Web URL Summary using OpenAI on Databricks and Python

# Design Document: Web URL Summary using OpenAI on Databricks

## 1. Introduction

This design document outlines the process and specifications for developing a web URL summarization tool using OpenAI's language model on Databricks. The tool aims to provide users with a concise summary of web page content by leveraging natural language processing techniques.

## 2. System Architecture

The web URL summarization tool on Databricks will consist of the following components:

### 2.1 Databricks Workspace

The Databricks Workspace will serve as the development environment and collaborative platform for building and deploying the summarization tool. It provides an interactive notebook interface for coding, data exploration, and model training.

### 2.2 Data Ingestion

To retrieve web page content, the system will utilize web scraping techniques. The Databricks environment will enable the ingestion of web data, including HTML content, using appropriate libraries such as `requests` or `BeautifulSoup`.

### 2.3 OpenAI Language Model

The OpenAI language model, such as GPT-3.5, will be employed to generate the web URL summaries. Databricks supports the integration of OpenAI's APIs, allowing seamless interaction with the language model for text processing and summarization tasks.

### 2.4 Processing and Summarization

Databricks notebooks will be utilized for pre-processing the extracted web page content, formatting it for input to the language model, and generating summaries. The language model's API calls will be made from within the notebook to process the text and generate concise summaries.

### 2.5 User Interface and Presentation

The Databricks environment offers multiple options for presenting the summarization tool's user interface. This can be achieved through Databricks notebooks, interactive dashboards, or by integrating the tool with external web frameworks like Flask or Streamlit.

## 3. System Workflow

The following workflow describes the steps involved in generating a summary for a given web URL:

1. User provides a web URL as input through the user interface.

2. The Databricks notebook receives the URL and performs validation and sanitization checks.

3. The notebook triggers the web scraping component to retrieve the HTML content of the web page.

4. The extracted text is pre-processed to remove unwanted elements (e.g., HTML tags) and prepare it for input to the language model.

5. An API call is made to the OpenAI language model using Databricks' integration capabilities.

6. The language model processes the text and generates a summary based on the provided input.

7. The generated summary is returned to the Databricks notebook.

8. The notebook formats and presents the summary to the user through the chosen interface option.

9. The user interface displays the generated summary to the user.

## 4. Integration with OpenAI Language Model

To integrate the OpenAI language model into the Databricks environment, follow these considerations:

- API Integration: Utilize OpenAI's API within the Databricks notebook to interact with the language model for text processing and summary generation.

- Input Formatting: Prepare the extracted text from the web URL in a suitable format for the language model (e.g., plain text or tokenized input).

- Output Parsing: Retrieve and parse the generated summary from the language model's API response for further processing and display.

## 5. Deployment and Scalability

The web URL summarization tool can be deployed and scaled within the Databricks environment using the following strategies:

- Cluster Configuration: Configure Databricks clusters with appropriate specifications, including the desired number of workers, instance types, and autoscaling policies, based on anticipated workload and user demand.

- Code Packaging: Package the notebook and associated dependencies into deployable artifacts, such as Databricks notebooks or D

python scripts, for easy deployment and reproducibility.

- Deployment Automation: Utilize Databricks automation capabilities, such as REST APIs or Databricks CLI, to automate the deployment process and ensure consistent environments across different stages.

- Monitoring and Scaling: Monitor the system's performance using Databricks monitoring tools and implement scaling mechanisms to handle increased traffic or workload. This can involve dynamically scaling the cluster or leveraging Databricks AutoML to optimize resource allocation.

## 6. Security and Privacy

When developing the web URL summarization tool on Databricks, it is crucial to consider security and privacy aspects:

- Data Protection: Handle user data with care, ensuring that sensitive information is not stored or exposed unnecessarily.

- Secure API Integration: Implement secure communication channels between Databricks and the OpenAI API, using encryption and authentication mechanisms to protect user data and API credentials.

