Quickstart

from tensorlake.functions_sdk import Graph, tensorlake_function

@tensorlake_function(input_encoder="json")
def say_hello(name: str) -> str:
    return f"Hello, {name}!"

@tensorlake_function(output_encoder="json")
def say_goodbye(greeting: str) -> str:
    name = greeting.split(" ")[1]
    return f"Goodbye, {name}!"

graph = Graph(name="greeter", start_node=say_hello)
graph.add_edge(say_hello, say_goodbye)

if __name__ == "__main__":
    invocation_id = graph.run(name="John")
    output = graph.get_output(invocation_id, "say_goodbye")
    print(output)

import os
import base64

import requests
from pydantic import BaseModel
from tensorlake.functions_sdk import Graph, tensorlake_function, Image, RequestException

image = Image().run("pip install openai pydantic requests")

class DrivingLicense(BaseModel):
    name: str
    date_of_birth: str
    address: str
    license_number: str
    license_expiration_date: str

@tensorlake_function(
    input_encoder="json", image=image, output_encoder="json", secrets=["OPENAI_API_KEY"]
)
def extract_driving_license_data(url: str) -> DrivingLicense:
    from openai import OpenAI

    # Download image from URL
    response = requests.get(url)
    try:
        response.raise_for_status()
    except Exception as e:
        raise RequestException(f"Failed to download image from URL: {url}: {e}")

    # Encode image as base64
    image_base64 = base64.b64encode(response.content).decode("utf-8")

    # Determine image format from content type or URL
    content_type = response.headers.get("content-type", "")
    if "jpeg" in content_type or "jpg" in content_type:
        image_format = "jpeg"
    elif "png" in content_type:
        image_format = "png"
    else:
        # Default to jpeg if can't determine
        image_format = "jpeg"

    openai = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))

    response = openai.beta.chat.completions.parse(
        model="gpt-4o-mini",
        messages=[
            {
                "role": "system",
                "content": "Extract the personal information from the driving license image.",
            },
            {
                "role": "user",
                "content": [
                    {
                        "type": "image_url",
                        "image_url": {
                            "url": f"data:image/{image_format};base64,{image_base64}"
                        },
                    }
                ],
            },
        ],
        response_format=DrivingLicense,
    )
    dl = response.choices[0].message.parsed
    return dl.model_dump()


graph = Graph(name="driving_license_extractor", start_node=extract_driving_license_data)