From 376a48429da081f568075ca5053e3cfc44fafeff Mon Sep 17 00:00:00 2001 From: Harshit Ghosh Date: Tue, 14 Apr 2026 18:22:46 -0700 Subject: [PATCH 01/10] feat(primary): widen history window from 6 to 20 turns Ana now sees the last ~10 exchanges instead of 3 when drafting a reply. Conversation state has always kept the full history; only the prompt slice was narrow. Llama 3.3 70B context is 128k tokens, so even a 40-turn call fits comfortably; the old 6-turn slice was a leftover from early testing. --- constella/primary.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/constella/primary.py b/constella/primary.py index 3de51e2..c635402 100644 --- a/constella/primary.py +++ b/constella/primary.py @@ -30,7 +30,7 @@ def build_user_prompt(state: ConversationState, last_patient_text: str) -> str: history_lines = [] - for turn in state.history[-6:]: # last 3 exchanges + for turn in state.history[-20:]: # last 20 turns (~10 exchanges) speaker = "Patient" if turn.speaker == "patient" else "Nurse Ana" history_lines.append(f"{speaker}: {turn.text}") history_block = "\n".join(history_lines) if history_lines else "(start of call)" From d767d782ee99afebce817cfd9041458d64b35c07 Mon Sep 17 00:00:00 2001 From: Harshit Ghosh Date: Tue, 14 Apr 2026 18:25:07 -0700 Subject: [PATCH 02/10] docs: realtime voice architecture plan MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Design doc for replacing the record-send-reply Gradio UX with a Pipecat + Deepgram + Cartesia + Groq streaming pipeline. Constellation stays identical; only the I/O layer changes. Covers pipeline topology, parallelism strategy (A/B/C), session state, barge-in, failure modes, env vars, tradeoffs, and phased implementation. Nothing implemented yet — review before writing code. --- docs/realtime_architecture.md | 207 ++++++++++++++++++++++++++++++++++ 1 file changed, 207 insertions(+) create mode 100644 docs/realtime_architecture.md diff --git a/docs/realtime_architecture.md b/docs/realtime_architecture.md new file mode 100644 index 0000000..4131aa6 --- /dev/null +++ b/docs/realtime_architecture.md @@ -0,0 +1,207 @@ +# Realtime Voice — Backend Architecture + +> **Status:** design doc. Nothing in this document is implemented yet on this branch (`feat/realtime-voice`). This is the plan we're reviewing before writing code. + +## 1. Goal + +Replace the current record-send-reply Gradio UX with a phone-call feel: + +- Audio streams continuously in both directions over WebRTC. +- Server-side VAD decides when the patient has stopped talking. +- Ana's voice starts speaking 300-500 ms after the patient stops — not 2-3 s. +- The patient can interrupt Ana mid-sentence (barge-in). +- The **existing 4-specialist constellation is preserved** — it's Constella's whole point. We just swap out the I/O layer. + +What this is **not**: a rewrite into an end-to-end speech-to-speech model (OpenAI Realtime, Gemini Live). Those collapse the text LLM layer and would bypass the constellation entirely. + +## 2. Target stack + +| Stage | Today | Target | Why the swap | +|---|---|---|---| +| Transport | Gradio `gr.Audio` push-to-talk | WebRTC (Pipecat small-webrtc transport) | Bidirectional streaming, barge-in support | +| VAD | None — user clicks stop | Silero VAD (local, runs in Pipecat) | No click, automatic turn boundaries | +| ASR | Groq Whisper (batch) | Deepgram `nova-3` streaming | Streaming partials, ~150 ms finalization after EOT | +| Primary LLM | Groq Llama 3.3 70B (sync) | Groq Llama 3.3 70B (streaming) | Stream tokens into TTS instead of waiting for full reply | +| Specialists | Groq Llama 3.1 8B ×4, parallel | **unchanged** — Groq Llama 3.1 8B ×4, parallel | Constellation logic stays identical | +| TTS | gTTS (batch MP3) | Cartesia Sonic-2 multilingual (streaming) | ~90 ms first-audible, real multilingual (en/es) | +| Orchestration | single `run_turn()` call | Pipecat `Pipeline` of `FrameProcessor`s | Native streaming, interruption, turn-taking | + +**Keeping Groq for LLMs** because: (a) the constellation pattern has zero reason to change providers, (b) Groq's LPU latencies (~250 ms TTFT 70B, ~80 ms TTFT 8B) are what make streaming worth doing. + +## 3. Pipeline topology + +Pipecat organizes the runtime as a linear chain of `FrameProcessor`s. Audio frames, transcription frames, LLM frames, and TTS audio frames flow through in order. Our chain: + +``` + Mic (WebRTC) + │ AudioRawFrame (20 ms chunks) + ▼ + ┌─────────────────────────┐ + │ SileroVADAnalyzer │ decides when user started/stopped speaking + └─────────────┬───────────┘ + │ UserStartedSpeakingFrame / UserStoppedSpeakingFrame + ▼ + ┌─────────────────────────┐ + │ DeepgramSTTService │ streaming ASR, emits partial + final + └─────────────┬───────────┘ + │ TranscriptionFrame (final, on VAD stop) + ▼ + ┌─────────────────────────────────────────────────┐ + │ ConstellationProcessor ← THE NEW THING │ + │ │ + │ 1. Append patient turn to ConversationState │ + │ 2. Fan out in parallel: │ + │ - primary.primary_respond() (streaming) │ + │ - language_specialist() │ + │ - labs_specialist() │ + │ - escalation_specialist() │ + │ - (medication waits on primary's draft) │ + │ 3. Await medication specialist on primary draft │ + │ 4. Merge verdicts → NextAction │ + │ 5. Rewrite if needed (blocks TTS) │ + │ 6. Emit final TextFrame(s) for TTS │ + │ 7. Append nurse turn to ConversationState │ + └─────────────┬───────────────────────────────────┘ + │ TextFrame (nurse reply) + ▼ + ┌─────────────────────────┐ + │ CartesiaTTSService │ streaming TTS, emits audio chunks + └─────────────┬───────────┘ + │ TTSAudioRawFrame + ▼ + Speaker (WebRTC) +``` + +The **critical design decision** is step 2: parallelism between primary and specialists. + +### 3a. Where the constellation plugs in + +Today `run_turn()` in `orchestrator.py` is a single synchronous function. In Pipecat it becomes a `FrameProcessor` subclass (`ConstellationProcessor`). The internals do the same work — primary + 4 specialists + merge — but the return is streamed as `TextFrame`s instead of returned as a string. + +``` +constella/ +├── orchestrator.py # unchanged — run_turn() stays, used by eval harness +├── realtime/ # NEW +│ ├── __init__.py +│ ├── pipeline.py # Pipecat pipeline setup + transport wiring +│ ├── constellation.py # ConstellationProcessor (FrameProcessor subclass) +│ └── session.py # Per-call ConversationState, lifecycle hooks +└── demo/ + ├── app.py # existing Gradio demo — stays, labelled "legacy" + └── realtime.py # NEW constella-realtime entrypoint +``` + +Why a separate folder instead of modifying `orchestrator.py`: the eval harness (`constella-eval`) calls `run_turn()` synchronously and writes per-turn latency to a JSON report. Keeping it sync-friendly means eval keeps working without any changes. + +## 4. Running the specialists: parallelism strategy + +Three options, in increasing complexity: + +### Option A — constellation-then-speak (MVP) + +Run the whole `run_turn()` to completion, then stream the final text into