Added gradio-in-r

guides/09_other-tutorials/using-gradio-in-other-programming-languages.md

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+# Using Gradio in Other Programming Languages
+
+The core `gradio` library is a Python library. But you can also use `gradio` to create UIs around programs written in other languages, thanks to Python's ability to interface with external processes. Using Python's `subprocess` module, you can call programs written in C++, Rust, or virtually any other language, allowing `gradio` to become a flexible UI layer for non-Python applications.
+
+In this post, we'll walk through how to integrate `gradio` with C++ and Rust, using Python's `subprocess` module to invoke code written in these languages. We'll also discuss how to use Gradio with R, which is even easier, thanks to the [reticulate](https://rstudio.github.io/reticulate/) R package, which makes it possible to install and import Python modules in R.
+
+## Using Gradio with C++
+
+Let’s start with a simple example of integrating a C++ program into a Gradio app. Suppose we have the following C++ program that adds two numbers:
+
+```cpp
+// add.cpp
+#include <iostream>
+
+int main() {
+    double a, b;
+    std::cin >> a >> b;
+    std::cout << a + b << std::endl;
+    return 0;
+}
+```
+
+This program reads two numbers from standard input, adds them, and outputs the result.
+
+We can build a Gradio interface around this C++ program using Python's `subprocess` module. Here’s the corresponding Python code:
+
+```python
+import gradio as gr
+import subprocess
+
+def add_numbers(a, b):
+    process = subprocess.Popen(
+        ['./add'], 
+        stdin=subprocess.PIPE, 
+        stdout=subprocess.PIPE, 
+        stderr=subprocess.PIPE
+    )
+    output, error = process.communicate(input=f"{a} {b}\n".encode())
+
+    if error:
+        return f"Error: {error.decode()}"
+    return float(output.decode().strip())
+
+demo = gr.Interface(
+    fn=add_numbers, 
+    inputs=[gr.Number(label="Number 1"), gr.Number(label="Number 2")], 
+    outputs=gr.Textbox(label="Result")
+)
+
+demo.launch()
+```
+
+Here, `subprocess.Popen` is used to execute the compiled C++ program (`add`), pass the input values, and capture the output. You can compile the C++ program by running:
+
+```bash
+g++ -o add add.cpp
+```
+
+This example shows how easy it is to call C++ from Python using `subprocess` and build a Gradio interface around it.
+
+## Using Gradio with Rust
+
+Now, let’s move to another example: calling a Rust program to apply a sepia filter to an image. The Rust code could look something like this:
+
+```rust
+// sepia.rs
+extern crate image;
+
+use image::{GenericImageView, ImageBuffer, Rgba};
+
+fn sepia_filter(input: &str, output: &str) {
+    let img = image::open(input).unwrap();
+    let (width, height) = img.dimensions();
+    let mut img_buf = ImageBuffer::new(width, height);
+
+    for (x, y, pixel) in img.pixels() {
+        let (r, g, b, a) = (pixel[0] as f32, pixel[1] as f32, pixel[2] as f32, pixel[3]);
+        let tr = (0.393 * r + 0.769 * g + 0.189 * b).min(255.0);
+        let tg = (0.349 * r + 0.686 * g + 0.168 * b).min(255.0);
+        let tb = (0.272 * r + 0.534 * g + 0.131 * b).min(255.0);
+        img_buf.put_pixel(x, y, Rgba([tr as u8, tg as u8, tb as u8, a]));
+    }
+
+    img_buf.save(output).unwrap();
+}
+
+fn main() {
+    let args: Vec<String> = std::env::args().collect();
+    if args.len() != 3 {
+        eprintln!("Usage: sepia <input_file> <output_file>");
+        return;
+    }
+    sepia_filter(&args[1], &args[2]);
+}
+```
+
+This Rust program applies a sepia filter to an image. It takes two command-line arguments: the input image path and the output image path. You can compile this program using:
+
+```bash
+cargo build --release
+```
+
+Now, we can call this Rust program from Python and use Gradio to build the interface:
+
+```python
+import gradio as gr
+import subprocess
+
+def apply_sepia(input_path):
+    output_path = "output.png"
+
+    process = subprocess.Popen(
+        ['./target/release/sepia', input_path, output_path], 
+        stdout=subprocess.PIPE, 
+        stderr=subprocess.PIPE
+    )
+    process.wait()
+
+    return output_path
+
+demo = gr.Interface(
+    fn=apply_sepia, 
+    inputs=gr.Image(type="filepath", label="Input Image"), 
+    outputs=gr.Image(label="Sepia Image")
+)
+
+demo.launch()
+```
+
+Here, when a user uploads an image and clicks submit, Gradio calls the Rust binary (`sepia`) to process the image, and returns the sepia-filtered output to Gradio.
+
+This setup showcases how you can integrate performance-critical or specialized code written in Rust into a Gradio interface.
+
+## Using Gradio with R (via `reticulate`)
+
+Integrating Gradio with R is particularly straightforward thanks to the `reticulate` package, which allows you to run Python code directly in R. Let’s walk through an example of using Gradio in R. 
+
+**Installation**
+
+First, you need to install the `reticulate` package in R:
+
+```r
+install.packages("reticulate")
+```
+
+
+Once installed, you can use the package to run Gradio directly from within an R script.
+
+
+```r
+library(reticulate)
+py_install("gradio", pip = TRUE)
+
+gr <- import("gradio") # import gradio as gr
+```
+
+**Building a Gradio Application**
+
+With gradio installed and imported, we now have access to gradio's app building methods. Let's build a simple app for an R function that returns a greeting
+
+```r
+greeting <- \(name) paste("Hello", name)
+
+app <- gr$Interface(
+  fn = greeting,
+  inputs = gr$Text(label = "Name"),
+  outputs = gr$Text(label = "Greeting"),
+  title = "Hello! &#128515 &#128075"
+)
+
+app$launch(server_name = "localhost", 
+           server_port = as.integer(3000))
+```
+
+Credit to [@IfeanyiIdiaye](https://github.com/Ifeanyi55) for contributing this section. You can see more examples [here](https://github.com/Ifeanyi55/Gradio-in-R/tree/main/Code), including using a full example of using Gradio Blocks to build a machine learning application in R.