# Implementation Plan: ItemSense MVP

## Goal Description
Build a desktop application to identify items using the webcam and OpenAI's visual capabilities, using a **native macOS UI (PyObjC)**.

## User Review Required
- **Technology Shift**: Switching from Tkinter to PyObjC (AppKit).
- **Camera Strategy**: Using OpenCV for frame capture and bridging to AppKit for display. This keeps the implementation simpler than writing a raw AVFoundation delegate in Python while maintaining a native UI.

## Proposed Changes

### Spec 001: Core UI & Camera Feed
#### [NEW] main.py
- Initialize `NSApplication` and `NSWindow` (AppKit).
- Implement a custom `AppDelegate` to handle app lifecycle.
- Integrate OpenCV (`cv2`) for webcam capture.
- Display video frames in an `NSImageView`.

#### [NEW] requirements.txt
- `pyobjc-framework-Cocoa`
- `opencv-python`
- `Pillow` (for easier image data manipulation if needed)

### Spec 002: OpenAI Vision Integration
#### [MODIFY] main.py
- Add `Capture` button (`NSButton`) to the UI.
- Implement logic to snapshot the current OpenCV frame.
- Run OpenAI API request in a background thread to prevent UI freezing.
- Send image to OpenAI API.

#### [MODIFY] requirements.txt
- Add `openai`
- Add `python-dotenv`

### Spec 003: Result Display
#### [MODIFY] main.py
- Add `NSTextView` (in `NSScrollView`) for results.
- Add "Scan Another" button logic.
- Ensure UI layout manages state transitions cleanly (Live vs Result).

## Verification Plan

### Manual Verification
1.  **Launch**: Run `python main.py`.
2.  **Native Look**: Verify the window uses native macOS controls.
3.  **Feed**: Verify camera feed is smooth and correctly oriented.
4.  **Flow**: Capture -> Processing -> Result -> Scan Another.