### Installing Gradio CLI and Viewing Help

Source: https://github.com/jacoblincool/gradio-rs/blob/main/README.md

This snippet provides instructions for installing the `gradio-rs` command-line interface using `cargo`, Rust's package manager. It also shows how to access the CLI's help documentation to understand available commands and options.

```sh
cargo install gradio
gr --help
```

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

### Listing API Specifications for Gradio Space via CLI

Source: https://github.com/jacoblincool/gradio-rs/blob/main/README.md

This example demonstrates how to use the `gr list` command from the `gradio-rs` CLI to retrieve and display the detailed API specification for a specific Hugging Face Gradio Space, such as `stabilityai/stable-diffusion-3-medium`. It outlines the available parameters, their types, and return values for the `/infer` endpoint.

```sh
> gr list stabilityai/stable-diffusion-3-medium
API Spec for stabilityai/stable-diffusion-3-medium:
	/infer
		Parameters:
			prompt               ( str      ) 
			negative_prompt      ( str      ) 
			seed                 ( float    ) numeric value between 0 and 2147483647
			randomize_seed       ( bool     ) 
			width                ( float    ) numeric value between 256 and 1344
			height               ( float    ) numeric value between 256 and 1344
			guidance_scale       ( float    ) numeric value between 0.0 and 10.0
			num_inference_steps  ( float    ) numeric value between 1 and 50
		Returns:
			Result               ( filepath ) 
			Seed                 ( float    ) numeric value between 0 and 2147483647
```

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

### Running Stable Diffusion 3 Inference via Gradio CLI

Source: https://github.com/jacoblincool/gradio-rs/blob/main/README.md

This command-line example illustrates how to perform an image generation inference using the `gr run` command with the `stabilityai/stable-diffusion-3-medium` Gradio Space. It demonstrates passing various parameters like prompt, negative prompt, seed, dimensions, guidance scale, and inference steps, then displays the URL of the generated image and the final seed.

```sh
> gr run stabilityai/stable-diffusion-3-medium infer 'Rusty text "AI & CLI" on the snow.' '' 0 true 1024 1024 5 28
Result: https://stabilityai-stable-diffusion-3-medium.hf.space/file=/tmp/gradio/5735ca7775e05f8d56d929d8f57b099a675c0a01/image.webp
Seed: 486085626
```

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

### Separating Vocals and Background Music with Gradio Client in Rust

Source: https://github.com/jacoblincool/gradio-rs/blob/main/README.md

This Rust example demonstrates how to use the `gradio-rs` client to connect to a Gradio space (specifically, a vocal separation model) and perform a prediction. It shows how to pass a local audio file and a model name as `PredictionInput` and then extract the URLs of the separated vocal and background tracks from the prediction output. The program expects an audio file path as a command-line argument.

```Rust
use gradio::{PredictionInput, Client, ClientOptions};

#[tokio::main]
async fn main() {
    if std::env::args().len() < 2 {
        println!("Please provide an audio file path as an argument");
        std::process::exit(1);
    }
    let args: Vec<String> = std::env::args().collect();
    let file_path = &args[1];
    println!("File: {}", file_path);

    let client = Client::new("JacobLinCool/vocal-separation", ClientOptions::default())
        .await
        .unwrap();

    let output = client
        .predict(
            "/separate",
            vec![
                PredictionInput::from_file(file_path),
                PredictionInput::from_value("BS-RoFormer"),
            ],
        )
        .await
        .unwrap();
    println!(
        "Vocals: {}",
        output[0].clone().as_file().unwrap().url.unwrap()
    );
    println!(
        "Background: {}",
        output[1].clone().as_file().unwrap().url.unwrap()
    );
}
```

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

### Transcribing Audio with Whisper Model via Gradio CLI

Source: https://github.com/jacoblincool/gradio-rs/blob/main/README.md

This snippet showcases how to use the `gr run` command to interact with an audio transcription model (Whisper-large-v3) hosted on a Gradio Space. It demonstrates passing a local audio file path as an argument for the `predict` endpoint and displays the transcribed text output.

```sh
gr run hf-audio/whisper-large-v3 predict 'test-audio.wav' 'transcribe'
output: " Did you know you can try the coolest model on your command line?"
```

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