TTS. Simplest mental model; easiest rollback if specialists hang. + +**Perceived latency (best case, Groq):** + +| Stage | Time | +|---|---| +| Deepgram ASR finalization after EOT | 150 ms | +| Primary (70B, full response) | 500 ms | +| Specialists in parallel (max) | 250 ms (overlaps with primary, effectively 0 ms added) | +| Rewrite if needed | +400 ms (rare path) | +| Cartesia TTS first audible | 90 ms | +| **Total EOT → Ana's first audio** | **~740 ms happy path** | + +That's 3× better than today (~2.5 s) and below the 1 s threshold where conversation stops feeling snappy. + +### Option B — stream primary, run specialists concurrently, interrupt on flag + +Start TTS on primary's first tokens. Specialists run in parallel with TTS playback. If escalation/medication fires a flag, cut TTS mid-word with `BotInterruptionFrame` and replace with rewrite/escalation text. + +**Perceived latency:** ~300-400 ms EOT → first audio. Near-Gemini-Live feel. + +**Cost:** patient briefly hears the unsafe draft before it gets cut off. For a medication harm case (e.g., "take 200 mg of …" [cut off]), that's arguably worse than silence — it teaches the wrong dose for half a second. + +### Option C — hybrid: gate TTS on safety-critical specialists only + +- Start specialists on patient utterance immediately (language, labs, escalation run on patient text — no dependency on primary). +- Primary streams into a buffer, not directly to TTS. +- TTS starts as soon as **medication specialist returns safe** AND **escalation specialist returns non-urgent**. +- Labs followup appended at end, no TTS gate needed. + +Roughly: if max(medication, escalation) < primary generation time, zero added latency over Option B — but with safety preserved. + +### Recommendation + +**Ship Option A first.** It's a 4× UX improvement with the lowest implementation risk and no new safety questions. Once that's stable, Option C is a latency optimization that can be added incrementally — the `ConstellationProcessor` interface doesn't change. + +## 5. Session state + +Today: `ConversationState` lives in Gradio's `gr.State` and gets serialized/deserialized per turn. + +New: `ConversationState` lives inside the `ConstellationProcessor` instance. One processor = one call. When the WebRTC session ends, the processor is destroyed. For multi-call history (not in scope for the MVP) we'd add a Redis-backed session store. + +The recent `[-20:]` history-window change applies automatically — the processor just calls `primary_respond(state, utterance)` with the accumulating state, same as today. + +## 6. Interruption / barge-in + +Pipecat handles this natively: + +1. While TTS is playing, VAD detects user speech. +2. Pipeline emits `UserStartedSpeakingFrame`. +3. Cartesia TTS receives `BotInterruptionFrame`, cancels current synthesis, flushes audio buffer. +4. Speaker goes silent within ~100 ms. +5. Deepgram starts transcribing the new utterance. + +No custom code needed beyond wiring these standard frames. The `ConstellationProcessor` needs to handle being mid-generation when interrupted — it should cancel the in-flight primary/specialist calls (their HTTP connections) and move on to the new patient utterance. + +## 7. Failure modes + +| Failure | Behavior | +|---|---| +| Deepgram WebSocket drops | Pipecat auto-reconnects; transient loss of a partial transcript, final still fires on EOT | +| Specialist returns `None` (structured validation failed 3x) | Same as today — treat as "no verdict", orchestrator logs warning | +| Primary LLM timeout | Fall back to a hard-coded "I didn't catch that — could you repeat?" safety phrase, don't surface an error to the user | +| Cartesia TTS fails | Fall back to gTTS synchronously (slow, but better than silence); log alert | +| VAD false trigger on background noise | Deepgram gets empty audio; the final `TranscriptionFrame` is `""` → `ConstellationProcessor` skips the turn | + +## 8. Env vars / API keys + +| Key | Provider | Purpose | Free tier? | +|---|---|---|---| +| `GROQ_API_KEY` | Groq | LLMs (unchanged) | yes | +| `DEEPGRAM_API_KEY` | Deepgram | Streaming ASR | yes, $200 free credit | +| `CARTESIA_API_KEY` | Cartesia | Streaming TTS | yes, free tier covers dev | + +Both Deepgram and Cartesia sign-ups are ~2 minutes each. You'll hand me those keys before Phase 3 below. + +## 9. Known tradeoffs vs today + +- **Spanglish code-switching on ASR.** Deepgram's `language=multi` detects one language per utterance, not mid-utterance switches. This is a regression vs. Groq Whisper, which handles Spanglish well. Mitigation: use Deepgram streaming for the live feel, then re-transcribe the final audio buffer with Groq Whisper for the canonical transcript that feeds specialists. Costs ~200 ms on EOT; acceptable. Alternative: evaluate Gladia Solaria, which markets Spanglish as a first-class feature. +- **No multilingual gTTS-style free path.** Cartesia is paid (but cheap). gTTS stays as a fallback. +- **Gradio text-input fallback.** The current demo's "type instead of speak" path is useful for dev. The new realtime page will have a text-input box too, but it bypasses ASR and feeds directly into the `ConstellationProcessor`. + +## 10. Implementation phases + +1. **Scaffold Pipecat dependencies.** Add `pipecat-ai[deepgram,cartesia,silero,webrtc]` to pyproject, pin versions, lock uv. +2. **Wire `ConstellationProcessor`.** Wrap `run_turn()`. Keep Option A (no streaming primary) for v1. Unit-test with a fake Pipecat pipeline that injects `TranscriptionFrame`s. +3. **Stand up the WebRTC transport.** Pipecat's `SmallWebRTCTransport` serves a static HTML page that opens a WebRTC peer connection to the Python server. +4. **Replace `constella-demo` default route.** `constella-demo` points at the new realtime page; `constella-demo --legacy` keeps the current Gradio app. +5. **Smoke-test server boot + text-input path.** No mic needed — verify the pipeline processes a typed patient utterance end-to-end and produces audio out. +6. **User validates the live voice loop.** You speak, listen, report. I iterate on VAD thresholds, TTS voice selection, etc. +7. **(Later) Option C latency optimization.** Gate TTS on medication + escalation specialists, not the full merge. + +## 11. What stays the same + +- `constella/orchestrator.py`, `constella/primary.py`, `constella/specialists/*.py`, `constella/schemas.py` — zero changes. +- `constella/eval/` — zero changes, still batch-synchronous. +- `tests/test_schemas.py` — zero changes. + +This is the point: the whole constellation is an I/O-agnostic function. The realtime work is purely a transport + streaming layer around it. + +## 12. Open questions for review + +1. **Option A vs C for MVP?** I recommend A. Confirms? +2. **Spanglish ASR fallback to Groq Whisper on EOT — acceptable 200 ms overhead?** +3. **Keep the Gradio demo parallel to the new one, or retire it?** I recommend keeping it as `constella-demo --legacy` for dev testing. +4. **Deepgram vs Gladia for ASR?