| # More on Examples | |
| In the [previous Guide](/main/guides/the-interface-class), we discussed how to provide example inputs for your demo to make it easier for users to try it out. Here, we dive into more details. | |
| ## Providing Examples | |
| Adding examples to an Interface is as easy as providing a list of lists to the `examples` | |
| keyword argument. | |
| Each sublist is a data sample, where each element corresponds to an input of the prediction function. | |
| The inputs must be ordered in the same order as the prediction function expects them. | |
| If your interface only has one input component, then you can provide your examples as a regular list instead of a list of lists. | |
| ### Loading Examples from a Directory | |
| You can also specify a path to a directory containing your examples. If your Interface takes only a single file-type input, e.g. an image classifier, you can simply pass a directory filepath to the `examples=` argument, and the `Interface` will load the images in the directory as examples. | |
| In the case of multiple inputs, this directory must | |
| contain a log.csv file with the example values. | |
| In the context of the calculator demo, we can set `examples='/demo/calculator/examples'` and in that directory we include the following `log.csv` file: | |
| ```csv | |
| num,operation,num2 | |
| 5,"add",3 | |
| 4,"divide",2 | |
| 5,"multiply",3 | |
| ``` | |
| This can be helpful when browsing flagged data. Simply point to the flagged directory and the `Interface` will load the examples from the flagged data. | |
| ### Providing Partial Examples | |
| Sometimes your app has many input components, but you would only like to provide examples for a subset of them. In order to exclude some inputs from the examples, pass `None` for all data samples corresponding to those particular components. | |
| ## Caching examples | |
| You may wish to provide some cached examples of your model for users to quickly try out, in case your model takes a while to run normally. | |
| If `cache_examples=True`, your Gradio app will run all of the examples and save the outputs when you call the `launch()` method. This data will be saved in a directory called `gradio_cached_examples` in your working directory by default. You can also set this directory with the `GRADIO_EXAMPLES_CACHE` environment variable, which can be either an absolute path or a relative path to your working directory. | |
| Whenever a user clicks on an example, the output will automatically be populated in the app now, using data from this cached directory instead of actually running the function. This is useful so users can quickly try out your model without adding any load! | |
| Alternatively, you can set `cache_examples="lazy"`. This means that each particular example will only get cached after it is first used (by any user) in the Gradio app. This is helpful if your prediction function is long-running and you do not want to wait a long time for your Gradio app to start. | |
| Keep in mind once the cache is generated, it will not be updated automatically in future launches. If the examples or function logic change, delete the cache folder to clear the cache and rebuild it with another `launch()`. | |