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Python Examples

Examples using Python to interact with the Scrub AI API.

The easiest way to use Scrub AI with Python is the official OpenAI SDK:

pip install openai

Basic Request​

from openai import OpenAI
import os

client = OpenAI(
    api_key=os.environ.get("SCRUB_API_KEY"),
    base_url="https://api.scrub.health/v1"
)

response = client.chat.completions.create(
    model="gpt-5.2",
    messages=[
        {"role": "user", "content": "What are common symptoms of the flu?"}
    ]
)

print(response.choices[0].message.content)

With System Message​

response = client.chat.completions.create(
    model="gpt-5.2",
    messages=[
        {"role": "system", "content": "You are a helpful healthcare assistant."},
        {"role": "user", "content": "What are the risk factors for heart disease?"}
    ],
    temperature=0.7,
    max_tokens=500
)

print(response.choices[0].message.content)

Streaming Response​

stream = client.chat.completions.create(
    model="gpt-5.2",
    messages=[
        {"role": "user", "content": "Explain the importance of regular exercise."}
    ],
    stream=True
)

for chunk in stream:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="", flush=True)

Using Different Providers​

# Using Claude
response = client.chat.completions.create(
    model="claude-sonnet-4-5",
    messages=[{"role": "user", "content": "Hello!"}],
    max_tokens=500
)

# Using Gemini
response = client.chat.completions.create(
    model="gemini-3-flash-preview",
    messages=[{"role": "user", "content": "Hello!"}]
)

Using Requests Library​

If you prefer not to use the OpenAI SDK:

import requests
import os

def chat_completion(messages, model="gpt-5.2"):
    response = requests.post(
        "https://api.scrub.health/v1/chat/completions",
        headers={
            "Authorization": f"Bearer {os.environ.get('SCRUB_API_KEY')}",
            "Content-Type": "application/json"
        },
        json={
            "model": model,
            "messages": messages
        }
    )
    response.raise_for_status()
    return response.json()

# Usage
result = chat_completion([
    {"role": "user", "content": "What is preventive care?"}
])

print(result["choices"][0]["message"]["content"])

Error Handling​

from openai import OpenAI, APIError, APIConnectionError, RateLimitError

client = OpenAI(
    api_key=os.environ.get("SCRUB_API_KEY"),
    base_url="https://api.scrub.health/v1"
)

try:
    response = client.chat.completions.create(
        model="gpt-5.2",
        messages=[{"role": "user", "content": "Hello"}]
    )
    print(response.choices[0].message.content)

except RateLimitError:
    print("Rate limit exceeded. Please wait and retry.")

except APIConnectionError:
    print("Failed to connect to Scrub AI API.")

except APIError as e:
    print(f"API error: {e.message}")
    # Check for PHI blocked error
    if "phi_blocked" in str(e):
        print("Request was blocked due to PHI detection.")

Async Usage​

import asyncio
from openai import AsyncOpenAI
import os

client = AsyncOpenAI(
    api_key=os.environ.get("SCRUB_API_KEY"),
    base_url="https://api.scrub.health/v1"
)

async def main():
    response = await client.chat.completions.create(
        model="gpt-5.2",
        messages=[{"role": "user", "content": "Hello!"}]
    )
    print(response.choices[0].message.content)

asyncio.run(main())

Complete Example​

"""
Scrub AI API Python Example
Healthcare chatbot with PHI protection
"""

from openai import OpenAI
import os

def create_client():
    return OpenAI(
        api_key=os.environ.get("SCRUB_API_KEY"),
        base_url="https://api.scrub.health/v1"
    )

def chat(client, user_message, conversation_history=None):
    if conversation_history is None:
        conversation_history = []

    messages = [
        {"role": "system", "content": "You are a helpful healthcare assistant."}
    ] + conversation_history + [
        {"role": "user", "content": user_message}
    ]

    response = client.chat.completions.create(
        model="gpt-5.2",
        messages=messages,
        temperature=0.7
    )

    assistant_message = response.choices[0].message.content

    # Update conversation history
    conversation_history.append({"role": "user", "content": user_message})
    conversation_history.append({"role": "assistant", "content": assistant_message})

    return assistant_message, conversation_history

if __name__ == "__main__":
    client = create_client()
    history = []

    print("Healthcare Assistant (type 'quit' to exit)")
    print("-" * 40)

    while True:
        user_input = input("\nYou: ").strip()
        if user_input.lower() == 'quit':
            break

        response, history = chat(client, user_input, history)
        print(f"\nAssistant: {response}")