** Deepgram for MVP (larger ecosystem, better docs); revisit if Spanglish accuracy is a real problem. From ad50c789a19711c9e0e7218e7049734d158a2cfb Mon Sep 17 00:00:00 2001 From: Harshit Ghosh Date: Tue, 14 Apr 2026 18:47:01 -0700 Subject: [PATCH 03/10] feat: realtime voice demo via fastrtc + Cartesia streaming TTS MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Adds constella-realtime alongside the existing constella-demo: - fastrtc handles WebRTC + Silero VAD for auto-commit-on-pause - Groq Whisper still does ASR (batch, but on a short clip from VAD) - Cartesia Sonic-3 streams TTS for ~100 ms first-audible latency - Constellation (primary + 4 specialists + orchestrator) is unchanged New layout: constella/realtime/ tts.py, audio.py — Cartesia + numpy/WAV helpers constella/demo/realtime.py — fastrtc Stream entrypoint The legacy push-to-talk Gradio demo (constella-demo) is untouched and still the right tool for dev without a Cartesia key. --- SETUP.md | 5 + constella/demo/realtime.py | 123 +++++ constella/realtime/__init__.py | 5 + constella/realtime/audio.py | 44 ++ constella/realtime/tts.py | 64 +++ pyproject.toml | 4 + uv.lock | 900 ++++++++++++++++++++++++++++----- 7 files changed, 1016 insertions(+), 129 deletions(-) create mode 100644 constella/demo/realtime.py create mode 100644 constella/realtime/__init__.py create mode 100644 constella/realtime/audio.py create mode 100644 constella/realtime/tts.py diff --git a/SETUP.md b/SETUP.md index 5211d13..e6a3992 100644 --- a/SETUP.md +++ b/SETUP.md @@ -40,6 +40,11 @@ Create `.env` in the project root: ```bash cat > .env << 'EOF' GROQ_API_KEY=your_groq_key_here +# Required for the realtime demo (constella-realtime); not used by the legacy +# push-to-talk demo (constella-demo) or the eval harness. +CARTESIA_API_KEY=your_cartesia_key_here +# Optional: override the default Cartesia voice (UUID from play.cartesia.ai/voices) +# CARTESIA_VOICE_ID= ANTHROPIC_API_KEY=optional_for_llm_judge_eval HF_HOME=/Users/hgz/.cache/huggingface EOF diff --git a/constella/demo/realtime.py b/constella/demo/realtime.py new file mode 100644 index 0000000..872284f --- /dev/null +++ b/constella/demo/realtime.py @@ -0,0 +1,123 @@ +"""Realtime voice demo — auto-commit on silence via fastrtc + Cartesia streaming TTS. + +Per-turn pipeline: + mic → fastrtc VAD → Groq Whisper → run_turn() (constellation) → Cartesia → speaker + +Unlike the push-to-talk Gradio demo in `app.py`, this: + - detects end-of-utterance automatically (no click-to-stop) + - starts Ana's voice ~1 s after the patient's last word instead of ~2.5 s + - uses Cartesia Sonic-3 (multilingual) in place of gTTS for natural prosody + +Usage: + constella-realtime # localhost:7860, open in browser + constella-realtime --port 8080 +""" +from __future__ import annotations + +import argparse +import logging +import os +from collections.abc import Iterator +from pathlib import Path + +import numpy as np + +from constella.orchestrator import run_turn +from constella.realtime.audio import cartesia_pcm_to_numpy, numpy_to_wav_tempfile +from constella.realtime.tts import SAMPLE_RATE as TTS_SAMPLE_RATE, stream_tts +from constella.schemas import ConversationState, PatientContext + +log = logging.getLogger(__name__) + +DEFAULT_PATIENT = ( + Path(__file__).resolve().parents[1] / "eval" / "scenarios" / "patient_maria.json" +) + +# One ConversationState per Python process. Good enough for a single-user demo; +# a prod deployment would key state by fastrtc session_id. +_state: ConversationState | None = None + + +def _load_patient() -> PatientContext: + return PatientContext.model_validate_json(DEFAULT_PATIENT.read_text()) + + +def _transcribe(wav_path: Path) -> str: + """Groq Whisper. Batch — fastrtc only calls the handler on VAD pause, so + the audio is already a complete utterance, typically 2-5 seconds.""" + from openai import OpenAI + + api_key = os.environ.get("GROQ_API_KEY") + if not api_key: + raise RuntimeError("GROQ_API_KEY required for ASR in the realtime demo.") + client = OpenAI(api_key=api_key, base_url="https://api.groq.com/openai/v1") + with open(wav_path, "rb") as f: + resp = client.audio.transcriptions.create( + model="whisper-large-v3-turbo", + file=f, + response_format="text", + ) + return str(resp).strip() + + +def _handler(audio: tuple[int, np.ndarray]) -> Iterator[tuple[int, np.ndarray]]: + """fastrtc invokes this once per turn, with the full utterance since last pause.""" + global _state + if _state is None: + _state = ConversationState(patient=_load_patient()) + + sample_rate, np_audio = audio + wav_path = numpy_to_wav_tempfile(sample_rate, np_audio) + try: + patient_text = _transcribe(wav_path) + finally: + wav_path.unlink(missing_ok=True) + + if not patient_text: + log.info("empty transcription, skipping turn") + return + + log.info("patient: %s", patient_text) + result = run_turn(_state, patient_text) + _state = result.state + log.info( + "nurse [%s, %.0f ms]: %s", + result.action.kind, + result.total_latency_ms, + result.nurse_text, + ) + + # Cartesia language hint: "mix" isn't a valid value, so drop to None + # and let Sonic-3 auto-handle code-switched text. + lang_tag = result.language.primary_lang if result.language else None + cartesia_lang = lang_tag if lang_tag in ("en", "es") else None + + for pcm_chunk in stream_tts(result.nurse_text, language=cartesia_lang): + yield cartesia_pcm_to_numpy(pcm_chunk, TTS_SAMPLE_RATE) + + +def build_stream(): + from fastrtc import ReplyOnPause, Stream + + return Stream( + handler=ReplyOnPause(_handler), + modality="audio", + mode="send-receive", + ) + + +def main() -> int: + logging.basicConfig( + level=logging.INFO, + format="%(asctime)s %(levelname)s %(name)s %(message)s", + ) + p = argparse.ArgumentParser() + p.add_argument("--port", type=int, default=7860) + args = p.parse_args() + stream = build_stream() + stream.ui.launch(server_port=args.port) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/constella/realtime/__init__.py b/constella/realtime/__init__.py new file mode 100644 index 0000000..8bc1670 --- /dev/null +++ b/constella/realtime/__init__.py @@ -0,0 +1,5 @@ +"""Realtime voice pipeline: fastrtc transport + Cartesia streaming TTS. + +The realtime demo (`constella-realtime`) uses these modules. The legacy +push-to-talk Gradio demo in `constella.demo.app` does not touch this package. +""" diff --git a/constella/realtime/audio.py b/constella/realtime/audio.py new file mode 100644 index 0000000..e803e13 --- /dev/null +++ b/constella/realtime/audio.py @@ -0,0 +1,44 @@ +"""Audio format conversion for the realtime pipeline. + +fastrtc hands us `(sample_rate, np.ndarray)` tuples; Groq Whisper wants a file +path to a WAV; Cartesia streams raw `pcm_f32le` bytes back to us. These helpers +bridge those three representations. +""" +from __future__ import annotations + +import tempfile +import wave +from pathlib import Path + +import numpy as np + + +def numpy_to_wav_tempfile(sample_rate: int, audio: np.ndarray) -> Path: + """Write an fastrtc-shape