#!/usr/bin/env python """ GritAI โ€” Audio โ†’ Notes (Project #7 of "30 AI Projects in 15 Days") Upload a recording or record from the mic -> a clean TRANSCRIPT with per-minute [mm:ss] timestamps (quotable by the minute) -> a short SUMMARY + a list of ACTION ITEMS (task / owner / due). FREE tier: faster-whisper ("small") on HF ZeroGPU for transcription + one Claude (haiku) call with forced tool-use for schema-valid {summary, action_items}. PRO tier ("GritAI Studio"): long meetings, SPEAKER attribution (who said what), a searchable/quotable archive, and auto-generated briefing docs -- the composition of several 30-in-15 rungs, assembled later. Shown here as a waitlist. Two-stage pipeline: transcription runs inside @spaces.GPU (GPU held only for that); the summarization Claude call runs after, on CPU, so the GPU is released before the network call. """ import os import gradio as gr ON_SPACE = bool(os.environ.get("SPACE_ID")) # True when running on a Hugging Face Space WHISPER_SIZE = "base" # int8 on CPU โ€” fast + reliable for short clips, no CUDA libs needed CLAUDE_MODEL = os.environ.get("ANTHROPIC_MODEL", "claude-haiku-4-5") MAX_TRANSCRIPT_CHARS = 24000 # keep the summarize call comfortably in-context _MODEL = None def _get_model(): """Construct the faster-whisper model on CPU (int8), cached. Runs anywhere โ€” no GPU / CUDA libs.""" global _MODEL if _MODEL is None: from faster_whisper import WhisperModel _MODEL = WhisperModel(WHISPER_SIZE, device="cpu", compute_type="int8") return _MODEL # Warm the model at startup on the Space so the first request isn't slow. if ON_SPACE: try: _get_model() print("[startup] faster-whisper (base, cpu/int8) loaded", flush=True) except Exception as _e: print(f"[startup] whisper load failed (will retry on first call): {_e}", flush=True) def _mmss(seconds): seconds = int(seconds or 0) return f"{seconds // 60:02d}:{seconds % 60:02d}" def _transcribe(audio_path): try: model = _get_model() except ImportError: raise gr.Error( "๐ŸŽ™๏ธ Local UI preview โ€” transcription runs on the Space (faster-whisper isn't installed here). " "The controls and Studio waitlist are live." ) segments, info = model.transcribe(audio_path, vad_filter=True, beam_size=5) rows = [(seg.start, seg.end, seg.text.strip()) for seg in segments] return rows, getattr(info, "language", "?") NOTES_TOOL = { "name": "emit_notes", "description": "Return a concise summary and the action items from a meeting/voice-memo transcript.", "input_schema": { "type": "object", "properties": { "summary": {"type": "string", "description": "3-5 sentence plain-English summary."}, "action_items": { "type": "array", "items": { "type": "object", "properties": { "task": {"type": "string"}, "owner": {"type": "string", "description": "Person responsible, or 'unassigned'."}, "due": {"type": "string", "description": "Due date/'when', or 'not stated'."}, }, "required": ["task"], }, }, }, "required": ["summary", "action_items"], }, } def _summarize(transcript): key = os.environ.get("ANTHROPIC_API_KEY") if not key: return "_(Set ANTHROPIC_API_KEY on the Space to get the summary + action items.)_", "" try: import anthropic client = anthropic.Anthropic(api_key=key) msg = client.messages.create( model=CLAUDE_MODEL, max_tokens=1024, tools=[NOTES_TOOL], tool_choice={"type": "tool", "name": "emit_notes"}, messages=[{"role": "user", "content": "Summarize this transcript and extract the action items.\n\n" + transcript[:MAX_TRANSCRIPT_CHARS]}], ) data = next((b.input for b in msg.content if getattr(b, "type", "") == "tool_use"), {}) except Exception as e: return f"_(Summary unavailable: {type(e).__name__})_", "" summary = data.get("summary", "").strip() items = data.get("action_items", []) or [] if items: lines = [] for it in items: task = (it.get("task") or "").strip() owner = (it.get("owner") or "unassigned").strip() due = (it.get("due") or "not stated").strip() lines.append(f"- **{task}** โ€” _{owner}_ ยท {due}") actions_md = "\n".join(lines) else: actions_md = "_No action items found._" return summary or "_(no summary)_", actions_md def process(audio_path, progress=gr.Progress(track_tqdm=True)): if not audio_path: raise gr.Error("Upload a file or record from the mic first.") rows, lang = _transcribe(audio_path) if not rows: raise gr.Error("No speech detected in that audio.") transcript = "\n".join(f"[{_mmss(s)}] {text}" for s, e, text in rows if text) summary, actions_md = _summarize(transcript) return transcript, summary, actions_md # ----------------------------------------------------------------------------- UI THEME = gr.themes.Base( primary_hue=gr.themes.colors.orange, neutral_hue=gr.themes.colors.slate, font=[gr.themes.GoogleFont("IBM Plex Sans"), "system-ui", "sans-serif"], font_mono=[gr.themes.GoogleFont("IBM Plex Mono"), "monospace"], ).set( body_background_fill="#0a0b0e", body_text_color="#eef0f4", body_text_color_subdued="#9aa1b0", background_fill_primary="#111318", background_fill_secondary="#0e1015", block_background_fill="#111318", block_border_color="#20242e", block_label_background_fill="#0e1015", block_label_text_color="#9aa1b0", block_title_text_color="#eef0f4", border_color_primary="#20242e", border_color_accent="#c9451f", input_background_fill="#0e1015", input_border_color="#2b303c", input_border_color_focus="#c9451f", button_primary_background_fill="#ff5a2a", button_primary_background_fill_hover="#ff7d54", button_primary_text_color="#140a06", button_primary_border_color="#c9451f", button_secondary_background_fill="#15181f", button_secondary_text_color="#eef0f4", button_secondary_border_color="#2b303c", color_accent="#ff5a2a", color_accent_soft="#2b1810", slider_color="#ff5a2a", checkbox_background_color_selected="#ff5a2a", checkbox_border_color_focus="#c9451f", ) CSS = """ @import url('https://fonts.googleapis.com/css2?family=Archivo:wght@700;800;900&family=IBM+Plex+Mono:wght@400;500;600&family=IBM+Plex+Sans:wght@400;500;600&display=swap'); .gradio-container{background:#0a0b0e !important} .gradio-container::before{content:"";position:fixed;inset:0;z-index:0;pointer-events:none; background:repeating-linear-gradient(90deg,transparent 0 63px,rgba(255,255,255,.018) 63px 64px), repeating-linear-gradient(0deg,transparent 0 63px,rgba(255,255,255,.018) 63px 64px), radial-gradient(120% 70% at 50% -10%,#141821 0%,#0a0b0e 55%)} .main,.contain{position:relative;z-index:1} #ghead{display:flex;align-items:center;gap:14px;padding:6px 2px 4px} #ghead .co{font-family:'Archivo',sans-serif;font-weight:900;font-size:19px;letter-spacing:.5px;color:#eef0f4;line-height:1} #ghead .co b{color:#ff5a2a} #ghead .sub{font-family:'IBM Plex Mono',monospace;font-size:10.5px;letter-spacing:.28em;text-transform:uppercase;color:#5f6675;margin-top:5px} #ghead .tags{margin-left:auto;display:flex;gap:8px} #ghead .tag{font-family:'IBM Plex Mono',monospace;font-size:10px;letter-spacing:.14em;text-transform:uppercase;color:#9aa1b0;border:1px solid #2b303c;border-radius:2px;padding:5px 9px} #ghead .tag.hot{color:#ff5a2a;border-color:#c9451f} #ghero h1{font-family:'Archivo',sans-serif;font-weight:800;font-size:clamp(26px,4.2vw,40px);line-height:1.05;letter-spacing:-.02em;margin:14px 0 4px;color:#eef0f4} #ghero h1 .g{color:#ff5a2a} #ghero p{color:#9aa1b0;margin:0;font-size:14.5px} footer{visibility:hidden} button.selected{color:#ff5a2a !important} .gritai-foot{text-align:center;color:#5f6675;font-size:.8rem;margin-top:16px;font-family:'IBM Plex Mono',monospace;letter-spacing:.05em} .gritai-foot a{color:#9aa1b0;text-decoration:none} .pro-card{border:1px solid #2b303c;border-left:2px solid #ff5a2a;border-radius:4px;padding:16px 18px;background:#111318} .pro-card h3{color:#eef0f4} """ with gr.Blocks(theme=THEME, css=CSS, title="GritAI ยท Audio โ†’ Notes") as demo: gr.HTML('''
GRITAI
Audio → Notes
▸ LiveSDVOSBLawton OK

