+
+At Gradio, we often try to understand what inputs a model is particularly sensitive to. To help facilitate this, we've developed and open-sourced `gradio`, a python library that allows you to quickly create input and output interfaces over trained models to make it easy for you to "play around" with your model in your browser by dragging-and-dropping in your own images (or pasting your own text, recording your own voice, etc.) and seeing what the model outputs. `gradio` can also generate a share link which allows anyone, anywhere to use the interface as the model continues to run on your machine.
+
+Gradio is useful for:
+* Creating demos of your machine learning code for clients / collaborators / users
+* Getting feedback on model performance from users
+* Debugging your model interactively during development
+
+To get a sense of `gradio`, take a look at a few of these examples, and find more on our website: www.gradio.app.
+
+## Installation
+```
+pip install gradio
+```
+(you may need to replace `pip` with `pip3` if you're running `python3`).
+
+## Usage
+
+Gradio is very easy to use with your existing code. Here are a few working examples:
+
+### 0. Hello World [](https://colab.research.google.com/drive/18ODkJvyxHutTN0P5APWyGFO_xwNcgHDZ?usp=sharing)
+
+Let's start with a basic function (no machine learning yet!) that greets an input name. We'll wrap the function with a `Text` to `Text` interface.
+
+```python
+import gradio as gr
+
+def greet(name):
+ return "Hello " + name + "!"
+
+gr.Interface(fn=greet, inputs="text", outputs="text").launch()
+```
+
+The core Interface class is initialized with three parameters:
+
+- `fn`: the function to wrap
+- `inputs`: the name of the input interface
+- `outputs`: the name of the output interface
+
+Calling the `launch()` function of the `Interface` object produces the interface shown in image below. Click on the screenshot to go the live interface in our getting started page.
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