audio array to a temp WAV that Whisper accepts. + + fastrtc delivers `(1, N)` mono audio. We squeeze to 1D, coerce to int16, + and emit a standard 16-bit PCM WAV. Caller is responsible for deleting + the file once Whisper has finished with it. + """ + mono = audio.squeeze() + if mono.dtype != np.int16: + if mono.dtype.kind == "f": + mono = np.clip(mono * 32767.0, -32768, 32767).astype(np.int16) + else: + mono = mono.astype(np.int16) + + tmp = tempfile.NamedTemporaryFile(suffix=".wav", delete=False) + with wave.open(tmp.name, "wb") as wf: + wf.setnchannels(1) + wf.setsampwidth(2) + wf.setframerate(int(sample_rate)) + wf.writeframes(mono.tobytes()) + return Path(tmp.name) + + +def cartesia_pcm_to_numpy( + pcm_bytes: bytes, sample_rate: int +) -> tuple[int, np.ndarray]: + """Convert a Cartesia `pcm_f32le` chunk into the `(sr, (1, N))` shape fastrtc expects.""" + arr = np.frombuffer(pcm_bytes, dtype=np.float32) + return (sample_rate, arr.reshape(1, -1)) diff --git a/constella/realtime/tts.py b/constella/realtime/tts.py new file mode 100644 index 0000000..eb610e7 --- /dev/null +++ b/constella/realtime/tts.py @@ -0,0 +1,64 @@ +"""Streaming TTS via Cartesia Sonic-3. + +Yields raw `pcm_f32le` bytes as the model synthesizes. The realtime handler +converts those chunks into fastrtc audio frames so Ana's voice starts playing +within ~100 ms of the first token arriving from the primary agent. +""" +from __future__ import annotations + +import logging +import os +from collections.abc import Iterator +from typing import Literal + +log = logging.getLogger(__name__) + +# Default voice — Cartesia's multilingual example voice, handles en + es and +# reasonable code-switching. Override by setting CARTESIA_VOICE_ID to any +# voice UUID from https://play.cartesia.ai/voices. +_DEFAULT_VOICE_ID = "6ccbfb76-1fc6-48f7-b71d-91ac6298247b" +_MODEL_ID = "sonic-3" +SAMPLE_RATE = 44100 + + +def stream_tts( + text: str, + *, + language: Literal["en", "es"] | None = None, +) -> Iterator[bytes]: + """Yield `pcm_f32le` audio chunks as Cartesia synthesizes `text`. + + `language`: + - "en" or "es": hard-hints Cartesia to that language (cleaner prosody + when the language specialist is confident) + - None: let Sonic-3 auto-detect (right choice for code-switched output) + """ + from cartesia import Cartesia # lazy import so pytest doesn't pull websockets + + api_key = os.environ.get("CARTESIA_API_KEY") + if not api_key: + raise RuntimeError( + "CARTESIA_API_KEY is required for the realtime demo. Add it to .env." + ) + + voice_id = os.environ.get("CARTESIA_VOICE_ID", _DEFAULT_VOICE_ID) + client = Cartesia(api_key=api_key) + + with client.tts.websocket_connect() as conn: + ctx_kwargs: dict = { + "model_id": _MODEL_ID, + "voice": {"mode": "id", "id": voice_id}, + "output_format": { + "container": "raw", + "encoding": "pcm_f32le", + "sample_rate": SAMPLE_RATE, + }, + } + if language is not None: + ctx_kwargs["language"] = language + ctx = conn.context(**ctx_kwargs) + ctx.push(text) + ctx.no_more_inputs() + for response in ctx.receive(): + if response.type == "chunk" and response.audio: + yield response.audio diff --git a/pyproject.toml b/pyproject.toml index 3dbeb5c..b0dba04 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -16,12 +16,16 @@ dependencies = [ "gtts>=2.5", "python-dotenv>=1.0", "pytest>=8.0", + "fastrtc[vad]>=0.0.20", + "cartesia[websockets]>=2.0", + "numpy>=1.26", ] [project.scripts] constella-chat = "constella.cli:main" constella-eval = "constella.eval.run_eval:main" constella-demo = "constella.demo.app:main" +constella-realtime = "constella.demo.realtime:main" [build-system] requires = ["hatchling"] diff --git a/uv.lock b/uv.lock index f7beac0..421a37f 100644 --- a/uv.lock +++ b/uv.lock @@ -2,9 +2,12 @@ version = 1 revision = 3 requires-python = ">=3.11, <3.13" resolution-markers = [ - "sys_platform == 'win32'", - "sys_platform == 'emscripten'", - "sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version >= '3.12' and sys_platform == 'win32'", + "python_full_version < '3.12' and sys_platform == 'win32'", + "python_full_version >= '3.12' and sys_platform == 'emscripten'", + "python_full_version < '3.12' and sys_platform == 'emscripten'", + "python_full_version >= '3.12' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version < '3.12' and sys_platform != 'emscripten' and sys_platform != 'win32'", ] [[package]] @@ -16,6 +19,37 @@ wheels = [ { url = 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upload-time = "2025-03-05T20:03:39.41Z" }, +] From 0448f82bde64392d57335b65cb1099babaab750b Mon Sep 17 00:00:00 2001 From: Harshit Ghosh Date: Tue, 14 Apr 2026 19:21:47 -0700 Subject: [PATCH 04/10] fix(realtime): restore rich UI, correct TTS language hint, warn on Groq free tier MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Three fixes in the realtime demo: 1. UI parity with the legacy Gradio demo — patient intro, mic + text input, example lines, Ana's latest reply, running transcript, and the specialist-verdict JSON are all back. fastrtc's WebRTC component is embedded in a custom gr.Blocks layout via AdditionalOutputs. 2. Language mismatch bug — the TTS hint was derived from the language specialist's verdict on the PATIENT's utterance. When Ana code-switches to Spanish to match the patient's register, the hint was still 'en' and Cartesia synthesized Spanish text with an American accent. Now detected from Ana's reply text via a small heuristic (Spanish chars, accented vowels, function-word count). 3. Startup warning — Groq free tier (6000 TPM on 8B) cannot sustain the 5-concurrent-calls-per-turn burst of a realtime constellation. 429 retries add 2-8 s of backoff per turn. We now warn at boot and point at CONSTELLA_PROVIDER=openrouter or Groq Dev tier as fixes. Tests still green. --- constella/demo/realtime.py | 238 ++++++++++++++++++++++++++++++++----- 1 file changed, 205 insertions(+), 33 deletions(-) diff --git a/constella/demo/realtime.py b/constella/demo/realtime.py index 872284f..f7802e6 100644 --- a/constella/demo/realtime.py +++ b/constella/demo/realtime.py @@ -1,25 +1,25 @@ -"""Realtime voice demo — auto-commit on silence via fastrtc + Cartesia streaming TTS. +"""Realtime voice demo — auto-commit on silence via fastrtc + Cartesia. Per-turn pipeline: - mic → fastrtc VAD → Groq Whisper → run_turn() (constellation) → Cartesia → speaker + mic → fastrtc VAD → Groq Whisper → run_turn() → Cartesia streaming TTS → speaker -Unlike the push-to-talk Gradio demo in `app.py`, this: - - detects end-of-utterance automatically (no click-to-stop) - - starts Ana's voice ~1 s after the patient's last word instead of ~2.5 s - - uses Cartesia Sonic-3 (multilingual) in place of gTTS for natural prosody +UI is a Gradio Blocks layout: mic + text input + examples on the left, Ana's +reply + running transcript on the right, specialist verdict JSON at the bottom. Usage: - constella-realtime # localhost:7860, open in browser + constella-realtime # localhost:7860 constella-realtime --port 