Online Chef Booking System — B.Tech Final Year Project (Flask + SQLite) | Source Code

This Online Chef Booking System is a compact, real-world web application built as a B.Tech final year or mini project. It demonstrates full-stack fundamentals: REST APIs, database design, CRUD operations, user/admin modules, and business logic — all implemented in a clean Flask (Python) app with SQLite persistence.

The project includes:

• Sample chef dataset and profile management

• Booking creation, update, deletion and retrieval endpoints

• Cost calculation logic for different service types and guest counts

• Analytics endpoint to compute totals and favorite cuisines

• A simple responsive front-end (templated) with booking pages

Key technical highlights and modules are implemented in app.py (uploaded). The app shows realistic modelling of bookings (fields such as customer details, service_type, date/time, duration, guests, total_cost, payment_status, rating/review and more) and exposes REST routes for integration or frontend work.

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🔧 What the project demonstrates (map to code)

• Backend framework: Flask app with routes: /api/chefs, /api/bookings, /api/bookings/<id>, /api/analytics (see app.py).

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• Database & schema: SQLite database bookings.db with bookings table including id, chef_id, customer details, service_type, date/time, duration, guests, budget, special_requests, dietary_restrictions, status, total_cost, payment_status, rating, review_text, timestamps, etc. (created by init_db() in app.py).

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• Business logic: calculate_total_cost(chef_id, duration, guests, service_type) — shows use of chef hourly rates, service-type multipliers, and guest-based multipliers to compute booking cost. Great for explaining algorithms and test cases in viva.

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• Sample data: CHEFS list with multiple chef objects (id, name, specialty, hourlyRate, rating, location, availability, etc.) — useful for demo data and frontend.

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• Analytics: /api/analytics computes total_bookings, total_spent, average rating and favorite_cuisine — a nice demonstration of aggregation.

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• Full source code (app.py and templates).

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• Database file bookings.db (initialized by init_db() or created during demo)

• Project report / synopsis (problem statement, literature review, system design, ER diagram, module descriptions)

• PPT for final presentation (screenshots + flow diagrams)

• Installation & run guide + sample API test cases

• Viva questions & prepared answers focusing on endpoints, DB schema, and calculate_total_cost logic

“Once purchased, running and managing the project is the buyer’s responsibility. Each project includes a detailed README guide.”