Audio in. Notes out.

Timestamped transcript + summary + action items · quotable by the minute · Project #7 of 30 in 15 days.

''') with gr.Tab("Transcribe & summarize (free)"): with gr.Row(): with gr.Column(scale=2): audio = gr.Audio(sources=["upload", "microphone"], type="filepath", label="Audio") go = gr.Button("Transcribe โ–ถ", variant="primary") gr.Markdown("Best on short clips (a few minutes). Long meetings + speaker labels " "are the Pro tier.") with gr.Column(scale=3): summary = gr.Markdown(label="Summary") actions = gr.Markdown(label="Action items") transcript = gr.Textbox(label="Transcript ( [mm:ss] per line )", lines=16, show_copy_button=True) go.click(process, inputs=[audio], outputs=[transcript, summary, actions], api_name="transcribe") with gr.Tab("GritAI Studio (Pro)"): gr.Markdown( "
\n\n" "### ๐Ÿš€ Beyond the free demo\n" "The free tier transcribes short clips and pulls a summary + action items. **GritAI Studio** " "turns audio into an actual knowledge base:\n\n" "- **Long meetings** โ€” hours, not minutes.\n" "- **Speaker attribution** โ€” who said what, quotable by **minute _and_ speaker**.\n" "- **Searchable & quotable archive** โ€” find any moment across every recording, with citations.\n" "- **Auto-briefings** โ€” one-click briefing docs generated from the transcript.\n\n" "*Built on our own stack โ€” the composition of several GritAI tools into one briefing room.*\n\n" "
" ) with gr.Row(): email = gr.Textbox(label="Email", placeholder="you@team.com", scale=3) use_case = gr.Textbox(label="What would you use it for?", placeholder="e.g. weekly stand-ups", scale=4) join = gr.Button("Join the Studio waitlist", variant="primary") waitlist_msg = gr.Markdown(visible=False) def _join(email_v, use_case_v): if not email_v or "@" not in email_v: return gr.update(value="โš ๏ธ Please enter a valid email.", visible=True) return gr.update(value="โœ… You're on the list โ€” we'll reach out when GritAI Studio opens. Thanks!", visible=True) join.click(_join, inputs=[email, use_case], outputs=waitlist_msg) gr.HTML("
faster-whisper + Claude · " "GRITAI SOLUTIONS · 30-in-15 №7
") if __name__ == "__main__": demo.queue(max_size=20).launch(server_name="0.0.0.0", server_port=int(os.environ.get("PORT", 7860)))