### Install fal-client

Source: https://github.com/fal-ai/fal/blob/main/projects/fal_client/README.md

Install the fal-client library using pip.

```bash
pip install fal-client
```

--------------------------------

### Install Fal Packages from Source

Source: https://github.com/fal-ai/fal/blob/main/README.md

Install the fal, fal-client, and isolate_proto packages in editable mode from the repository root. This is useful for development and contributing to the project.

```bash
pip install -e 'projects/fal[dev]'
pip install -e 'projects/fal_client[dev]'
pip install -e 'projects/isolate_proto[dev]'
```

--------------------------------

### Install Fal Client with Dev Dependencies

Source: https://github.com/fal-ai/fal/blob/main/projects/fal_client/AGENTS.md

Install the fal_client package in editable mode with development dependencies. Run this command from the repository root.

```bash
pip install -e 'projects/fal_client[dev]'
```

--------------------------------

### Install and Authenticate Fal

Source: https://github.com/fal-ai/fal/blob/main/README.md

Install the fal package and log in to your Fal account. This is the first step to deploying Python code to Fal.

```bash
pip install fal
fal auth login
```

--------------------------------

### Install Development Dependencies

Source: https://github.com/fal-ai/fal/blob/main/AGENTS.md

Installs development dependencies for the main fal package, fal_client, and isolate_proto from the repository root. Use individual package installations for faster iteration when working on a single package.

```bash
python -m pip install --upgrade pip
pip install -e 'projects/fal[dev]'
pip install -e 'projects/fal_client[dev]'
pip install -e 'projects/isolate_proto[dev]'
```

--------------------------------

### Install Development Dependencies

Source: https://github.com/fal-ai/fal/blob/main/projects/isolate_proto/AGENTS.md

Install the isolate_proto package with development dependencies from the repository root.

```bash
pip install -e 'projects/isolate_proto[dev]'
```

--------------------------------

### Install Fal Client and Set API Key

Source: https://github.com/fal-ai/fal/blob/main/README.md

Install the fal-client package and set your Fal API key as an environment variable. This is necessary for making calls to Fal models and endpoints.

```bash
pip install fal-client
export FAL_KEY="your-api-key"
```

--------------------------------

### Install Fal Package with Dev Dependencies

Source: https://github.com/fal-ai/fal/blob/main/projects/fal/AGENTS.md

Install the fal package in editable mode with development dependencies from the repository root.

```bash
pip install -e 'projects/fal[dev]'
```

--------------------------------

### Define a Minimal Fal App

Source: https://github.com/fal-ai/fal/blob/main/README.md

Create a basic Fal application using the `fal.App` class and define an endpoint. This serves as a starting point for serverless applications.

```python
import fal


class MyApp(fal.App):
    @fal.endpoint("/")
    def run(self) -> dict:
        return {"message": "Hello, World!"}
```

--------------------------------

### Call a Fal Model API

Source: https://github.com/fal-ai/fal/blob/main/README.md

Use the fal-client to subscribe to and call a Fal model. This example demonstrates how to generate an image using a specified prompt and size.

```python
import fal_client

result = fal_client.subscribe(
    "fal-ai/flux/schnell",
    arguments={
        "prompt": "a futuristic cityscape at sunset",
        "image_size": "landscape_16_9",
    },
)

print(result["images"][0]["url"])
```

--------------------------------

### Regenerate gRPC Definitions

Source: https://github.com/fal-ai/fal/blob/main/projects/isolate_proto/README.md

Run these commands in the 'projects/isolate_proto' directory to regenerate gRPC definitions. Ensure you have the development dependencies installed and provide a valid isolate version tag.

```bash
$ cd projects/isolate_proto
$ pip install -e '.[dev]'
$ python ../../tools/regen_grpc.py --isolate-version <isolate version>
$ pre-commit run --all-files
```

--------------------------------

### Regenerate gRPC Code

Source: https://github.com/fal-ai/fal/blob/main/projects/isolate_proto/AGENTS.md

Workflow to regenerate gRPC code from proto definitions within the projects/isolate_proto directory. Ensure development dependencies are installed and run pre-commit hooks and tests afterward.

```bash
pip install -e '.[dev]'
python ../../tools/regen_grpc.py --isolate-version <isolate-version>
pre-commit run --all-files
pytest tests
```

--------------------------------

### Build Documentation

Source: https://github.com/fal-ai/fal/blob/main/AGENTS.md

Builds both SDK and client documentation from the repository root. This command delegates to project-level documentation builds and assembles the output.

```bash
make docs
```

--------------------------------

### Create a Basic Client

Source: https://github.com/fal-ai/fal/blob/main/projects/fal/openapi-fal-rest/README.md

