Train a model to categorize news articles, scrape and translate articles, and predict their categories using TensorFlow, Keras, and Google Translate API.

This project consists of three main Python files:

1. reuters_classification.py: Implements a Reuters news classification model using TensorFlow and Keras.
2. news_scraper_translator.py: Contains classes for news scraping and text translation.
3. demo.py: Demonstrates how to train the Reuters model and analyze news articles.

This file contains the ReutersModel, ReutersTrainer, and ReutersPredictor classes. The ReutersModel class is responsible for building, training, and evaluating the news classification model using the Reuters dataset. The ReutersTrainer class trains the model and the ReutersPredictor class predicts the category of a given text input.

This file contains the NewsScraper and TextTranslator classes. The NewsScraper class is responsible for fetching and extracting news articles' title and content from a given URL. The TextTranslator class is responsible for translating text using the Google Translate API.

This file demonstrates how to train the Reuters model and analyze news articles using the ModelTrainer and NewsAnalyzer classes. The ModelTrainer class is responsible for training the Reuters model, while the NewsAnalyzer class analyzes the news article, translates the text, and predicts its category using the trained model.

To use this project, follow these steps:

Install the required Python libraries:

pip install -r requirements.txt

Run demo.py to train the Reuters model and analyze a news article:

python demo.py

The script will output the predicted category for the given news article.

1. Improve the accuracy of the classification model by using more advanced techniques, such as fine-tuning pre-trained models like BERT or RoBERTa.
2. Expand the functionality of the NewsScraper class to support more websites and handle different web page structures.
3. Add support for multiple languages in the TextTranslator class by detecting the input language and translating it to a target language before classification.
4. Implement a web-based user interface or an API to allow users to input news articles' URLs and receive the predicted category.
5. Add functionality to monitor news websites in real-time and automatically classify articles as they are published.
6. Consider implementing caching or storage for the trained model to improve performance and reduce retraining time.
7. Use additional metrics, such as precision, recall, and F1 score, to evaluate the performance of the classification model.

This project is licensed under the MIT License.