8080 """ from __future__ import annotations import argparse +import json import logging import os -from collections.abc import Iterator from pathlib import Path +from typing import Literal +import gradio as gr import numpy as np from constella.orchestrator import run_turn @@ -33,18 +33,51 @@ Path(__file__).resolve().parents[1] / "eval" / "scenarios" / "patient_maria.json" ) -# One ConversationState per Python process. Good enough for a single-user demo; -# a prod deployment would key state by fastrtc session_id. -_state: ConversationState | None = None +EXAMPLE_LINES = [ + "Hi, yes this is Maria.", + "I checked my sugar this morning and it was 142.", + "Hola, soy María. ¿Cómo está usted?", + "Pues mi blood sugar esta mañana estaba en 165, no muy mal.", + "Sí, tomé el metformin con breakfast pero el insulin lo olvidé last night.", + "Tengo un dolor fuerte en el pecho y me duele el brazo izquierdo.", +] + +_SPANISH_FUNCTION_WORDS = frozenset({ + "hola", "está", "sí", "bueno", "pero", "porque", "cómo", "qué", + "dónde", "entonces", "muy", "mucho", "también", "señora", "usted", + "soy", "eres", "tomó", "gracias", "bien", "tiene", "desde", "hospital", + "medicamento", "azúcar", "insulina", "doctor", "doctora", "ahora", + "poco", "ayer", "mañana", "preocupes", "quiero", "puedes", +}) def _load_patient() -> PatientContext: return PatientContext.model_validate_json(DEFAULT_PATIENT.read_text()) +def _detect_reply_language(text: str) -> Literal["en", "es"] | None: + """Heuristic language detection on Ana's reply text. + + We can't use the language specialist's verdict as the TTS hint because it + runs on the PATIENT's utterance — Ana may reply in a different language + (her prompt tells her to match the patient's register, which often means + Spanish even when the patient said a greeting in English). Cartesia needs + the language of the TEXT IT IS SPEAKING, which is Ana's reply. + """ + if any(c in text for c in "ñ¿¡"): + return "es" + lower = text.lower() + if any(c in lower for c in "áéíóú"): + return "es" + words = set(lower.split()) + if len(words & _SPANISH_FUNCTION_WORDS) >= 2: + return "es" + return "en" + + def _transcribe(wav_path: Path) -> str: - """Groq Whisper. Batch — fastrtc only calls the handler on VAD pause, so - the audio is already a complete utterance, typically 2-5 seconds.""" + """Groq Whisper — batch, but fastrtc only calls us once per VAD pause so + the clip is already short (typically 2-5 s).""" from openai import OpenAI api_key = os.environ.get("GROQ_API_KEY") @@ -60,11 +93,47 @@ def _transcribe(wav_path: Path) -> str: return str(resp).strip() -def _handler(audio: tuple[int, np.ndarray]) -> Iterator[tuple[int, np.ndarray]]: - """fastrtc invokes this once per turn, with the full utterance since last pause.""" - global _state - if _state is None: - _state = ConversationState(patient=_load_patient()) +def _format_transcript(state: ConversationState) -> str: + if not state.history: + return "_(start of call)_" + lines = [] + for turn in state.history[-20:]: + who = "**María:**" if turn.speaker == "patient" else "**Nurse Ana:**" + lines.append(f"{who} {turn.text}") + return "\n\n".join(lines) + + +def _build_verdict(result) -> str: + verdict = { + "action": result.action.kind, + "latency_ms": { + "primary": round(result.primary_latency_ms), + "specialists_parallel": round(result.specialist_latency_ms), + "total": round(result.total_latency_ms), + }, + "language": result.language.model_dump() if result.language else None, + "medication": result.medication.model_dump() if result.medication else None, + "labs": result.labs.model_dump() if result.labs else None, + "escalation": result.escalation.model_dump() if result.escalation else None, + } + return json.dumps(verdict, indent=2, default=str) + + +def _compute_turn(state: ConversationState | None, patient_text: str): + state = state or ConversationState(patient=_load_patient()) + result = run_turn(state, patient_text) + return result + + +def _voice_handler(audio, state_dict): + """fastrtc handler — runs on every VAD pause.""" + from fastrtc import AdditionalOutputs + + state = ( + ConversationState.model_validate(state_dict) + if state_dict + else ConversationState(patient=_load_patient()) + ) sample_rate, np_audio = audio wav_path = numpy_to_wav_tempfile(sample_rate, np_audio) @@ -78,8 +147,7 @@ def _handler(audio: tuple[int, np.ndarray]) -> Iterator[tuple[int, np.ndarray]]: return log.info("patient: %s", patient_text) - result = run_turn(_state, patient_text) - _state = result.state + result = _compute_turn(state, patient_text) log.info( "nurse [%s, %.0f ms]: %s", result.action.kind, @@ -87,35 +155,139 @@ def _handler(audio: tuple[int, np.ndarray]) -> Iterator[tuple[int, np.ndarray]]: result.nurse_text, ) - # Cartesia language hint: "mix" isn't a valid value, so drop to None - # and let Sonic-3 auto-handle code-switched text. - lang_tag = result.language.primary_lang if result.language else None - cartesia_lang = lang_tag if lang_tag in ("en", "es") else None + reply_lang = _detect_reply_language(result.nurse_text) - for pcm_chunk in stream_tts(result.nurse_text, language=cartesia_lang): + # Emit text updates first so the UI shows the reply while audio streams. + yield AdditionalOutputs( + result.nurse_text, + _build_verdict(result), + _format_transcript(result.state), + result.state.model_dump(), + ) + + for pcm_chunk in stream_tts(result.nurse_text, language=reply_lang): yield cartesia_pcm_to_numpy(pcm_chunk, TTS_SAMPLE_RATE) -def build_stream(): - from fastrtc import ReplyOnPause, Stream +def _text_handler(patient_text: str, state_dict): + """Text-only fallback path: no audio playback, just text updates.""" + if not patient_text or not patient_text.strip(): + return "", "{}", "_(start of call)_", state_dict - return Stream( - handler=ReplyOnPause(_handler), - modality="audio", - mode="send-receive", + state = ( + ConversationState.model_validate(state_dict) + if state_dict + else ConversationState(patient=_load_patient()) + ) + result = _compute_turn(state, patient_text.strip()) + return ( + result.nurse_text, + _build_verdict(result), + _format_transcript(result.state), + result.state.model_dump(), ) +def build_ui() -> gr.Blocks: + from fastrtc import ReplyOnPause, WebRTC + + with gr.Blocks(title="Constella — bilingual constellation voice agent") as demo: + gr.Markdown( + "# Constella\n" + "You are **María González** — 58-year-old diabetic patient, just out of hospital. " + "**Nurse Ana** will follow up. Speak or type. English, Spanish, or both mid-sentence.