Laurence
19c3e156a0
A runnable, self-hosted web interface so the whole workflow can be tested end to end: record or upload audio in the browser, watch it transcribe and summarise, browse a library, play back, and export. What changed: - server/app/store.py: SQLite + on-disk audio storage for recordings (stdlib only). - server/app/pipeline.py: background task audio -> transcribe -> summarise via the existing provider layer, updating status (queued/transcribing/summarising/done/error). - server/app/main.py: web API - POST upload, list, detail, audio (Range), delete, and export (txt/md/srt/vtt/json) - and serves the SPA at /. - server/app/web/: Plaud-style single-page UI (index.html, styles.css, app.js). Sidebar library, in-browser recording (MediaRecorder) + file upload, live status polling, audio player, summary (overview/key points/actions), timestamped transcript, exports. - server/Dockerfile + README: two-minute run instructions (default provider: Groq free tier for both Whisper + LLM), and a Docker option. - config: env prefix switched OPENSCRIBE_ -> NIGHTJAR_ to match the brand and the site tutorials; .env.example rewritten with a ready Groq quick-start. - state/TODO: web app recorded as done. Why: - User asked for a Plaud-like web interface to test how it all works. Nothing testable existed before (marketing site is a brochure; pipeline was unwired). This delivers a real, runnable product demo and effectively lands M5/M6 for the HTTP providers. Notes: - Slim by design: AI is offloaded to the configured provider, so no local ML deps needed for the demo. Byte-compiles clean; JS passes node --check. Local faster-whisper still needs its model (M5) for the fully-offline path. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
55 lines
No EOL
2.1 KiB
Python
55 lines
No EOL
2.1 KiB
Python
# SPDX-License-Identifier: GPL-3.0-only
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"""Server configuration, loaded from environment / .env (see .env.example).
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Everything points at self-hosted services by default: local object storage, a local
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Ollama, and a local faster-whisper model. Nothing here requires a proprietary cloud.
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"""
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from __future__ import annotations
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from pydantic_settings import BaseSettings, SettingsConfigDict
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class Settings(BaseSettings):
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model_config = SettingsConfigDict(env_prefix="NIGHTJAR_", env_file=".env")
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# API
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api_token: str = "change-me" # bearer token for write endpoints
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# Storage: "local" | "s3" | "webdav"
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storage_backend: str = "local"
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local_media_dir: str = "./media"
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# S3-compatible (MinIO) - used when storage_backend == "s3"
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s3_endpoint: str = "http://localhost:9000"
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s3_bucket: str = "openscribe"
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s3_access_key: str = ""
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s3_secret_key: str = ""
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# Metadata DB (SQLite to start; Postgres URL later)
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database_url: str = "sqlite:///./openscribe.db"
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# --- AI providers (see docs/ai-providers.md) --------------------------------------
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# Transcription provider: "local_whisper" (self-hosted) | "openai_compatible"
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transcription_provider: str = "local_whisper"
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transcription_base_url: str = "" # e.g. https://api.openai.com/v1 or a Groq/local URL
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transcription_api_key: str = ""
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transcription_model: str = "" # e.g. whisper-1, whisper-large-v3
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# LLM provider for summaries + Ask-AI:
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# "ollama" (default, self-hosted) | "openai_compatible" | "anthropic"
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llm_provider: str = "ollama"
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llm_base_url: str = "https://api.openai.com/v1" # used by openai_compatible
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llm_api_key: str = ""
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llm_model: str = "" # per-provider default applied if empty
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# Local faster-whisper (used when transcription_provider == local_whisper)
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whisper_model: str = "base" # tiny|base|small|medium|large-v3
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whisper_device: str = "cpu" # cpu|cuda
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whisper_compute_type: str = "int8"
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# Local Ollama (used when llm_provider == ollama)
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ollama_url: str = "http://localhost:11434"
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ollama_model: str = "llama3.1"
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settings = Settings() |