metadata

title: News Summarizer
emoji: 👁
colorFrom: gray
colorTo: green
sdk: docker
sdk_version: 5.22.0
app_file: api.py
pinned: false
short_description: An app for summarizing news articles on orgs.

News Summarization and Text-to-Speech Application

Overview

This project is a web-based application that extracts key details from multiple news articles related to a given company, performs sentiment analysis, conducts a comparative analysis, and generates a text-to-speech (TTS) output in Hindi.

Features

News Extraction: Scrapes and displays at least 10 news articles from The New York Times and BBC.
Sentiment Analysis: Categorizes articles into Positive, Negative, or Neutral sentiments.
Comparative Analysis: Groups articles with most semantic similarity. Then compares the groups to derive insights on how a company's news coverage varies.
Text-to-Speech (TTS): Converts the summarized sentiment report into Hindi speech.
User Interface: Provides a simple web-based interface using Gradio.
API Integration: Implements FastAPI for backend communication.
Deployment: Deployable on Hugging Face Spaces.

Tech Stack

Frontend: Gradio
Backend: FastAPI
Scraping: BeautifulSoup
NLP: OpenAI GPT models, LangChain, Sentence Transformers
Sentiment Analysis: Pre-trained Transformer model
Text-to-Speech: Google TTS (gTTS)
Deployment: Uvicorn, Hugging Face Spaces

Installation and Setup

1. Clone the Repository

git clone https://github.com/Senzen18/News-Summarizer.git
cd News-Summarizer

2. Install Dependencies

Ensure you have Python 3.8+ installed. Then, run:

pip install -r requirements.txt

3. To run Fast API endpoints

Start the FastAPI backend:

uvicorn api:app --host 127.0.0.1 --port 8000 --reload

4. To run the both Gradio and Fast API

Start the FastAPI backend:

gradio app.py

5. Access the Application

Once started, access the Gradio UI at:

http://127.0.0.1:7860

API Endpoints

1. Fetch News

GET /news/{company_name}

Fetches the latest articles related to a company.
Example: /news/Tesla

2. Analyze News Sentiment

GET /analyze-news

Performs sentiment analysis on the extracted articles.

3. Compare News Articles

POST /compare-news

Performs comparative analysis.
Request Body:

{
  "api_key": "your-openai-api-key",
  "model_name": "gpt-4o-mini",
  "company_name": "Tesla"
}

4. Generate Hindi Summary

GET /hindi-summary

Returns the summarized analysis in Hindi and stores the speech file.

File Structure

├── api.py               # FastAPI backend for news extraction, sentiment analysis, and comparison
├── app.py               # Gradio frontend to interact with users
├── llm_utils.py         # Handles OpenAI API calls for topic extraction and comparative analysis
├── utils.py             # Utility functions for web scraping, sentiment analysis, and TTS
├── requirements.txt     # Dependencies
└── README.md            # Project documentation

Assumptions and Limitations

Only extracts articles from The New York Times and BBC.
Requires a valid OpenAI API key for sentiment analysis and comparison.
Hindi speech output uses gTTS, which requires an internet connection.

Deployment

This project can be deployed on Hugging Face Spaces. To deploy:

Push your repository to GitHub.
Follow Hugging Face Spaces documentation for deployment.

Example Output

{
  "Company": "Tesla",
  "Articles": [
    {
      "Title": "Tesla's New Model Breaks Sales Records",
      "Summary": "Tesla's latest EV sees record sales in Q3...",
      "Sentiment": "Positive",
      "Topics": ["Electric Vehicles", "Stock Market", "Innovation"]
    }
  ],
  "Comparative Sentiment Score": {
    "Sentiment Distribution": {"Positive": 1, "Negative": 1, "Neutral": 0},
    "Coverage Differences": [{
      "Comparison": "Article 1 highlights Tesla's strong sales, while Article 2 discusses regulatory issues.",
      "Impact": "Investors may react positively to growth news but stay cautious due to regulatory scrutiny."
    }],
    "Topic Overlap": {
      "Common Topics": ["Electric Vehicles"],
      "Unique Topics in Article 1": ["Stock Market", "Innovation"],
      "Unique Topics in Article 2": ["Regulations", "Autonomous Vehicles"]
    }
  },
  "Final Sentiment Analysis": "Tesla’s latest news coverage is mostly positive. Potential stock growth expected.",
  "Audio": "[Play Hindi Speech]"
}

Contributing

Feel free to contribute by:

Adding more news sources
Improving the sentiment model
Enhancing the UI

Contact

For queries, reach out at [[email protected]].