Make sure to update this file every time.

**manual_slop** is a local GUI tool for manually curating and sending context to AI APIs. It aggregates files, screenshots, and discussion history into a structured markdown file and sends it to a chosen AI provider with a user-written message. The AI can also execute PowerShell scripts within the project directory, with user confirmation required before each execution.

**Stack:**
- `dearpygui` - GUI with docking/floating/resizable panels
- `google-genai` - Gemini API
- `anthropic` - Anthropic API
- `tomli-w` - TOML writing
- `uv` - package/env management

**Files:**
- `gui.py` - main GUI, `App` class, all panels, all callbacks, confirmation dialog, layout persistence
- `ai_client.py` - unified provider wrapper, model listing, session management, send, tool/function-call loop, comms log
- `aggregate.py` - reads config, collects files/screenshots/discussion, writes numbered `.md` files to `output_dir`
- `shell_runner.py` - subprocess wrapper that runs PowerShell scripts sandboxed to `base_dir`, returns stdout/stderr/exit code as a string
- `config.toml` - namespace, output_dir, files paths+base_dir, screenshots paths+base_dir, discussion history array, ai provider+model
- `credentials.toml` - gemini api_key, anthropic api_key
- `dpg_layout.ini` - Dear PyGui window layout file (auto-saved on exit, auto-loaded on startup); gitignore this per-user

**GUI Panels:**
- **Config** - namespace, output dir, save
- **Files** - base_dir, scrollable path list with remove, add file(s), add wildcard
- **Screenshots** - base_dir, scrollable path list with remove, add screenshot(s)
- **Discussion History** - multiline text box, `---` as separator between excerpts, save splits on `---` back into toml array
- **Provider** - provider combo (gemini/anthropic), model listbox populated from API, fetch models button
- **Message** - multiline input, Gen+Send button, MD Only button, Reset session button
- **Response** - readonly multiline displaying last AI response
- **Tool Calls** - scrollable log of every PowerShell tool call the AI made, showing script and result; Clear button
- **Comms History** - live log of every raw request/response/tool_call/tool_result exchanged with the vendor API; status line lives here; Clear button; heavy fields (message, text, script, output) clamped to an 80px scrollable box when they exceed `COMMS_CLAMP_CHARS` (300) characters

**Layout persistence:**
- `dpg.configure_app(..., init_file="dpg_layout.ini")` loads the ini at startup if it exists; DPG silently ignores a missing file
- `dpg.save_init_file("dpg_layout.ini")` is called immediately before `dpg.destroy_context()` on clean exit
- The ini records window positions, sizes, and dock node assignments in DPG's native format
- First run (no ini) uses the hardcoded `pos=` defaults in `_build_ui()`; after that the ini takes over
- Delete `dpg_layout.ini` to reset to defaults

**AI Tool Use (PowerShell):**
- Both Gemini and Anthropic are configured with a `run_powershell` tool/function declaration
- When the AI wants to edit or create files it emits a tool call with a `script` string
- `ai_client` runs a loop (max `MAX_TOOL_ROUNDS = 5`) feeding tool results back until the AI stops calling tools
- Before any script runs, `gui.py` shows a modal `ConfirmDialog` on the main thread; the background send thread blocks on a `threading.Event` until the user clicks Approve or Reject
- The dialog displays `base_dir`, shows the script in an editable text box (allowing last-second tweaks), and has Approve & Run / Reject buttons
- On approval the (possibly edited) script is passed to `shell_runner.run_powershell()` which prepends `Set-Location -LiteralPath '<base_dir>'` and runs it via `powershell -NoProfile -NonInteractive -Command`
- stdout, stderr, and exit code are returned to the AI as the tool result
- Rejections return `"USER REJECTED: command was not executed"` to the AI
- All tool calls (script + result/rejection) are appended to `_tool_log` and displayed in the Tool Calls panel

**Comms Log (ai_client.py):**
- `_comms_log: list[dict]` accumulates every API interaction during a session
- `_append_comms(direction, kind, payload)` called at each boundary: OUT/request before sending, IN/response after each model reply, OUT/tool_call before executing, IN/tool_result after executing, OUT/tool_result_send when returning results to the model
- Entry fields: `ts` (HH:MM:SS), `direction` (OUT/IN), `kind`, `provider`, `model`, `payload` (dict)
- Anthropic responses also include `usage` (input_tokens/output_tokens) and `stop_reason` in payload
- `get_comms_log()` returns a snapshot; `clear_comms_log()` empties it
- `comms_log_callback` (injected by gui.py) is called from the background thread with each new entry; gui queues entries in `_pending_comms` (lock-protected) and flushes them to the DPG panel each render frame
- `MAX_FIELD_CHARS = 400` in ai_client is the threshold used for the clamp decision in the UI (`COMMS_CLAMP_CHARS = 300` in gui.py governs the display cutoff)

**Comms History panel rendering:**
- Each entry shows: index, timestamp, direction (colour-coded blue=OUT / green=IN), kind (colour-coded), provider/model
- Payload fields rendered below the header; fields in `_HEAVY_KEYS` (`message`, `text`, `script`, `output`, `content`) that exceed `COMMS_CLAMP_CHARS` are shown in an 80px tall readonly scrollable `input_text` box instead of a plain `add_text`
- Colour legend row at the top of the panel
- Status line (formerly in Provider panel) moved to top of Comms History panel
- Reset session also clears the comms log and panel; Clear button in Comms History clears only the comms log

**Data flow:**
1. GUI edits are held in `App` state lists (`self.files`, `self.screenshots`, `self.history`) and dpg widget values
2. `_flush_to_config()` pulls all widget values into `self.config` dict
3. `_do_generate()` calls `_flush_to_config()`, saves `config.toml`, calls `aggregate.run(config)` which writes the md and returns `(markdown_str, path)`
4. `cb_generate_send()` calls `_do_generate()` then threads a call to `ai_client.send(md, message, base_dir)`
5. `ai_client.send()` prepends the md as a `<context>` block to the user message and sends via the active provider chat session
6. If the AI responds with tool calls, the loop handles them (with GUI confirmation) before returning the final text response
7. Sessions are stateful within a run (chat history maintained), `Reset` clears them, the tool log, and the comms log

**Config persistence:**
- Every send and save writes `config.toml` with current state including selected provider and model under `[ai]`
- Discussion history is stored as a TOML array of strings in `[discussion] history`
- File and screenshot paths are stored as TOML arrays, support absolute paths, relative paths from base_dir, and `**/*` wildcards

**Threading model:**
- DPG render loop runs on the main thread
- AI sends and model fetches run on daemon background threads
- `_pending_dialog` (guarded by a `threading.Lock`) is set by the background thread and consumed by the render loop each frame, calling `dialog.show()` on the main thread
- `dialog.wait()` blocks the background thread on a `threading.Event` until the user acts
- `_pending_comms` (guarded by a separate `threading.Lock`) is populated by `_on_comms_entry` (background thread) and drained by `_flush_pending_comms()` each render frame (main thread)

**Known extension points:**
- Add more providers by adding a section to `credentials.toml`, a `_list_*` and `_send_*` function in `ai_client.py`, and the provider name to the `PROVIDERS` list in `gui.py`
- System prompt support could be added as a field in `config.toml` and passed in `ai_client.send()`
- Discussion history excerpts could be individually toggleable for inclusion in the generated md
- `MAX_TOOL_ROUNDS` in `ai_client.py` caps agentic loops at 5 rounds; adjustable
- `COMMS_CLAMP_CHARS` in `gui.py` controls the character threshold for clamping heavy payload fields