How sasibharath9/lilcamp-project works
LIL-CAMP is a menu-driven campus lost-item logger. A user selects an operation in the menu; the system adds item records, lists or searches stored records, or computes and visualizes reports. Item records use an inherited model and are persisted in JSON through the storage layer. The README also describes graph visualization, supported by the sampled report implementation. The feature methods share one source file; edges show the calls visible there, not an assumed sequence between operations.
Main entry points: main.py Menu · lilcamp_system.py Reports · storage.py JSON storage · item.py Item record
Components 13
Menu and operations
- Menu
Lost_Item_Logger_System_for_College_Campus_(LIL-CAMP)/main.py - Add item
Lost_Item_Logger_System_for_College_Campus_(LIL-CAMP)/system/lilcamp_system.py - View items
Lost_Item_Logger_System_for_College_Campus_(LIL-CAMP)/system/lilcamp_system.py - Search items
Lost_Item_Logger_System_for_College_Campus_(LIL-CAMP)/system/lilcamp_system.py - Reports
Lost_Item_Logger_System_for_College_Campus_(LIL-CAMP)/system/lilcamp_system.py
Item model
- Item record
Lost_Item_Logger_System_for_College_Campus_(LIL-CAMP)/models/item.py - Base item
Lost_Item_Logger_System_for_College_Campus_(LIL-CAMP)/models/base_item.py
Storage and reporting
- JSON storage
Lost_Item_Logger_System_for_College_Campus_(LIL-CAMP)/storage/storage.py - Item data
Lost_Item_Logger_System_for_College_Campus_(LIL-CAMP)/data/lilcamp_data.json
Other components
- Campus user
- Pandas
- NumPy
- Matplotlib
Connections 17
- Campus user → Menu: chooses operation
- Menu → Add item: dispatches add
- Menu → View items: dispatches view
- Menu → Search items: dispatches search
- Menu → Reports: dispatches report
- Add item → JSON storage: loads and saves
- View items → JSON storage: loads
- Search items → JSON storage: loads
- Reports → JSON storage: loads
- Add item → Item record: creates
- Item record → Base item: inherits
- JSON storage → Item data: reads and writes
- View items → Pandas: builds table
- Search items → Pandas: filters table
- Reports → Pandas: builds and counts
- Reports → NumPy: counts statuses
- Reports → Matplotlib: plots charts