrape HTML content from the EWA reports if they are web-based.
o PDF Parsing: Use PyPDF2 or pdfplumber to extract text from PDF reports.
o Pattern Matching: Use regular expressions to identify and extract hyperlinks, critical issues, and recommendations.
from bs4 import BeautifulSoup
import requests
import re

# Example of web scraping
url = 'http://example.com/ewa_report'
response = requests.get(url)
soup = BeautifulSoup(response.content, 'html.parser')

# Extract hyperlinks
links = soup.find_all('a', href=True)
for link in links:
print(link['href'])

# Extract critical issues and recommendations
text = soup.get_text()
critical_issues = re.findall(r'Critical Issue: (.*?)\n', text)
recommendations = re.findall(r'Recommendation: (.*?)\n', text)
2. Natural Language Processing (NLP):
o Entity Recognition: Use Azure Text Analytics or SpaCy to identify entities and key phrases.
o Summarization: Use pre-trained models from Azure OpenAI to summarize the extracted text.
import spacy
from azure.ai.textanalytics import TextAnalyticsClient
from azure.core.credentials import AzureKeyCredential

# Example of using SpaCy for entity recognition
nlp = spacy.load('en_core_web_sm')
doc = nlp(text)
for ent in doc.ents:
print(ent.text, ent.label_)

# Example of using Azure Text Analytics
credential = AzureKeyCredential("your_key")
client = TextAnalyticsClient(endpoint="your_endpoint", credential=credential)
response = client.extract_key_phrases([text])
key_phrases = response[0].key_phrases
3. Machine Learning Models:
o Custom Models: Develop and fine-tune models using TensorFlow or PyTorch to classify and summarize the data.
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, LSTM

# Example of a simple LSTM model
model = Sequential()
model.add(LSTM(128, input_shape=(timesteps, features)))
model.add(Dense(1, activation='sigmoid'))
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

# Train the model (example)
model.fit(X_train, y_train, epochs=10, batch_size=32)
4. Automation and Integration:
o GitHub Copilot: Use Copilot to assist in writing and optimizing code.
o Azure Functions: Automate the workflow to run weekly and process new reports.
import azure.functions as func

def main(req: func.HttpRequest) -> func.HttpResponse:
# Your code to process the EWA report
return func.HttpResponse("Report processed successfully.")
5. Visualization and Reporting:
o Pandas and Matplotlib: Create tables and visualizations to present the summarized data.
o Power BI: Develop interactive dashboards to provide insights.
import pandas as pd
import matplotlib.pyplot as plt

# Create DataFrame
data = {'Issue': critical_issues, 'Recommendation': recommendations}
df = pd.DataFrame(data)

# Plot data
df.plot(kind='bar')
plt.show()

Programming Knowledge Required

1. Python: Proficiency in Python is essential for data extraction, parsing, NLP, and machine learning tasks.
2. Web Scraping: Knowledge of web scraping techniques using BeautifulSoup.
3. PDF Parsing: Familiarity with libraries like PyPDF2 or pdfplumber.
4. Regular Expressions: Understanding of regular expressions for pattern matching.
5. NLP: Experience with NLP libraries like SpaCy or NLTK.
6. Machine Learning: Skills in developing and fine-tuning models using TensorFlow or PyTorch.
7. Azure Services: Knowledge of Azure Cognitive Services and Azure Functions.
8. Data Visualization: Proficiency in data manipulation and visualization using Pandas, Matplotlib, and Power BI.

CHATGPT and COPILOT Conclusion
It is very much possible to automate the summarization of SAP EWA Reports using the tools and techniques outlined above. By following this detailed proposal and leveraging the required programming knowledge, your development team can successfully automate the process and present the information in a structured, tabular format.
Anyone has done any automation of EWA Report Analysis and tracker?