AI Price Tracker Cloud-Based Autonomous Price Monitoring System using Docker & Microservices 📌 Overview
The AI Price Tracker is a cloud-deployed full-stack application that automatically monitors product prices from e-commerce platforms. It uses an autonomous worker agent to continuously scrape prices, stores historical data, and provides real-time insights through an interactive dashboard.
The system is built using a microservices architecture, where the API, worker, and UI run independently using Docker and are deployed on Azure Container Apps.
URL link: https://price-tracker-ui.greenplant-9b018a93.southeastasia.azurecontainerapps.io/
✨ Features 📦 Track multiple products using URLs 🔄 Automated price scraping using worker agent 📊 Price history visualization (Chart.js) 🚨 Price drop detection ☁️ Cloud deployment on Azure 🐳 Docker-based microservices architecture 🏗️ Architecture Frontend (Dashboard UI) │ ▼ FastAPI Backend (REST APIs) │ ▼ Azure CosmosDB (Database) ▲ │ Worker Agent (Scraper + Automation) ⚙️ Tech Stack
Frontend
HTML, CSS, JavaScript Chart.js
Backend
FastAPI (Python)
Database
Azure CosmosDB
Cloud & DevOps
Azure Container Apps Azure Container Registry (ACR) Docker 🔁 Workflow User adds a product via dashboard Backend stores product details Worker agent periodically scrapes price Data stored in CosmosDB Dashboard displays latest price + history 🐳 Docker Setup (Local)
docker build -f Dockerfile -t price-tracker-api .
docker build -f Dockerfile.worker -t price-tracker-worker .
docker build -f Dockerfile.ui -t price-tracker-ui . ☁️ Azure Deployment Deployed using Azure Container Apps Images stored in Azure Container Registry Services: price-tracker-api price-tracker-worker price-tracker-ui 🚧 Challenges Faced Handling dynamic web scraping Debugging Azure container deployment Managing environment variables API integration with frontend 🔮 Future Improvements AI-based price prediction Multi-platform tracking (Amazon, Flipkart, etc.) Smart alerts & recommendations Enhanced UI (React dashboard) 🧠 What I Learned Building scalable backend systems with FastAPI Designing microservices architecture Docker containerization & cloud deployment Working with Azure services (CosmosDB, Container Apps) 🙌 Author
Jeevan Sresanth S 🚧 Challenges Faced Handling dynamic web scraping Debugging Azure container deployment Managing environment variables API integration with frontend 🔮 Future Improvements AI-based price prediction Multi-platform tracking (Amazon, Flipkart, etc.) Smart alerts & recommendations Enhanced UI (React dashboard) 🧠 What I Learned Building scalable backend systems with FastAPI Designing microservices architecture Docker containerization & cloud deployment Working with Azure services (CosmosDB, Container Apps) 🙌 Author
Jeevan Sresanth S ⭐ If you like this project
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