🛒 Inspiration
Brick-and-mortar malls and retail stores often make it hard for customers to quickly find product information, store locations, or check what's available. I wanted to build a conversational AI agent that helps users get instant answers about products and store data, powered by a real cloud database.
🤖 What it does
SmartRetail AI Agent is a conversational web app that:
- Answers questions about available products and inventory
- Shows store/location data
- Searches the database by keyword in real-time
- Returns live results directly from MongoDB Atlas
🔧 How I built it
- Python + Flask — backend server and API routes
- MongoDB Atlas — cloud database storing product/catalog data
- HTML/CSS/JS — chat-style frontend interface
- Render — cloud hosting and deployment
- Git/GitHub — version control
🧠 How it works
- User types a question or clicks a suggestion
- Flask backend receives the query
- Backend queries MongoDB Atlas based on the request type (search, locations, top items)
- Results are formatted and returned to the chat interface in real-time
🚧 Challenges
- Setting up MongoDB Atlas connection securely with environment variables
- Debugging deployment issues (port binding, network access) on Render
- Designing query logic to return relevant results based on user intent
📚 What I learned
- Connecting a Flask app to MongoDB Atlas
- Deploying a full-stack app to the cloud with Render
- Handling environment variables and IP whitelisting for cloud databases
- Debugging real production deployment issues end-to-end
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