Instantiate the client for making API requests to a specified base URL.

```python
from openapi_fal_rest import Client

client = Client(base_url="https://api.example.com")
```

--------------------------------

### Run Local/Unit Tests

Source: https://github.com/fal-ai/fal/blob/main/projects/fal_client/AGENTS.md

Execute only the local and unit tests for the fal_client package. This is useful when cloud credentials are unavailable. Run this command from the repository root.

```bash
pytest projects/fal_client/tests/unit
```

--------------------------------

### Run All Repository Hooks with Pre-commit

Source: https://github.com/fal-ai/fal/blob/main/projects/fal/README.md

Execute all pre-commit hooks across the entire repository to ensure code quality and consistency before committing.

```bash
pre-commit run --all-files
```

--------------------------------

### Run Integration Tests for Fal Package

Source: https://github.com/fal-ai/fal/blob/main/projects/fal/AGENTS.md

Execute integration tests for the fal package, requiring FAL_KEY and FAL_HOST environment variables.

```bash
FAL_KEY=... FAL_HOST=api.fal.dev FAL_RUN_HOST=run.fal.dev \
  pytest -n auto -v projects/fal/tests/integration
```

--------------------------------

### Create an Authenticated Client

Source: https://github.com/fal-ai/fal/blob/main/projects/fal/openapi-fal-rest/README.md

Instantiate the client for making authenticated API requests using a token.

```python
from openapi_fal_rest import AuthenticatedClient

client = AuthenticatedClient(base_url="https://api.example.com", token="SuperSecretToken")
```

--------------------------------

### Run a Model Synchronously

Source: https://github.com/fal-ai/fal/blob/main/projects/fal_client/README.md

Use the client to run a model and print the resulting image URL. Ensure the API key is set.

```python
import fal_client

response = fal_client.run("fal-ai/fast-sdxl", arguments={"prompt": "a cute cat, realistic, orange"})
print(response["images"][0]["url"])
```

--------------------------------

### Run Fal App for Testing

Source: https://github.com/fal-ai/fal/blob/main/README.md

Execute your Fal application locally for testing purposes using the `fal run` command. This command allows you to quickly iterate on your code.

```bash
fal run hello_world.py::MyApp
```

--------------------------------

### Run End-to-End Tests for Fal Package

Source: https://github.com/fal-ai/fal/blob/main/projects/fal/AGENTS.md

Execute end-to-end tests for the fal package, requiring FAL_KEY and FAL_RUN_HOST environment variables, with gRPC host specified.

```bash
FAL_KEY=... FAL_GRPC_HOST=api.fal.dev FAL_RUN_HOST=run.fal.dev \
  pytest -n auto -v projects/fal/tests/e2e
```

--------------------------------

### Authenticated Client with Custom SSL Certificate

Source: https://github.com/fal-ai/fal/blob/main/projects/fal/openapi-fal-rest/README.md

Instantiate an authenticated client using a custom certificate bundle for SSL verification.

```python
client = AuthenticatedClient(
    base_url="https://internal_api.example.com", 
    token="SuperSecretToken",
    verify_ssl="/path/to/certificate_bundle.pem",
)
```

--------------------------------

### Deploy Fal App to Production

Source: https://github.com/fal-ai/fal/blob/main/README.md

Deploy your Fal application to a persistent endpoint using the `fal deploy` command. This makes your application accessible in the cloud.

```bash
fal deploy hello_world.py::MyApp
```

--------------------------------

### Run Tests for fal_client package

Source: https://github.com/fal-ai/fal/blob/main/AGENTS.md

Executes tests for the 'fal_client' package using pytest.

```bash
pytest projects/fal_client/tests
```

--------------------------------

### Generate OpenAPI REST Client

Source: https://github.com/fal-ai/fal/blob/main/AGENTS.md

Generates the OpenAPI REST client for the 'fal' package. This command should be run from the 'projects/fal' directory using the provided configuration file and OpenAPI spec path.

```bash
cd projects/fal
openapi-python-client generate --config openapi_rest.config.yaml --path <openapi-spec>
```

--------------------------------

### Set API Key

Source: https://github.com/fal-ai/fal/blob/main/projects/fal_client/README.md

Set your fal.ai API key as an environment variable.

```bash
export FAL_KEY=your-api-key
```

--------------------------------

### Authenticated Client with SSL Verification Disabled

Source: https://github.com/fal-ai/fal/blob/main/projects/fal/openapi-fal-rest/README.md

Instantiate an authenticated client with SSL verification disabled. This is a security risk and should be used with caution.

```python
client = AuthenticatedClient(
    base_url="https://internal_api.example.com", 
    token="SuperSecretToken", 
    verify_ssl=False
)
```

