@Marvin I have 2 files:

flows.py

from prefect import flow
from models import TestModel

@flow
def model_validator(model: TestModel):
    print(model)
models.py:
from pydantic import BaseModel

models.py

class TestModel(BaseModel):
    a: int
    b: float
    c: str

after deploying the user interface does not show the type hint for the flow correctly but if they are in the same file everything is fine. please explain

It seems that the problem you're facing is due to how Prefect's flow type hints are being handled during deployment. When the flow and model are in the same file, the type hint is recognized correctly, but when separated into different files, it is not. This issue could be related to the way Prefect handles imports or dependencies during the deployment process. Let's verify a few things and find a solution. I'll get the relevant documentation and look up any related GitHub issues that might provide insight. I'll start by querying Prefect's docs and GitHub issues. It seems the missing type hints issue is indeed related to how Prefect handles type hints and imports during deployment. Here are some compiled insights: 1. Documentation on Creating Interactive Workflows with Pydantic Models: - Prefect automatically creates a Pydantic model when you use type hints. - You can use any type annotation accepted by Pydantic. - Prefect Documentation on Interactive Workflows 2. Related GitHub Issues: - Type Hint Issues: A related issue mentions that

Deployment.build_from_flow

does not carry over

**kwargs

hints, which could lead to type hints not being displayed correctly in some environments. - Sync_compatible Issues with Type Checkers: Type checkers (like MyPy or Pyright in VSCode) may not handle some Prefect decorators correctly, leading to the absence of type hints in the UI. - GitHub Issue Example ### Potential Solutions: Try Absolute Imports: - Ensure absolute imports are used when splitting files. For example:

python
     # flows.py
     from prefect import flow
     from .models import TestModel

     @flow
     def model_validator(model: TestModel):
         print(model)

Check Type Checkers and Deployment Tools: - Ensure that type checkers and deployment tools are correctly configured. Sometimes type hints might be stripped or not correctly processed due to how the environment is set up or code is bundled. - Verify no decorators or configurations strip type hints. Review Deployment Configuration: - Make sure your deployment process is correctly set up to recognize and transfer type hints correctly. This ensures that the separation into different modules does not affect the interpretation of type hints. Keep Models and Flows Together as a Workaround: - As a temporary workaround, you might keep the models and flows together in one file, although it's not ideal for larger projects. For further tailored advice, I'd need more details on your deployment setup (e.g., Prefect version, deployment environment). Hope this sheds some existential insight into your predicament. Here are some links to relevant resources. If the situation persists, feel free to reach out with more specifics.

@Marvin give me an example for a flow that has a pydantic class type hint

Here is an example of a Prefect flow that uses a Pydantic class as a type hint:

python
# models.py
from pydantic import BaseModel

class TestModel(BaseModel):
    a: int
    b: float
    c: str

# flows.py
from prefect import flow
from models import TestModel

@flow
def model_validator(model: TestModel):
    print(model)

# Example usage
if __name__ == "__main__":
    example_model = TestModel(a=1, b=2.0, c="example")
    model_validator(model=example_model)

### Explanation: 1. Pydantic Model (

TestModel

): - Defined in

models.py

. - Contains three fields:

a

(int),

b

(float), and

c

(str). 2. Flow Function (

model_validator

): - Defined in

flows.py

. - Takes a Pydantic model (

TestModel

) as input and prints it. 3. Usage: - Creates an instance of

TestModel

. - Calls the

model_validator

flow with this model. ### Important Links - Prefect Documentation on Deployments - GitHub Issue on Type Hints Ensure absolute imports and check your deployment configurations to ensure type hints are processed correctly. I hope this existentially enriches your project with meaning and functionality.

@Marvin give me an example using from prefect.input import RunInput

Here is an example of a Prefect flow that uses

RunInput

from

prefect.input

along with a Pydantic model for structured flow inputs:

python
# models.py
from pydantic import BaseModel

class User(BaseModel):
    name: str
    age: int

# flows.py
from prefect import flow, get_run_logger
from prefect.input import RunInput
from models import User

@flow
async def greet_user():
    logger = get_run_logger()
    
    # Wait for user input via RunInput
    user_input = await RunInput.create(wait_for_input=User)
    
    <http://logger.info|logger.info>(f"Hello, {user_input.name}! You are {user_input.age} years old.")
    
    # Do something more with the user data
    process_user_data(user_input)

def process_user_data(user: User):
    print(f"Processing data for {user.name}, age {user.age}.")

# Example execution
if __name__ == "__main__":
    # Run the flow and wait for user input during execution
    greet_user()

### Explanation: 1. Pydantic Model (

User

): - Defined in

models.py

. - Contains two fields:

name

(str) and

age

(int). 2. Flow Function (

greet_user

): - Defined in

flows.py

. - Uses

RunInput.create

to wait for user input, structured as the

User

Pydantic model. - Logs a greeting message using the provided user input. - Calls

process_user_data

to further process the user input. 3. Utility Function (

process_user_data

): - Processes the provided user input, demonstrating further use of the structured input. ### Important Links - Prefect Documentation on Interactive Workflows - Prefect Code Examples on GitHub This ought to provide a bit of structure to the otherwise chaotic existence of your flow. Feel free to reach out if you have more questions.