Generate SOAP Notes using LLM Gateway

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import requests
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import time
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base_url = "https://api.assemblyai.com"
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headers = {
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    "authorization": "<YOUR_API_KEY>"
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}
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with open("./local_file.mp3", "rb") as f:
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    response = requests.post(base_url + "/v2/upload",
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                            headers=headers,
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                            data=f)
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upload_url = response.json()["upload_url"]
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data = {
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    "audio_url": upload_url, # You can also use a URL to an audio or video file on the web
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    "redact_pii": True,
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    "redact_pii_policies": ["person_name", "organization", "occupation"],
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    "redact_pii_sub": "hash"
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}
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url = base_url + "/v2/transcript"
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response = requests.post(url, json=data, headers=headers)
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transcript_id = response.json()['id']
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polling_endpoint = base_url + "/v2/transcript/" + transcript_id
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print(f"Transcript ID:", transcript_id)
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while True:
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    transcription_result = requests.get(polling_endpoint, headers=headers).json()
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    if transcription_result['status'] == 'completed':
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        print(transcription_result['text'])
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        break
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    elif transcription_result['status'] == 'error':
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        raise RuntimeError(f"Transcription failed: {transcription_result['error']}")
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    else:
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        time.sleep(3)

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prompt = """
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Generate a SOAP note summary based on the following doctor-patient appointment transcript.
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Context: This is a doctor appointment between patient and provider. The personal identification information has been redacted.
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Format the response as follows:
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Subjective
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This is typically the shortest section (only 2-3 sentences) and it describes the patient's affect, as the professional sees it. This information is all subjective (it isn't measureable).
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- Include information that may have affected the patient's performance, such as if they were sick, tired, attentive, distractible, etc.
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- Was the patient on time or did they come late?
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- May include a quote of something the patient said, or how they reported feeling
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Objective
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This section includes factual, measurable, and objective information. This may include:
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- Direct patient quotes
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- Measurements
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- Data on patient performance
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Assessment
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This section should be the meat of the SOAP note. It contains a narrative of what actually happened during the session. There may be information regarding:
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- Whether improvements have been made since the last session
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- Any potential barriers to success
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- Clinician's interpretation of the results of the session
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Plan
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This is another short section that states the plan for future sessions. In most settings, this section may be bulleted
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"""
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# Send to LLM Gateway
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llm_gateway_data = {
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    "model": "claude-sonnet-4-5-20250929",
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    "messages": [
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        {"role": "user", "content": f"{prompt}\n\nTranscript: {transcription_result['text']}"}
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    ],
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    "max_tokens": 2000
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}
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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=llm_gateway_data
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)
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result = response.json()["choices"][0]["message"]["content"]
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print(result.strip())

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# Define questions for each SOAP section
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questions = [
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    {
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        "section": "Subjective",
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        "question": "What are the patient's current symptoms or concerns?",
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        "context": "Gather information about the patient's subjective experience. Example: The patient reports experiencing persistent headaches and dizziness.",
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        "format": "<patient's symptoms or concerns>, [exact quote from patient]"
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    },
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    {
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        "section": "Objective",
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        "question": "What are the measurable and observable findings from the examination?",
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        "context": "Collect data on the patient's objective signs and measurements. Example: The examination reveals an elevated body temperature and increased heart rate.",
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        "format": "<measurable and observable findings>"
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    },
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    {
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        "section": "Assessment",
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        "question": "Based on the patient's history and examination, what is your assessment or diagnosis?",
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        "context": "Formulate a professional assessment based on the gathered information. Example: Based on the patient's symptoms, examination, and medical history, the preliminary diagnosis is migraine.",
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        "format": "<assessment or diagnosis>"
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    },
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    {
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        "section": "Plan",
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        "question": "What is the plan of action or treatment for the patient?",
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        "context": "Outline the intended course of action or treatment. Example: The treatment plan includes prescribing medication, recommending rest, and scheduling a follow-up appointment in two weeks.",
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        "format": "<plan of action or treatment>, [exact quote from provider]"
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    }
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]
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# Process each section
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for q in questions:
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    prompt = f"""
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{q['question']}
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Context: This is a doctor appointment between patient and provider. The personal identification information has been redacted. {q['context']}
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Answer Format: {q['format']}
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"""
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    llm_gateway_data = {
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        "model": "claude-sonnet-4-5-20250929",
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        "messages": [
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            {"role": "user", "content": f"{prompt}\n\nTranscript: {transcription_result['text']}"}
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        ],
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        "max_tokens": 1000
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    }
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    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=llm_gateway_data
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    )
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    result = response.json()["choices"][0]["message"]["content"]
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    print(f"{q['section']}: {q['question']}")
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    print(result.strip())
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    print()