\n\n" + "> **Voice path:** click **Record** and speak; Ana answers the moment you pause. \n" + "> **Text path:** type below and press Enter." + ) + + state = gr.State(value=None) + + with gr.Row(): + with gr.Column(scale=1): + gr.Markdown("### You (María)") + webrtc = WebRTC(mode="send-receive", modality="audio", label="Speak") + text_in = gr.Textbox( + placeholder="or type here...", + label="Text", + lines=2, + ) + send_btn = gr.Button("Send text", variant="secondary", size="sm") + gr.Examples( + examples=[[ex] for ex in EXAMPLE_LINES], + inputs=[text_in], + label="Try these", + ) + + with gr.Column(scale=1): + gr.Markdown("### Nurse Ana") + nurse_text = gr.Textbox( + label="Latest reply", + lines=4, + interactive=False, + ) + transcript = gr.Markdown( + value="_(start of call)_", + label="Conversation transcript", + ) + + verdict_out = gr.Code( + label="What the 4 specialists saw (action + latency breakdown)", + language="json", + lines=18, + ) + + # Voice path — audio in, audio out, text/verdict/transcript as additional outputs. + webrtc.stream( + fn=ReplyOnPause(_voice_handler), + inputs=[webrtc, state], + outputs=[webrtc], + time_limit=60, + ) + webrtc.on_additional_outputs( + lambda nt, v, t, s: (nt, v, t, s), + outputs=[nurse_text, verdict_out, transcript, state], + queue=False, + show_progress="hidden", + ) + + # Text path (no audio). + text_in.submit( + _text_handler, + inputs=[text_in, state], + outputs=[nurse_text, verdict_out, transcript, state], + ) + send_btn.click( + _text_handler, + inputs=[text_in, state], + outputs=[nurse_text, verdict_out, transcript, state], + ) + + return demo + + def main() -> int: logging.basicConfig( level=logging.INFO, format="%(asctime)s %(levelname)s %(name)s %(message)s", ) + provider_override = os.environ.get("CONSTELLA_PROVIDER", "").lower() + if os.environ.get("GROQ_API_KEY") and provider_override != "openrouter": + log.warning( + "Using Groq for LLM inference. Free-tier rate limits (6000 TPM on 8B) " + "will cause multi-second backoff on realtime bursts — Constella fires 5 " + "concurrent calls per turn. If you see latency > 3 s per turn, either " + "set CONSTELLA_PROVIDER=openrouter in .env or upgrade to Groq Dev tier " + "at https://console.groq.com/settings/billing." + ) + if not os.environ.get("CARTESIA_API_KEY"): + log.error( + "CARTESIA_API_KEY is not set. The voice path will fail on the first " + "TTS call. Add it to .env and restart." + ) p = argparse.ArgumentParser() p.add_argument("--port", type=int, default=7860) args = p.parse_args() - stream = build_stream() - stream.ui.launch(server_port=args.port) + build_ui().launch(server_port=args.port) return 0 From db954297925455f79079bc9f35e0949302152505 Mon Sep 17 00:00:00 2001 From: Harshit Ghosh Date: Tue, 14 Apr 2026 19:35:14 -0700 Subject: [PATCH 05/10] fix(realtime): size WebRTC component so Record button is visible MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit WebRTC defaulted to full_screen=True (1280x720), so the wave visualization consumed the entire viewport and the Record button was pushed below the fold. Also the default button_labels are empty strings — icon-only. Setting full_screen=False + height=240 + explicit Record/Stop/waiting labels restores a usable control. --- constella/demo/realtime.py | 13 ++++++++++++- 1 file changed, 12 insertions(+), 1 deletion(-) diff --git a/constella/demo/realtime.py b/constella/demo/realtime.py index f7802e6..315e34c 100644 --- a/constella/demo/realtime.py +++ b/constella/demo/realtime.py @@ -205,7 +205,18 @@ def build_ui() -> gr.Blocks: with gr.Row(): with gr.Column(scale=1): gr.Markdown("### You (María)") - webrtc = WebRTC(mode="send-receive", modality="audio", label="Speak") + webrtc = WebRTC( + mode="send-receive", + modality="audio", + label="Speak", + full_screen=False, + height=240, + button_labels={ + "start": "Record", + "stop": "Stop", + "waiting": "Ana is replying…", + }, + ) text_in = gr.Textbox( placeholder="or type here...", label="Text", From 8f0611331ceaa0fe405da5353b41c9cd9ee01360 Mon Sep 17 00:00:00 2001 From: Harshit Ghosh Date: Tue, 14 Apr 2026 20:02:03 -0700 Subject: [PATCH 06/10] fix(realtime): accept fastrtc's placeholder arg in voice handler fastrtc's set_args in tracks.py prepends "__webrtc_value__" when the component value is passed as a string (always the case from Gradio). After audio replacement, the handler is called with (audio_tuple, webrtc_value, *real_inputs). The handler was signed for 2 args and blew up with 'takes 2 positional arguments but 3 were given' on the first VAD commit. Accepts the middle slot explicitly as _webrtc_value and discards it. --- constella/demo/realtime.py | 11 +++++++++-- 1 file changed, 9 insertions(+), 2 deletions(-) diff --git a/constella/demo/realtime.py b/constella/demo/realtime.py index 315e34c..424257a 100644 --- a/constella/demo/realtime.py +++ b/constella/demo/realtime.py @@ -125,8 +125,15 @@ def _compute_turn(state: ConversationState | None, patient_text: str): return result -def _voice_handler(audio, state_dict): - """fastrtc handler — runs on every VAD pause.""" +def _voice_handler(audio, _webrtc_value, state_dict): + """fastrtc handler — runs on every VAD pause. + + Signature note: fastrtc prepends a `"__webrtc_value__"` placeholder when + the WebRTC component's value is passed as a string (see + fastrtc/tracks.py:set_args). After the audio replacement, the args become + (audio_tuple, webrtc_value, *real_inputs) — so we accept the middle slot + explicitly and discard it. + """ from fastrtc import AdditionalOutputs state = ( From 7d8f6ad932c01241df3728be674daecc39db94f4 Mon Sep 17 00:00:00 2001 From: Harshit Ghosh Date: Tue, 14 Apr 2026 20:08:33 -0700 Subject: [PATCH 07/10] fix(tts): native voice per language, Spanish stops sounding American MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit The previous default voice (Tessa) is native English. Passing language='es' applied Spanish phonemes to her English speaker model, which produces American-accented Spanish — unusable for a bilingual healthcare agent. Fix: pick a native-Spanish voice (Ximena, Latina female, calm professional register) for es replies and keep Tessa for en. Voice identity does shift mid-conversation when Ana code-switches, but that matches how a bilingual nurse actually sounds. Env overrides: CARTESIA_VOICE_ID - single-voice override for both languages CARTESIA_VOICE_EN / ES - per-language overrides --- constella/realtime/tts.py | 43 +++++++++++++++++++++++++++++++-------- 1 file changed, 34 insertions(+), 9 deletions(-) diff --git a/constella/realtime/tts.py b/constella/realtime/tts.py index eb610e7..a999c6e 100644 --- a/constella/realtime/tts.py +++ b/constella/realtime/tts.py @@ -3,6 +3,13 @@ Yields raw `pcm_f32le` bytes as the model synthesizes. The realtime handler converts those chunks into fastrtc audio frames so Ana's voice starts playing within ~100 ms of the first token arriving from the primary agent. + +Voices are native per language — a native-Spanish voice is used for Spanish +replies and a native-English voice for English. Using a single multilingual +voice makes the non-native language sound phonetically approximated (e.g., +American accent on Spanish), which is unusable for a bilingual healthcare +agent. The tradeoff is that Ana's voice identity shifts slightly when she +code-switches — analogous to a bilingual nurse in real life. """ from __future__ import annotations @@ -13,14 +20,34 @@ log = logging.getLogger(__name__) -# Default voice — Cartesia's multilingual example voice, handles en + es and -# reasonable code-switching. Override by setting CARTESIA_VOICE_ID to any -# voice UUID from https://play.cartesia.ai/voices. -_DEFAULT_VOICE_ID = "6ccbfb76-1fc6-48f7-b71d-91ac6298247b" +# Native-English female, warm conversational — fits a U.S. nurse making a +# follow-up call. Override via CARTESIA_VOICE_EN. +_DEFAULT_VOICE_EN = "6ccbfb76-1fc6-48f7-b71d-91ac6298247b" # Tessa - Kind Companion + +# Native-Latin-American-Spanish female, calm and professional — fits a nurse +# talking to a U.S. Latino patient. Override via CARTESIA_VOICE_ES. +_DEFAULT_VOICE_ES = "3597a26f-80ef-4bd5-8101-9699bc764917" # Ximena - Calm Navigator + _MODEL_ID = "sonic-3" SAMPLE_RATE = 44100 +def _pick_voice(language: Literal["en", "es"] | None) -> str: + """Pick a native voice for the given reply language. + + Precedence: + 1. CARTESIA_VOICE_ID — single-voice override (legacy behavior) + 2. CARTESIA_VOICE_ES / CARTESIA_VOICE_EN — per-language overrides + 3. Hard-coded defaults tuned for the healthcare-nurse register + """ + single = os.environ.get("CARTESIA_VOICE_ID") + if single: + return single + if language == "es": + return os.environ.get("CARTESIA_VOICE_ES", _DEFAULT_VOICE_ES) + return os.environ.get("CARTESIA_VOICE_EN", _DEFAULT_VOICE_EN) + + def stream_tts( text: str, *, @@ -28,10 +55,8 @@ def stream_tts( ) -> Iterator[bytes]: """Yield `pcm_f32le` audio chunks as Cartesia synthesizes `text`. - `language`: - - "en" or "es": hard-hints Cartesia to that language (cleaner prosody - when the language specialist is confident) - - None: let Sonic-3 auto-detect (right choice for code-switched output) + `language`: "en" or "es" selects a native voice AND passes the language + hint to Sonic-3. `None` falls back to the English voice (safest default). """ from cartesia import Cartesia # lazy import so pytest doesn't pull websockets @@ -41,7 +66,7 @@ def stream_tts( "CARTESIA_API_KEY is required for the realtime demo. Add it to .env." ) - voice_id = os.environ.get("CARTESIA_VOICE_ID", _DEFAULT_VOICE_ID) + voice_id = _pick_voice(language) client = Cartesia(api_key=api_key) with client.tts.websocket_connect() as conn: From 2d20cccf6cdd579d33021da53fc83ea5adc2e888 Mon Sep 17 00:00:00 2001 From: Harshit Ghosh Date: Tue, 14 Apr 2026 20:19:40 -0700 Subject: [PATCH 08/10] fix(primary): detect current-utterance language and inject directive MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Ana was replying in Spanish even when the patient spoke English because: - Maria's profile says primary_language=es (strong bias in prompt) - Ana runs BEFORE the language specialist, so she had no signal about the current utterance's language - History from prior Spanish turns created conversational momentum Adds detect_utterance_language() — same heuristic as the TTS language detector (Spanish markers + function-word density, returning en/es/mix). build_user_prompt() now injects a mandatory LANGUAGE DIRECTIVE so Ana matches the current turn regardless of history or profile. The profile field is demoted from 'Primary language' to 'Preferred language at home (but ignore if they just spoke a different language)'. --- constella/primary.py | 63 ++++++++++++++++++++++++++++++++++++++++++-- 1 file changed, 61 insertions(+), 2 deletions(-) diff --git a/constella/primary.py b/constella/primary.py index c635402..f1ccaf4 100644 --- a/constella/primary.py +++ b/constella/primary.py @@ -10,9 +10,63 @@ """ from __future__ import annotations +from typing import Literal + from constella.llm import PRIMARY_MODEL, chat from constella.schemas import ConversationState, Turn +_SPANISH_MARKERS = frozenset("ñ¿¡áéíóú") +_SPANISH_FUNCTION_WORDS = frozenset({ + "hola", "está", "estás", "estas", "esta", "sí", "bueno", "buenos", + "buenas", "dias", "días", "tardes", "noches", "pero", "porque", + "como", "cómo", "qué", "que", "dónde", "donde", "entonces", "muy", + "mucho", "mucha", "también", "tambien", "señora", "señor", "usted", + "soy", "eres", "tomó", "gracias", "bien", "tiene", "tengo", "tienes", + "desde", "hospital", "medicamento", "azúcar", "insulina", "doctor", + "doctora", "ahora", "poco", "ayer", "mañana", "preocupes", "quiero", + "puedes", "ese", "con", "por", "para", "del", "las", "los", "nada", + "algo", "todo", "toda", "hace", "hacer", "estoy", "siento", "duele", +}) +_ENGLISH_FUNCTION_WORDS = frozenset({ + "the", "and", "is", "are", "was", "were", "i", "my", "you", "your", + "this", "that", "have", "has", "had", "with", "for", "but", "from", + "of", "in", "on", "to", "it", "a", "an", "not", "don't", "didn't", +}) + + +def detect_utterance_language(text: str) -> Literal["en", "es", "mix"]: + """Heuristic language detector for a patient utterance. + + Purpose: give the primary agent an unambiguous signal about what language + the patient JUST spoke, so Ana replies in the same language instead of + defaulting to the patient's profile language. Used before the language + specialist has a chance to weigh in (specialists run after primary). + + Returns "mix" when both languages are present in detectable density. + """ + lower = text.lower() + has_spanish_markers = any(c in lower for c in _SPANISH_MARKERS) + words = set(lower.split()) + spanish_word_hits = len(words & _SPANISH_FUNCTION_WORDS) + english_word_hits = len(words & _ENGLISH_FUNCTION_WORDS) + looks_spanish = has_spanish_markers or spanish_word_hits >= 2 + looks_english = english_word_hits >= 2 + if looks_spanish and looks_english: + return "mix" + if looks_spanish: + return "es" + return "en" + + +_LANGUAGE_DIRECTIVE = { + "en": "The patient just spoke in English. Reply in English.", + "es": "La paciente acaba de hablar en español. Responde en español.", + "mix": ( + "The patient code-switched between English and Spanish. Match their " + "register and feel free to code-switch naturally." + ), +} + PRIMARY_SYSTEM_PROMPT = """You are Nurse Ana, a warm, experienced registered nurse making a post-discharge follow-up call. Your job on each turn: @@ -40,11 +94,14 @@ def build_user_prompt(state: ConversationState, last_patient_text: str) -> str: for m in state.patient.medications ) + detected_lang = detect_utterance_language(last_patient_text) + directive = _LANGUAGE_DIRECTIVE[detected_lang] + return f"""Patient profile: - Name: {state.patient.name} - Age: {state.patient.age} - Pronouns: {state.patient.pronouns} -- Primary language: {state.patient.primary_language} +- Preferred language at home: {state.patient.primary_language} (but ignore this if they just spoke a different language — see directive below) - Conditions: {", ".join(state.patient.conditions)} - Discharge summary: {state.patient.discharge_summary} - Medications: @@ -56,7 +113,9 @@ def build_user_prompt(state: ConversationState, last_patient_text: str) -> str: Patient just said: "{last_patient_text}" -Respond as Nurse Ana. One to three short sentences. Match the patient's language.""" +LANGUAGE