📰 News Analyzer with LLM Summarization

This project scrapes the latest news articles from various domains like Business, Technology, Health, Sports, Entertainment, and even Reddit posts—then feeds them into a locally running LLM (via Ollama) to generate a detailed analysis.

🚀 Features

• 🔍 Scrapes latest headlines from:

  • Business Today
  • TechCrunch
  • ESPN
  • Healthline
  • Variety
  • Reddit (via Reddit's JSON API)

• 💬 Uses Ollama + LLaMA 3.2 locally for:

  • Summarization
  • Sentiment analysis
  • Socio-economic, political & stock market impact evaluation

• ⚙️ Clean command-line interface for selecting the type of news

• ⏱️ Supports rate limiting with time.sleep()

• 🔗 Extracts full article content when possible

🧠 LLM Prompt

The LLM receives a prompt like:

You are a global news analyst. Given a news article, respond with the following format:

Summary: ...

Sentiment: Positive / Negative / Neutral

Socio-economic Impact: ...

Political Impact: ...

Stock Market Impact: ...

📦 Requirements

• Python 3.8+
• Ollama installed and running
• Model (e.g., llama3.2) pulled via ollama run llama3.2

Install Python dependencies: pip install requests beautifulsoup4

▶️ How to Run

1. Make sure Ollama is installed and running:
   ollama run llama3.2

2. Install required Python packages:
   pip install requests beautifulsoup4

3. Run the script:

4. Follow the prompt to choose a category (1–6) and get LLM-based analysis.

Future Scope

1. Cloud Deployment
2. Cache Previous articles
3. Buy/Sell/Hold sentiment catagories based on NIFTY50 and S&P500 using financial news data.
4. Add tickers manually or let users input a company name for financial news.