- Access Control: Implement appropriate access controls within the Databricks environment to restrict access to sensitive data and system resources.

- Compliance: Comply with relevant data protection regulations and best practices, ensuring that user data is handled in accordance with privacy requirements.

## 7. Error Handling and Resilience

To ensure the robustness and resilience of the web URL summarization tool, consider the following measures:

- Error Handling: Implement error handling mechanisms within the Databricks notebook to handle cases such as invalid URLs, network errors, or API failures. Provide meaningful error messages and appropriate user feedback.

- Retry Mechanisms: Implement retry logic for API calls or web scraping operations to handle temporary failures or intermittent connectivity issues.

- Logging and Monitoring: Incorporate logging and monitoring capabilities to track system behavior, detect errors, and facilitate troubleshooting.

- Backup and Recovery: Implement regular data backups and define recovery procedures to ensure data integrity and minimize the impact of system failures.

## 8. Additional Features (Optional)

To enhance the functionality of the web URL summarization tool, consider the following additional features:

- Multi-threading: Implement parallel processing or multi-threading techniques to improve the efficiency and speed of web scraping and summary generation.

- Caching Mechanism: Introduce a caching mechanism to store previously generated summaries for frequently accessed URLs, reducing processing time and API usage.

- Sentiment Analysis: Extend the tool to include sentiment analysis of the summarized content, providing insights into the overall sentiment expressed in the web page.

- Entity Extraction: Incorporate named entity recognition techniques to extract important entities (e.g., people, organizations) from the summary and provide additional context.

- Multimedia Summarization: Extend the tool to handle multimedia content, such as images or videos, and generate summaries based on visual or audio analysis.

## 9. Conclusion

This design document outlines the architecture, workflow, integration, and deployment considerations for developing a web URL summarization tool using OpenAI's language model on Databricks. With careful implementation and scalability measures, the tool can provide users with concise and informative summaries of web page content, opening up possibilities for various applications in information retrieval, content curation, and more.

%python

import newspaper

# Specify the URL of the article

article_url = dbutils.widgets.get("Get_article_url") #"https://www.cnn.com/2023/06/06/world/spiral-galaxy-james-webb-latest-image-scn/index.html"

# Create a newspaper Article object

article = newspaper.Article(article_url)

# Download and parse the article

article.download()

article.parse()

# Extract the article content

article_text = article.text

# Use your preferred summarization technique or library here

# For example, you can use the nltk library to generate a summary

import nltk

from nltk.corpus import stopwords

from nltk.tokenize import sent_tokenize, word_tokenize

from nltk.probability import FreqDist

import heapq

# Download required resources

nltk.download('punkt')

nltk.download('stopwords')

# Tokenize the content into sentences

sentences = sent_tokenize(article_text)

# Tokenize the content into words

words = word_tokenize(article_text)

# Remove stopwords

stop_words = set(stopwords.words('english'))

filtered_words = [word for word in words if word.casefold() not in stop_words]

# Calculate word frequencies

word_frequencies = FreqDist(filtered_words)

# Calculate sentence scores based on word frequencies

sentence_scores = {}

for sentence in sentences:

? ? for word in word_tokenize(sentence.lower()):

? ? ? ? if word in word_frequencies:

? ? ? ? ? ? if len(sentence.split()) < 30:

? ? ? ? ? ? ? ? if sentence not in sentence_scores:

? ? ? ? ? ? ? ? ? ? sentence_scores[sentence] = word_frequencies[word]

? ? ? ? ? ? ? ? else:

? ? ? ? ? ? ? ? ? ? sentence_scores[sentence] += word_frequencies[word]

# Select the top N sentences with highest scores

summary_sentences = heapq.nlargest(3, sentence_scores, key=sentence_scores.get)

# Print the summary

print("Article Summary:")

for sentence in summary_sentences:

? ? display(sentence)

#databricks #webscraping