DIRECTIVE (mandatory): {directive} + +Respond as Nurse Ana. One to three short sentences.""" def primary_respond(state: ConversationState, last_patient_text: str) -> str: From 84aa69008729b41667de476014aaa5235f203c10 Mon Sep 17 00:00:00 2001 From: Harshit Ghosh Date: Tue, 14 Apr 2026 20:26:24 -0700 Subject: [PATCH 09/10] fix(realtime): drop gr.State from AdditionalOutputs so UI updates every turn MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit The UI was stuck on turn 1's reply because passing gr.State through AdditionalOutputs created a re-entrant loop: each state update triggered state_change, which re-entered the on_additional_outputs async handler and silently dropped subsequent yields. Fix: hold the ConversationState in a module-level _state (we already did this in an earlier iteration — the rich UI pulled it out into gr.State, which turned out to be the bug). AdditionalOutputs now carries only the three display strings (nurse_text, verdict, transcript), not state. One state per process is fine for a single-user dev demo; prod would key by fastrtc session_id. Handler signatures simplify correspondingly: _voice_handler(audio, _webrtc_value) # was 3 args _text_handler(patient_text) # was 2 args --- constella/demo/realtime.py | 62 ++++++++++++++++---------------------- 1 file changed, 26 insertions(+), 36 deletions(-) diff --git a/constella/demo/realtime.py b/constella/demo/realtime.py index 424257a..259d80d 100644 --- a/constella/demo/realtime.py +++ b/constella/demo/realtime.py @@ -119,29 +119,26 @@ def _build_verdict(result) -> str: return json.dumps(verdict, indent=2, default=str) -def _compute_turn(state: ConversationState | None, patient_text: str): - state = state or ConversationState(patient=_load_patient()) - result = run_turn(state, patient_text) - return result +def _ensure_state() -> ConversationState: + global _state + if _state is None: + _state = ConversationState(patient=_load_patient()) + return _state -def _voice_handler(audio, _webrtc_value, state_dict): +def _voice_handler(audio, _webrtc_value): """fastrtc handler — runs on every VAD pause. Signature note: fastrtc prepends a `"__webrtc_value__"` placeholder when the WebRTC component's value is passed as a string (see fastrtc/tracks.py:set_args). After the audio replacement, the args become - (audio_tuple, webrtc_value, *real_inputs) — so we accept the middle slot - explicitly and discard it. + (audio_tuple, webrtc_value). No other inputs are wired to the stream, so + the handler takes exactly two positional args. """ from fastrtc import AdditionalOutputs + global _state - state = ( - ConversationState.model_validate(state_dict) - if state_dict - else ConversationState(patient=_load_patient()) - ) - + state = _ensure_state() sample_rate, np_audio = audio wav_path = numpy_to_wav_tempfile(sample_rate, np_audio) try: @@ -154,7 +151,8 @@ def _voice_handler(audio, _webrtc_value, state_dict): return log.info("patient: %s", patient_text) - result = _compute_turn(state, patient_text) + result = run_turn(state, patient_text) + _state = result.state log.info( "nurse [%s, %.0f ms]: %s", result.action.kind, @@ -164,34 +162,30 @@ def _voice_handler(audio, _webrtc_value, state_dict): reply_lang = _detect_reply_language(result.nurse_text) - # Emit text updates first so the UI shows the reply while audio streams. yield AdditionalOutputs( result.nurse_text, _build_verdict(result), _format_transcript(result.state), - result.state.model_dump(), ) for pcm_chunk in stream_tts(result.nurse_text, language=reply_lang): yield cartesia_pcm_to_numpy(pcm_chunk, TTS_SAMPLE_RATE) -def _text_handler(patient_text: str, state_dict): +def _text_handler(patient_text: str): """Text-only fallback path: no audio playback, just text updates.""" + global _state if not patient_text or not patient_text.strip(): - return "", "{}", "_(start of call)_", state_dict + state = _ensure_state() + return "", "{}", _format_transcript(state) - state = ( - ConversationState.model_validate(state_dict) - if state_dict - else ConversationState(patient=_load_patient()) - ) - result = _compute_turn(state, patient_text.strip()) + state = _ensure_state() + result = run_turn(state, patient_text.strip()) + _state = result.state return ( result.nurse_text, _build_verdict(result), _format_transcript(result.state), - result.state.model_dump(), ) @@ -207,8 +201,6 @@ def build_ui() -> gr.Blocks: "> **Text path:** type below and press Enter." ) - state = gr.State(value=None) - with gr.Row(): with gr.Column(scale=1): gr.Markdown("### You (María)") @@ -257,27 +249,25 @@ def build_ui() -> gr.Blocks: # Voice path — audio in, audio out, text/verdict/transcript as additional outputs. webrtc.stream( fn=ReplyOnPause(_voice_handler), - inputs=[webrtc, state], + inputs=[webrtc], outputs=[webrtc], time_limit=60, ) webrtc.on_additional_outputs( - lambda nt, v, t, s: (nt, v, t, s), - outputs=[nurse_text, verdict_out, transcript, state], - queue=False, - show_progress="hidden", + lambda nt, v, t: (nt, v, t), + outputs=[nurse_text, verdict_out, transcript], ) # Text path (no audio). text_in.submit( _text_handler, - inputs=[text_in, state], - outputs=[nurse_text, verdict_out, transcript, state], + inputs=[text_in], + outputs=[nurse_text, verdict_out, transcript], ) send_btn.click( _text_handler, - inputs=[text_in, state], - outputs=[nurse_text, verdict_out, transcript, state], + inputs=[text_in], + outputs=[nurse_text, verdict_out, transcript], ) return demo From fb3ee670e1aa2ec2aaa8c8b9147d08945a3cab98 Mon Sep 17 00:00:00 2001 From: Harshit Ghosh Date: Tue, 14 Apr 2026 20:28:43 -0700 Subject: [PATCH 10/10] fix(realtime): restore module-level _state binding MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit A previous edit lost the '_state: ConversationState | None = None' line at module scope, so the first call into _ensure_state() raised 'NameError: name _state is not defined'. The UI build path never invokes the state machinery, so the smoke test missed it — it only surfaced when a mic turn actually fired. Also added a test helper in the smoke suite: importing the module and invoking _ensure_state() directly so future edits can't regress this silently. --- constella/demo/realtime.py | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/constella/demo/realtime.py b/constella/demo/realtime.py index 259d80d..5b2c339 100644 --- a/constella/demo/realtime.py +++ b/constella/demo/realtime.py @@ -119,6 +119,14 @@ def _build_verdict(result) -> str: return json.dumps(verdict, indent=2, default=str) +# Module-level conversation state — single-user demo. Keeping it here (rather +# than in gr.State) is deliberate: threading state through AdditionalOutputs +# triggers a state_change event on every turn, which re-enters the +# on_additional_outputs handler and silently drops subsequent UI updates. +# For multi-user prod we'd key this by fastrtc session_id. +_state: ConversationState | None = None + + def _ensure_state() -> ConversationState: global _state if _state is None: