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Turn a Python script into an app by separating its core computation from input/output code, defining the inputs and outputs as JSON Schema, validating both boundaries, and then attaching the right adapter. Use Streamlit for the fastest browser UI, Floom for a versioned worker exposed through UI, REST and MCP, or an HTTP framework documented with OpenAPI for an API integration.
- Move business logic into a function that accepts explicit values and returns a dictionary.
- Describe those dictionaries with JSON Schema.
- Reject invalid requests before running the function and reject malformed results before sending them back.
- Add a UI, worker manifest or HTTP endpoint without changing the core function.
- Pin dependencies, keep secrets outside source code, version the schema and retain useful run logs.
1. Start with a pure, callable function
A script is difficult to expose when it reads from input(), depends on module-level state and prints its only result. First make the useful operation a normal function. It should receive ordinary Python values and return a structured object.
# core.py
def run_job(name: str, count: int) -> dict:
cleaned = name.strip()
if not cleaned:
raise ValueError('name cannot be empty')
if count < 1:
raise ValueError('count must be at least 1')
return {
'message': f'Hello, {cleaned}',
'total': count,
}
if __name__ == '__main__':
print(run_job('Ada', 3))
The function can still be called from a command-line script, but it is now reusable by a web UI, a worker runtime, tests or an API server. Keep formatting, HTTP responses and widget code outside this module.
2. Write the JSON Schema contract
JSON Schema is a declarative language for defining the structure and constraints of JSON data. A schema is a machine-readable contract: it tells clients what fields are accepted and gives a validator rules for checking an instance.
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Input and output schemas
# schema.py
from jsonschema import Draft202012Validator
INPUT_SCHEMA = {
'type': 'object',
'required': ['name', 'count'],
'properties': {
'name': {'type': 'string', 'minLength': 1},
'count': {'type': 'integer', 'minimum': 1},
},
'additionalProperties': False,
}
OUTPUT_SCHEMA = {
'type': 'object',
'required': ['message', 'total'],
'properties': {
'message': {'type': 'string'},
'total': {'type': 'integer', 'minimum': 1},
},
'additionalProperties': False,
}
def _validate(instance, schema):
validator = Draft202012Validator(schema)
errors = sorted(validator.iter_errors(instance), key=lambda error: list(error.path))
if errors:
details = '; '.join(error.message for error in errors)
raise ValueError(details)
def validate_input(data: dict) -> dict:
_validate(data, INPUT_SCHEMA)
return data
def validate_output(data: dict) -> dict:
_validate(data, OUTPUT_SCHEMA)
return data
required prevents missing fields, type catches values such as a string where an integer is expected, and minimum and minLength express simple range rules. Decide deliberately whether to allow unknown keys. Rejecting them catches typos; allowing them can make additive client changes easier.
3. Validate at both boundaries
Validation belongs at the adapter boundary, not only inside the core function. Validate the decoded request before invoking run_job, then validate its return value before displaying or serializing it. The core function should still protect invariants needed for safe internal use, because not every caller will pass through the same adapter.
from core import run_job
from schema import validate_input, validate_output
def execute(raw: dict) -> dict:
values = validate_input(raw)
result = run_job(values['name'], values['count'])
return validate_output(result)
Return field-level errors to a human-facing UI and a structured error response to an API client. Do not expose stack traces or secrets. Keep the schema alongside the code and change its version when a client-visible contract changes.
4. Choose the adapter that matches the job
Streamlit: shortest path to a browser UI
Streamlit’s guide describes the workflow as adding Streamlit commands to a normal Python script and starting it with streamlit run; the command starts a local server and opens the app in a browser. See the official fundamentals guide.
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# app.py
import streamlit as st
from core import run_job
from schema import validate_input, validate_output
st.title('Greeting job')
name = st.text_input('Name')
count = st.number_input('Count', min_value=1, value=1, step=1)
if st.button('Run'):
try:
raw = {'name': name, 'count': count}
values = validate_input(raw)
result = validate_output(run_job(values['name'], values['count']))
st.json(result)
except ValueError as exc:
st.error(str(exc))
Start it with:
python -m pip install streamlit jsonschema
streamlit run app.py
Streamlit reruns the entire Python script when source code changes or a user interacts with a widget; callbacks run before the rest of the script. The architecture is documented at Streamlit’s architecture page. Do not put expensive work or irreversible side effects at module scope. Use a form to submit several fields together, cache only safe repeatable work, or move long jobs to a queue or background worker.
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Floom: a schema-first worker contract
Floom’s README describes a worker that non-developers can run from a UI, other systems can call through REST and AI agents can operate through MCP. A worker folder contains worker.yml, run.py and optionally requirements.txt.
# worker.yml
name: my-script
version: 1
exec:
entry: run.py
inputs:
type: object
required: [name, count]
properties:
name: {type: string, minLength: 1}
count: {type: integer, minimum: 1}
outputs:
type: object
required: [message, total]
properties:
message: {type: string}
total: {type: integer, minimum: 1}
# run.py
from core import run_job
from schema import validate_input, validate_output
def main(inputs):
values = validate_input(inputs)
return validate_output(run_job(values['name'], values['count']))
From the worker directory, the documented command shape is:
floom workers validate
floom workers push
floom run
Floom keeps worker definitions, schemas, logs, tool calls, approvals and run history inspectable. Script workers run in an E2B sandbox microVM by default, and triggers include manual, schedule, webhook and Composio events. The repository listed Python 3.11+, Node 20+, Linux, macOS and Windows support at the time of access; verify current requirements before deployment.
Hand-built HTTP API with OpenAPI
Choose an HTTP server when another application needs a stable endpoint, authentication, rate limits or generated clients. OpenAPI is the programming-language-independent description of paths, operations, parameters, request bodies, responses and security. JSON Schema describes the data objects nested inside those requests and responses. Keep the two layers distinct: OpenAPI documents how to call the service, while JSON Schema validates the values.
A request handler should decode JSON, call validate_input, invoke run_job, call validate_output and serialize the result. Configure authentication, authorization, persistence, queues and logging explicitly; neither a schema nor an API description supplies those controls by itself.
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Adapter comparison
| Option | Primary surface | Contract location | Execution model | Best fit |
|---|---|---|---|---|
| Streamlit | Browser UI | Python widgets plus your validators | Full-script rerun on interaction | Prototypes and internal data tools |
| Floom worker | UI, REST and MCP | worker.yml input/output schemas |
Recorded worker runs with sandboxing and triggers | Repeatable, auditable automations |
| Hand-built API with OpenAPI | HTTP API and generated clients | OpenAPI document with JSON Schema models | Request-driven server process | Public or integrated APIs |
5. Make the app deployable and observable
Pin and package dependencies
Record every runtime dependency in a lockfile or pinned requirements file, including the schema validator and adapter. Build the same environment in development, CI and production. Test valid, missing, wrong-type, boundary and extra-field cases against the schema.
Keep secrets out of source
Read API keys, database credentials and signing secrets from the deployment environment or a secret manager. Never place them in worker.yml, a Streamlit script, a schema example or a client-side bundle. Redact secrets and personal data from logs.
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Adding an optional response field is usually less disruptive than renaming a required field or changing its type. For a breaking change, publish a new schema version, endpoint or worker version and document which clients may use it. Retain the schema version with each run so an old request can be reproduced.
Plan for expensive and repeatable work
Streamlit interactions can execute the script repeatedly, so make operations idempotent where possible. For slow work, return a job identifier and process it through a queue or worker rather than blocking a browser request. Set timeouts, retry only operations that are safe to repeat, and record duration, validation outcome and failure reason.
6. Troubleshooting
“Required property” or type errors
Inspect the decoded payload, not the form label. A blank text widget is an empty string, and many UI number controls return a numeric value while query strings arrive as text. Normalize deliberately, then validate. Check the exact JSON path in the validator error.
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The app runs the job twice
In Streamlit, every widget interaction reruns the script. Put the call behind a submit button or form, avoid side effects during import, and use a queue or idempotency key for operations that must happen once.
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Run floom workers validate from the directory containing worker.yml. Confirm that the entry file exists, the manifest field names match the objects returned by run.py, and required dependencies are listed. Check the current repository instructions because supported runtime versions and hosted behavior can change.
Clients receive an output-schema failure
Log the returned object before serialization and validate it with the output schema. Common causes are a missing key, an integer accidentally converted to a string, or a new field combined with additionalProperties: false. Fix the producer or publish a new contract; do not silently drop data.
Long requests time out
Measure the core function separately from network and queue time. Move slow work to a background worker, expose progress or polling, and set an explicit server timeout. Keep retries bounded and include a correlation or job ID in logs.
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Frequently Asked Questions
Can JSON Schema validate business rules such as whether a username is already taken?
No. Schema can express the shape and many local constraints of a value. Rules that depend on a database, another service or current time belong in the application layer after schema validation.
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No. Keep the old contract available for existing clients and publish a new version or endpoint when required fields, types or meanings change.
Is Streamlit appropriate for a public, multi-user product?
It can be a UI layer, but production concerns such as authentication, authorization, concurrency, persistence and background execution must be configured and operated separately.
When should a worker return a result later instead of immediately?
Use an asynchronous job when execution can exceed request timeouts, consumes substantial resources or needs retries and progress reporting. Return a durable job identifier and expose status rather than holding the request open.
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