Medical scribe

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import requests
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import time
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# ── Config ────────────────────────────────────────────────────
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base_url = "https://api.assemblyai.com"
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headers = {"authorization": "YOUR_API_KEY"}
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audio_url = "https://assembly.ai/wildfires.mp3"  # Replace with your clinical audio
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# ── Step 1: Transcribe with Medical Mode + speaker labels + entity detection ──
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data = {
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    "audio_url": audio_url,
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    "speech_models": ["universal-3-pro"],
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    "domain": "medical-v1",
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    "speaker_labels": True,
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    "entity_detection": True,
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}
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response = requests.post(base_url + "/v2/transcript", headers=headers, json=data)
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response.raise_for_status()
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transcript_id = response.json()["id"]
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while True:
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    result = requests.get(f"{base_url}/v2/transcript/{transcript_id}", headers=headers).json()
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    if result["status"] == "completed":
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        break
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    elif result["status"] == "error":
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        raise RuntimeError(f"Transcription failed: {result['error']}")
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    time.sleep(3)
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# ── Step 2: Identify speakers by role ──
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understanding_response = requests.post(
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    "https://llm-gateway.assemblyai.com/v1/understanding",
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    headers=headers,
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    json={
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        "transcript_id": transcript_id,
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        "speech_understanding": {
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            "request": {
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                "speaker_identification": {
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                    "speaker_type": "role",
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                    "known_values": ["Provider", "Patient"],
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                }
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            }
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        },
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    },
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)
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understanding_response.raise_for_status()
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identified = understanding_response.json()
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# ── Step 3: Extract detected entities ──
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entities = result.get("entities", [])
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entity_summary = "\n".join(
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    f"- {e['entity_type']}: {e['text']}" for e in entities
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)
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# ── Step 4: Format identified transcript ──
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speaker_transcript = "\n".join(
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    f"{u['speaker']}: {u['text']}" for u in identified["utterances"]
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)
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# ── Step 5: Generate SOAP note via LLM Gateway ──
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llm_response = requests.post(
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    "https://llm-gateway.assemblyai.com/v1/chat/completions",
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    headers=headers,
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    json={
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        "model": "claude-sonnet-4-5-20250929",
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        "messages": [
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            {
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                "role": "user",
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                "content": (
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                    "You are a medical scribe. Given the clinical encounter transcript and "
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                    "detected entities below, generate a structured SOAP note.\n\n"
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                    "Format the note with these sections:\n"
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                    "- **Subjective**: Patient's reported symptoms and history\n"
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                    "- **Objective**: Clinical observations and measurements\n"
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                    "- **Assessment**: Diagnosis or clinical impression\n"
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                    "- **Plan**: Treatment plan, prescriptions, and follow-up\n\n"
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                    f"Detected entities:\n{entity_summary}\n\n"
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                    f"Transcript:\n{speaker_transcript}"
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                ),
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            }
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        ],
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        "max_tokens": 2000,
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    },
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)
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llm_response.raise_for_status()
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print("=== SOAP Note ===\n")
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print(llm_response.json()["choices"][0]["message"]["content"])