Files
voice-to-notes/src-tauri/src/commands/project.rs

246 lines
7.2 KiB
Rust
Raw Normal View History

use serde::{Deserialize, Serialize};
use tauri::State;
Phase 1 foundation: Tauri shell, Python sidecar, SQLite database Tauri v2 + Svelte + TypeScript frontend: - App shell with workspace layout (waveform, transcript, speakers, AI chat) - Placeholder components for all major UI areas - Typed stores (project, transcript, playback, AI) - TypeScript interfaces matching the database schema - Tauri bridge service with typed invoke wrappers - svelte-check passes with 0 errors Rust backend: - Tauri v2 app entry point with command registration - SQLite database layer (rusqlite with bundled SQLite) - Full schema: projects, media_files, speakers, segments, words, ai_outputs, annotations (with indexes) - Model structs with serde serialization - CRUD queries for projects, speakers, segments, words - Segment text editing preserves original text - Schema versioning for future migrations - 6 tests passing - Command stubs for project, transcribe, export, AI, settings, system - App state management Python sidecar: - JSON-line IPC protocol (stdin/stdout) - Message types: IPCMessage, progress, error, ready - Handler registry with routing and error handling - Ping/pong handler for connectivity testing - Service stubs: transcribe, diarize, pipeline, AI, export - Provider stubs: local (llama-server), OpenAI, Anthropic, LiteLLM - Hardware detection stubs - 14 tests passing, ruff clean Also adds: - Testing strategy document (docs/TESTING.md) - Validation script (scripts/validate.sh) - Updated .gitignore for Svelte, Rust, Python artifacts Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-02-26 15:16:06 -08:00
use crate::db::models::Project;
use crate::db::queries;
use crate::state::AppState;
Phase 1 foundation: Tauri shell, Python sidecar, SQLite database Tauri v2 + Svelte + TypeScript frontend: - App shell with workspace layout (waveform, transcript, speakers, AI chat) - Placeholder components for all major UI areas - Typed stores (project, transcript, playback, AI) - TypeScript interfaces matching the database schema - Tauri bridge service with typed invoke wrappers - svelte-check passes with 0 errors Rust backend: - Tauri v2 app entry point with command registration - SQLite database layer (rusqlite with bundled SQLite) - Full schema: projects, media_files, speakers, segments, words, ai_outputs, annotations (with indexes) - Model structs with serde serialization - CRUD queries for projects, speakers, segments, words - Segment text editing preserves original text - Schema versioning for future migrations - 6 tests passing - Command stubs for project, transcribe, export, AI, settings, system - App state management Python sidecar: - JSON-line IPC protocol (stdin/stdout) - Message types: IPCMessage, progress, error, ready - Handler registry with routing and error handling - Ping/pong handler for connectivity testing - Service stubs: transcribe, diarize, pipeline, AI, export - Provider stubs: local (llama-server), OpenAI, Anthropic, LiteLLM - Hardware detection stubs - 14 tests passing, ruff clean Also adds: - Testing strategy document (docs/TESTING.md) - Validation script (scripts/validate.sh) - Updated .gitignore for Svelte, Rust, Python artifacts Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-02-26 15:16:06 -08:00
// ── Input types for save_project_transcript ──────────────────────
#[derive(Deserialize)]
pub struct WordInput {
pub word: String,
pub start_ms: i64,
pub end_ms: i64,
pub confidence: f64,
}
#[derive(Deserialize)]
pub struct SegmentInput {
pub text: String,
pub start_ms: i64,
pub end_ms: i64,
pub speaker: Option<String>, // speaker label, not id
pub words: Vec<WordInput>,
}
#[derive(Deserialize)]
pub struct SpeakerInput {
pub label: String,
pub color: String,
}
// ── Output types for load_project_transcript ─────────────────────
#[derive(Serialize)]
pub struct WordOutput {
pub word: String,
pub start_ms: i64,
pub end_ms: i64,
pub confidence: Option<f64>,
}
#[derive(Serialize)]
pub struct SegmentOutput {
pub id: String,
pub text: String,
pub start_ms: i64,
pub end_ms: i64,
pub speaker: Option<String>, // speaker label
pub words: Vec<WordOutput>,
}
#[derive(Serialize)]
pub struct SpeakerOutput {
pub id: String,
pub label: String,
pub display_name: Option<String>,
pub color: Option<String>,
}
#[derive(Serialize)]
pub struct ProjectTranscript {
pub file_path: String,
pub segments: Vec<SegmentOutput>,
pub speakers: Vec<SpeakerOutput>,
}
// ── Commands ─────────────────────────────────────────────────────
Phase 1 foundation: Tauri shell, Python sidecar, SQLite database Tauri v2 + Svelte + TypeScript frontend: - App shell with workspace layout (waveform, transcript, speakers, AI chat) - Placeholder components for all major UI areas - Typed stores (project, transcript, playback, AI) - TypeScript interfaces matching the database schema - Tauri bridge service with typed invoke wrappers - svelte-check passes with 0 errors Rust backend: - Tauri v2 app entry point with command registration - SQLite database layer (rusqlite with bundled SQLite) - Full schema: projects, media_files, speakers, segments, words, ai_outputs, annotations (with indexes) - Model structs with serde serialization - CRUD queries for projects, speakers, segments, words - Segment text editing preserves original text - Schema versioning for future migrations - 6 tests passing - Command stubs for project, transcribe, export, AI, settings, system - App state management Python sidecar: - JSON-line IPC protocol (stdin/stdout) - Message types: IPCMessage, progress, error, ready - Handler registry with routing and error handling - Ping/pong handler for connectivity testing - Service stubs: transcribe, diarize, pipeline, AI, export - Provider stubs: local (llama-server), OpenAI, Anthropic, LiteLLM - Hardware detection stubs - 14 tests passing, ruff clean Also adds: - Testing strategy document (docs/TESTING.md) - Validation script (scripts/validate.sh) - Updated .gitignore for Svelte, Rust, Python artifacts Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-02-26 15:16:06 -08:00
#[tauri::command]
pub fn create_project(name: String, state: State<AppState>) -> Result<Project, String> {
let conn = state.db.lock().map_err(|e| e.to_string())?;
queries::create_project(&conn, &name).map_err(|e| e.to_string())
Phase 1 foundation: Tauri shell, Python sidecar, SQLite database Tauri v2 + Svelte + TypeScript frontend: - App shell with workspace layout (waveform, transcript, speakers, AI chat) - Placeholder components for all major UI areas - Typed stores (project, transcript, playback, AI) - TypeScript interfaces matching the database schema - Tauri bridge service with typed invoke wrappers - svelte-check passes with 0 errors Rust backend: - Tauri v2 app entry point with command registration - SQLite database layer (rusqlite with bundled SQLite) - Full schema: projects, media_files, speakers, segments, words, ai_outputs, annotations (with indexes) - Model structs with serde serialization - CRUD queries for projects, speakers, segments, words - Segment text editing preserves original text - Schema versioning for future migrations - 6 tests passing - Command stubs for project, transcribe, export, AI, settings, system - App state management Python sidecar: - JSON-line IPC protocol (stdin/stdout) - Message types: IPCMessage, progress, error, ready - Handler registry with routing and error handling - Ping/pong handler for connectivity testing - Service stubs: transcribe, diarize, pipeline, AI, export - Provider stubs: local (llama-server), OpenAI, Anthropic, LiteLLM - Hardware detection stubs - 14 tests passing, ruff clean Also adds: - Testing strategy document (docs/TESTING.md) - Validation script (scripts/validate.sh) - Updated .gitignore for Svelte, Rust, Python artifacts Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-02-26 15:16:06 -08:00
}
#[tauri::command]
pub fn get_project(id: String, state: State<AppState>) -> Result<Option<Project>, String> {
let conn = state.db.lock().map_err(|e| e.to_string())?;
queries::get_project(&conn, &id).map_err(|e| e.to_string())
Phase 1 foundation: Tauri shell, Python sidecar, SQLite database Tauri v2 + Svelte + TypeScript frontend: - App shell with workspace layout (waveform, transcript, speakers, AI chat) - Placeholder components for all major UI areas - Typed stores (project, transcript, playback, AI) - TypeScript interfaces matching the database schema - Tauri bridge service with typed invoke wrappers - svelte-check passes with 0 errors Rust backend: - Tauri v2 app entry point with command registration - SQLite database layer (rusqlite with bundled SQLite) - Full schema: projects, media_files, speakers, segments, words, ai_outputs, annotations (with indexes) - Model structs with serde serialization - CRUD queries for projects, speakers, segments, words - Segment text editing preserves original text - Schema versioning for future migrations - 6 tests passing - Command stubs for project, transcribe, export, AI, settings, system - App state management Python sidecar: - JSON-line IPC protocol (stdin/stdout) - Message types: IPCMessage, progress, error, ready - Handler registry with routing and error handling - Ping/pong handler for connectivity testing - Service stubs: transcribe, diarize, pipeline, AI, export - Provider stubs: local (llama-server), OpenAI, Anthropic, LiteLLM - Hardware detection stubs - 14 tests passing, ruff clean Also adds: - Testing strategy document (docs/TESTING.md) - Validation script (scripts/validate.sh) - Updated .gitignore for Svelte, Rust, Python artifacts Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-02-26 15:16:06 -08:00
}
#[tauri::command]
pub fn list_projects(state: State<AppState>) -> Result<Vec<Project>, String> {
let conn = state.db.lock().map_err(|e| e.to_string())?;
queries::list_projects(&conn).map_err(|e| e.to_string())
Phase 1 foundation: Tauri shell, Python sidecar, SQLite database Tauri v2 + Svelte + TypeScript frontend: - App shell with workspace layout (waveform, transcript, speakers, AI chat) - Placeholder components for all major UI areas - Typed stores (project, transcript, playback, AI) - TypeScript interfaces matching the database schema - Tauri bridge service with typed invoke wrappers - svelte-check passes with 0 errors Rust backend: - Tauri v2 app entry point with command registration - SQLite database layer (rusqlite with bundled SQLite) - Full schema: projects, media_files, speakers, segments, words, ai_outputs, annotations (with indexes) - Model structs with serde serialization - CRUD queries for projects, speakers, segments, words - Segment text editing preserves original text - Schema versioning for future migrations - 6 tests passing - Command stubs for project, transcribe, export, AI, settings, system - App state management Python sidecar: - JSON-line IPC protocol (stdin/stdout) - Message types: IPCMessage, progress, error, ready - Handler registry with routing and error handling - Ping/pong handler for connectivity testing - Service stubs: transcribe, diarize, pipeline, AI, export - Provider stubs: local (llama-server), OpenAI, Anthropic, LiteLLM - Hardware detection stubs - 14 tests passing, ruff clean Also adds: - Testing strategy document (docs/TESTING.md) - Validation script (scripts/validate.sh) - Updated .gitignore for Svelte, Rust, Python artifacts Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-02-26 15:16:06 -08:00
}
#[tauri::command]
pub fn delete_project(id: String, state: State<AppState>) -> Result<(), String> {
