AI News Summarizer 🚀

A dynamic web application that fetches the latest news from Google News, uses Large Language Models (LLMs) for summarization, and displays the results in a modern, responsive interface. The application features a non-blocking background task queue to process summaries, with on-screen articles being prioritized for a seamless user experience.

Key Features ✨

• Dual LLM Support: Easily switch between a local Ollama instance (e.g., using Gemma, Llama3) and the cloud-based Google Gemini API.
• Live News Feed: Fetches trending news from India or search results for any user-defined topic.
• Background Processing: Summarization tasks are handled by a dedicated background thread, ensuring the UI remains fast and responsive.
• Priority Queue: Intelligently prioritizes summarizing articles that are currently visible to the user.
• Persistent Caching: Uses a SQLite database to cache news articles and summaries, improving performance and data integrity.
• Time-To-Live (TTL) Cache: Automatically fetches fresh news for a topic if the cached data is older than 15 minutes, ensuring content stays relevant.
• Dynamic UI: The frontend automatically polls for completed summaries and updates the view without requiring a page refresh.
• Modern & Responsive: Built with clean HTML, modern CSS, and vanilla JavaScript for a fast, accessible experience on any device.

Technology Stack ⚙️

• Backend: Python 3, Flask
• LLM Integration: ollama, google-generativeai
• Data Fetching: feedparser
• Database: SQLite 3 (via Python's sqlite3 module)
• Environment Management: python-dotenv
• Frontend: HTML5, CSS3, Vanilla JavaScript (Fetch API, DOM Manipulation)

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Setup and Installation 🔧

Follow these steps to get the application running on your local machine.

1. Prerequisites

• Python 3.8+
• (Optional) Ollama installed and running if you plan to use it as an AI provider.

2. Clone the Repository

git clone https://github.com/Mathew005/Google-New-Summary-RSS.git
cd Google-New-Summary-RSS

3. Set Up a Virtual Environment

It's highly recommended to use a virtual environment to manage dependencies.

# Create the virtual environment
python3 -m venv venv

# Activate it (on Linux/macOS)
source venv/bin/activate

# On Windows, use:
# venv\Scripts\activate

4. Install Dependencies

Create a file named requirements.txt in the project root with the following content:

# requirements.txt
flask
feedparser
python-dotenv
google-generativeai
ollama

Now, install these packages using pip:

pip install -r requirements.txt

5. Configure Your Environment

Create a file named .env in the project root. You can copy the example below and fill in your details.

# .env file

# --- AI Provider Selection ---
# Choose your AI provider. Use "google" or "ollama".
AI_PROVIDER="ollama"


# --- Google AI Configuration ---
# Required if AI_PROVIDER is "google".
# Get your API key from Google AI Studio: https://aistudio.google.com/app/apikey
GOOGLE_API_KEY="YOUR_GOOGLE_API_KEY_HERE"
GOOGLE_MODEL="gemini-1.5-flash"


# --- Ollama Configuration ---
# Required if AI_PROVIDER is "ollama".
# Make sure your Ollama instance is running.
OLLAMA_MODEL="gemma3n"

Important: If using Ollama, ensure you have pulled the model you specify in the .env file. For example: ollama pull gemma3n

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Running the Application ▶️

With your virtual environment activated and your .env file configured, start the Flask server:

python3 app.py

The server will start, initialize the database (news.db will be created), and launch the background threads.

You can now access the application in your web browser at: http://127.0.0.1:5000

How It Works 💡

The application is composed of several key components working together:

1. Flask Web Server: Handles incoming HTTP requests. It serves the main index.html page and provides a /get-news API endpoint.
2. SQLite Database (news.db): Acts as the single source of truth. It stores all fetched articles, their metadata, and their summarization status (pending, in_progress, done, error).
3. Startup Thread: When the app starts, this thread runs once to initialize the database and reset any summaries that were stuck in_progress from a previous run. It also warms up the Ollama model if selected.
4. Background Summarizer Thread: This is the application's workhorse. It runs continuously in a loop:
   • It first checks a high-priority in-memory queue for articles that the user is currently viewing.
   • If the priority queue is empty, it queries the database for any article with a pending status.
   • It processes one article at a time, calling the selected LLM and updating the database with the result.
5. Frontend JavaScript: The client-side code fetches news from the /get-news endpoint, renders the article cards, and then polls periodically to check for and display newly completed summaries.

Project Structure 📁

/AI-News-Summarizer
|-- app.py              # The main Flask application backend
|-- templates/
|   |-- index.html      # The single HTML file for the user interface
|-- .env                # Your local configuration file (secret)
|-- news.db             # The SQLite database (created on first run)
|-- requirements.txt    # Python dependencies
|-- README.md           # This file