--------------------------------

### Upload File for Model Input

Source: https://github.com/fal-ai/fal/blob/main/projects/fal_client/README.md

Upload an audio file to fal.media and pass its URL to the model. This is suitable for larger files or when CDN usage is preferred.

```python
import fal_client

audio_url = fal_client.upload_file("path/to/audio.wav")
response = fal_client.run("fal-ai/whisper", arguments={"audio_url": audio_url})
print(response["text"])
```

--------------------------------

### Run Unit Tests for Fal Package

Source: https://github.com/fal-ai/fal/blob/main/projects/fal/AGENTS.md

Execute unit tests for the fal package using pytest, leveraging parallel execution.

```bash
pytest -n auto -v projects/fal/tests/unit
```

--------------------------------

### Regenerate gRPC Bindings

Source: https://github.com/fal-ai/fal/blob/main/AGENTS.md

Regenerates gRPC bindings for the 'isolate_proto' package. This command should be run from the 'projects/isolate_proto' directory after changing .proto definitions. It requires the 'isolate-version' to be specified.

```bash
pip install -e '.[dev]'
python ../../tools/regen_grpc.py --isolate-version <isolate-version>
pre-commit run --all-files
```

--------------------------------

### Run a Model Asynchronously

Source: https://github.com/fal-ai/fal/blob/main/projects/fal_client/README.md

Execute a model asynchronously and print the resulting image URL. This is useful for non-blocking operations.

```python
import asyncio
import fal_client

async def main():
    response = await fal_client.run_async("fal-ai/fast-sdxl", arguments={"prompt": "a cute cat, realistic, orange"})
    print(response["images"][0]["url"])


asyncio.run(main())
```

--------------------------------

### Run Full Validation Workflow

Source: https://github.com/fal-ai/fal/blob/main/projects/fal_client/AGENTS.md

Execute the complete validation workflow, including pre-commit checks and all pytest tests. Run this command from the repository root.

```bash
pre-commit run --all-files
pytest projects/fal_client/tests
```

--------------------------------

### Run Tests for isolate_proto package

Source: https://github.com/fal-ai/fal/blob/main/AGENTS.md

Executes tests for the 'isolate_proto' package using pytest.

```bash
pytest projects/isolate_proto/tests
```

--------------------------------

### Encode File as Data URL

Source: https://github.com/fal-ai/fal/blob/main/projects/fal_client/README.md

Encode a local audio file into a data URL for direct use with the client, avoiding CDN uploads. This can reduce latency for smaller files.

```python
import fal_client

audio_data_url = fal_client.encode_file("path/to/audio.wav")
response = fal_client.run("fal-ai/whisper", arguments={"audio_url": audio_data_url})
print(response["text"])
```

--------------------------------

### Call an Endpoint (Synchronous)

Source: https://github.com/fal-ai/fal/blob/main/projects/fal/openapi-fal-rest/README.md

Make a synchronous API call to retrieve parsed data or detailed response information.

```python
from openapi_fal_rest.models import MyDataModel
from openapi_fal_rest.api.my_tag import get_my_data_model
from openapi_fal_rest.types import Response

my_data: MyDataModel = get_my_data_model.sync(client=client)
# or if you need more info (e.g. status_code)
response: Response[MyDataModel] = get_my_data_model.sync_detailed(client=client)
```

--------------------------------

### Submit Request and Stream Updates

Source: https://github.com/fal-ai/fal/blob/main/projects/fal_client/README.md

Submit a request asynchronously and iterate through events to receive real-time status updates, logs, and the final result. This is ideal for long-running tasks.

```python
import asyncio
import fal_client

async def main():
    response = await fal_client.submit_async("fal-ai/fast-sdxl", arguments={"prompt": "a cute cat, realistic, orange"})

    logs_index = 0
    async for event in response.iter_events(with_logs=True):
        if isinstance(event, fal_client.Queued):
            print("Queued. Position:", event.position)
        elif isinstance(event, (fal_client.InProgress, fal_client.Completed)):
            new_logs = event.logs[logs_index:]
            for log in new_logs:
                print(log["message"])
            logs_index = len(event.logs)

    result = await response.get()
    print(result["images"][0]["url"])


asyncio.run(main())
```

--------------------------------

### Call an Endpoint (Asynchronous)

Source: https://github.com/fal-ai/fal/blob/main/projects/fal/openapi-fal-rest/README.md

Make an asynchronous API call to retrieve parsed data or detailed response information.

```python
from openapi_fal_rest.models import MyDataModel
from openapi_fal_rest.api.my_tag import get_my_data_model
from openapi_fal_rest.types import Response

my_data: MyDataModel = await get_my_data_model.asyncio(client=client)
response: Response[MyDataModel] = await get_my_data_model.asyncio_detailed(client=client)
```

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