let conn = state.db.lock().map_err(|e| e.to_string())?;
queries::delete_project(&conn, &id).map_err(|e| e.to_string())
}
#[tauri::command]
pub fn update_segment(
segment_id: String,
new_text: String,
state: State<AppState>,
) -> Result<(), String> {
let conn = state.db.lock().map_err(|e| e.to_string())?;
queries::update_segment_text(&conn, &segment_id, &new_text).map_err(|e| e.to_string())
}
#[tauri::command]
pub fn save_project_transcript(
project_id: String,
file_path: String,
segments: Vec<SegmentInput>,
speakers: Vec<SpeakerInput>,
state: State<AppState>,
) -> Result<Project, String> {
let conn = state.db.lock().map_err(|e| e.to_string())?;
// 1. Create media file entry
let media_file =
queries::create_media_file(&conn, &project_id, &file_path).map_err(|e| e.to_string())?;
// 2. Create speaker entries and build label -> id map
let mut speaker_map = std::collections::HashMap::new();
for speaker_input in &speakers {
let speaker = queries::create_speaker(
&conn,
&project_id,
&speaker_input.label,
Some(&speaker_input.color),
)
.map_err(|e| e.to_string())?;
speaker_map.insert(speaker_input.label.clone(), speaker.id);
}
// 3. Create segments with words
for (index, seg_input) in segments.iter().enumerate() {
let speaker_id = seg_input
.speaker
.as_ref()
.and_then(|label| speaker_map.get(label));
let segment = queries::create_segment(
&conn,
&project_id,
&media_file.id,
speaker_id.map(|s| s.as_str()),
seg_input.start_ms,
seg_input.end_ms,
&seg_input.text,
index as i32,
)
.map_err(|e| e.to_string())?;
// Create words for this segment
for (word_index, word_input) in seg_input.words.iter().enumerate() {
queries::create_word(
&conn,
&segment.id,
&word_input.word,
word_input.start_ms,
word_input.end_ms,
Some(word_input.confidence),
word_index as i32,
)
.map_err(|e| e.to_string())?;
}
}
// 4. Return updated project info
queries::get_project(&conn, &project_id)
.map_err(|e| e.to_string())?
.ok_or_else(|| "Project not found".to_string())
}
#[tauri::command]
pub fn load_project_transcript(
project_id: String,
state: State<AppState>,
) -> Result<Option<ProjectTranscript>, String> {
let conn = state.db.lock().map_err(|e| e.to_string())?;
// 1. Get media files for the project
let media_files =
queries::get_media_files_for_project(&conn, &project_id).map_err(|e| e.to_string())?;
let media_file = match media_files.first() {
Some(mf) => mf,
None => return Ok(None),
};
// 2. Get speakers for the project and build id -> label map
let speakers =
queries::get_speakers_for_project(&conn, &project_id).map_err(|e| e.to_string())?;
let speaker_label_map: std::collections::HashMap<String, String> = speakers
.iter()
.map(|s| (s.id.clone(), s.label.clone()))
.collect();
// 3. Get segments for the media file
let db_segments =
queries::get_segments_for_media(&conn, &media_file.id).map_err(|e| e.to_string())?;
// 4. Build output segments with nested words
let mut segment_outputs = Vec::with_capacity(db_segments.len());
for seg in &db_segments {
let words = queries::get_words_for_segment(&conn, &seg.id).map_err(|e| e.to_string())?;
let word_outputs: Vec<WordOutput> = words
.into_iter()
.map(|w| WordOutput {
word: w.word,
start_ms: w.start_ms,
end_ms: w.end_ms,
confidence: w.confidence,
})
.collect();
let speaker_label = seg
.speaker_id
.as_ref()
.and_then(|sid| speaker_label_map.get(sid))
.cloned();
segment_outputs.push(SegmentOutput {
id: seg.id.clone(),
text: seg.text.clone(),
start_ms: seg.start_ms,
end_ms: seg.end_ms,
speaker: speaker_label,
words: word_outputs,
});
}
// 5. Build speaker outputs
let speaker_outputs: Vec<SpeakerOutput> = speakers
.into_iter()
.map(|s| SpeakerOutput {
id: s.id,
label: s.label,
display_name: s.display_name,
color: s.color,
})
.collect();
Ok(Some(ProjectTranscript {
file_path: media_file.file_path.clone(),
segments: segment_outputs,
speakers: speaker_outputs,
}))
}