Granite-AI Market Navigator

Technical Architecture

🚀 Inspiration

In today’s fast-paced digital world, news consumption is at an all-time high. However, understanding the sentiment behind news articles is often challenging. Investors, businesses, and everyday users struggle to gauge market sentiment based on news trends. We wanted to create a smart, AI-powered tool that fetches real-time news and analyzes sentiment using IBM Granite models, providing an intuitive way to navigate the market based on news trends.

🔍 What it does

Granite-AI Market Navigator is a real-time sentiment analysis tool that:

Fetches live news articles from the Bing News API based on user queries.
Analyzes the sentiment of news articles using IBM Granite AI (Positive, Neutral, Negative).
Displays sentiment trends using a thermometer-style gauge, allowing users to assess the market mood quickly.
Provides AI-driven recommendations based on sentiment analysis, helping users make informed decisions.

🛠 How we built it

Frontend: Built with React.js and hosted on Vercel for a seamless user experience.
Backend: Developed using Flask, handling API requests and processing data.
Data Sources: News articles are fetched from Bing News API (Azure).
AI Processing: IBM Granite AI models analyze news sentiment and generate insights.
Deployment: Hosted on IBM Watsonx.ai for scalability.

⚡ Challenges we ran into

IBM Granite Model Integration: Ensuring accurate sentiment classification for diverse news topics.
Real-Time Data Processing: Optimizing API calls to fetch and process news efficiently.
Frontend Visualization: Designing an interactive, easy-to-read thermometer gauge for sentiment representation.
Data Accuracy: Filtering out irrelevant or misleading news articles.

🎯 Accomplishments that we're proud of

Successfully integrated IBM Granite AI for sentiment analysis.
Built a fully functional real-time news analysis system within a short timeframe.
Designed an intuitive and interactive UI that provides valuable insights.
Overcame challenges related to API rate limits and data filtering.

📚 What we learned

Deepened our understanding of IBM Granite AI and sentiment analysis.
Improved skills in React, Flask, FastAPI, and API integration.
Learned how to optimize real-time data processing for AI applications.
Understood the importance of data visualization in conveying AI-driven insights effectively.

🔮 What's next for Granite-AI Market Navigator

Expand Data Sources: Integrate more news sources like Google News, Twitter, and Reddit.
Advanced AI Insights: Use multi-modal AI models to extract deeper insights from news articles.
Personalized Sentiment Tracking: Allow users to track specific topics or industries.
Mobile App Version: Develop a mobile app for on-the-go sentiment tracking.
Subscription Model: Offer premium features like detailed market reports and custom AI-generated insights.