How well can an algorithm guess what you're drawing? A few years ago, Google released the **Quick Draw** dataset, which contains drawings made by humans of a variety of every objects. Researchers have used this dataset to train models to guess Pictionary-style drawings.
Such models are perfect to use with Gradio's *sketchpad* input, so in this tutorial we will build a Pictionary web application using Gradio. We will be able to build the whole web application in Python, and will look like this (try drawing something!):
Make sure you have the `gradio` Python package already [installed](/getting_started). To use the pretrained sketchpad model, also install `torch`.
## Step 1 — Setting up the Sketch Recognition Model
First, you will need a sketch recognition model. Since many researchers have already trained their own models on the Quick Draw dataset, we will use a pretrained model in this tutorial. Our model is a light 1.5 MB model trained by Nate Raw, that [you can download here](https://huggingface.co/spaces/nateraw/quickdraw/blob/main/pytorch_model.bin).
If you are interested, here [is the code](https://github.com/nateraw/quickdraw-pytorch) that was used to train the model. We will simply load the pretrained model in PyTorch, as follows:
Next, you will need to define a function that takes in the *user input*, which in this case is a sketched image, and returns the prediction. The prediction should be returned as a dictionary whose keys are class name and values are confidence probabilities. We will load the class names from this [text file](https://huggingface.co/spaces/nateraw/quickdraw/blob/main/class_names.txt).
In the case of our pretrained model, it will look like this:
confidences = {LABELS[i]: v.item() for i, v in zip(indices, values)}
return confidences
```
Let's break this down. The function takes one parameters:
*`img`: the input image as a `numpy` array
Then, the function converts the image to a PyTorch `tensor`, passes it through the model, and returns:
*`confidences`: the top five predictions, as a dictionary whose keys are class labels and whose values are confidence probabilities
## Step 3 — Creating a Gradio Interface
Now that we have our predictive function set up, we can create a Gradio Interface around it.
In this case, the input component is a sketchpad. To create a sketchpad input, we can use the convenient string shortcut, `"sketchpad"` which creates a canvas for a user to draw on and handles the preprocessing to convert that to a numpy array.
The output component will be a `"label"`, which displays the top labels in a nice form.
Finally, we'll add one more parameter, setting `live=True`, which allows our interface to run in real time, adjusting its predictions every time a user draws on the sketchpad. The code for Gradio looks like this:
```python
import gradio as gr
gr.Interface(fn=predict,
inputs="sketchpad",
outputs="label",
live=True).launch()
```
This produces the following interface, which you can try right here in your browser (try drawing something, like a "snake" or a "laptop"):
