This app providers a summary of news along with sentiment analysis on news content using three different methods: NLTK's VADER, TextBlob, and BERT from the transformers library. The news headlines are gathered via various methods offered through the app, then an OpenAI Assistant is called via API to perform the summarization.
- Fetch news articles from GNews API and Yahoo Finance.
- Upload text files for sentiment analysis.
- Paste text directly into the app for summarization and analysis.
- Perform sentiment analysis using NLTK, TextBlob, or BERT.
- Interact with OpenAI's GPT model to create content based on the news.
Before running the app, install the required libraries using:
pip install streamlit requests textblob transformers nltkYou also need to download the VADER lexicon with:
import nltk
nltk.download('vader_lexicon')To start the app, run:
streamlit run streamlit_app.pyGNEWS_API_KEY: To fetch news from the GNews API.
OPENAI_API_KEY: For using the OpenAI GPT model.
Ensure these are set in your environment or use the Streamlit interface to input them.
- Upload a File: Upload a text file for sentiment analysis.
- Paste Text: Paste the news text directly into the app.
- Fetch from URL: Provide a URL to fetch content for analysis.
- GNews API: Enter a search query to fetch news from GNews.
- Yahoo Finance News: Get the latest news from Yahoo Finance.
- Sentiment Analysis Options
- Choose between NLTK, TextBlob, and BERT for sentiment analysis.
Contributions are welcome! To contribute to News Summarizer, please follow these guidelines:
- Fork the repository.
- Create a new branch for your changes.
- Make your changes and write tests for them.
- Run the tests using pytest to make sure they pass.
- Submit a pull request.
If you have any questions or need help using News Summarizer, please post a question or open an issue on GitHub.
This project is licensed under the Apache 2.0 License - see the LICENSE file for details.
