gradio/guides/10_other-tutorials/image-classification-in-tensorflow.md

# Image Classification in TensorFlow and Keras

Related spaces: https://huggingface.co/spaces/abidlabs/keras-image-classifier
Tags: VISION, MOBILENET, TENSORFLOW

## Introduction

Image classification is a central task in computer vision. Building better classifiers to classify what object is present in a picture is an active area of research, as it has applications stretching from traffic control systems to satellite imaging.

Such models are perfect to use with Gradio's _image_ input component, so in this tutorial we will build a web demo to classify images using Gradio. We will be able to build the whole web application in Python, and it will look like the demo on the bottom of the page.


Let's get started!

### Prerequisites

Make sure you have the `gradio` Python package already [installed](/getting_started). We will be using a pretrained Keras image classification model, so you should also have `tensorflow` installed.

## Step 1 — Setting up the Image Classification Model

First, we will need an image classification model. For this tutorial, we will use a pretrained Mobile Net model, as it is easily downloadable from [Keras](https://keras.io/api/applications/mobilenet/). You can use a different pretrained model or train your own.

```python
import tensorflow as tf

inception_net = tf.keras.applications.MobileNetV2()
```

This line automatically downloads the MobileNet model and weights using the Keras library.

## Step 2 — Defining a `predict` function

Next, we will need to define a function that takes in the _user input_, which in this case is an 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://git.io/JJkYN).

In the case of our pretrained model, it will look like this:

```python
import requests

# Download human-readable labels for ImageNet.
response = requests.get("https://git.io/JJkYN")
labels = response.text.split("\n")

def classify_image(inp):
  inp = inp.reshape((-1, 224, 224, 3))
  inp = tf.keras.applications.mobilenet_v2.preprocess_input(inp)
  prediction = inception_net.predict(inp).flatten()
  confidences = {labels[i]: float(prediction[i]) for i in range(1000)}
  return confidences
```

Let's break this down. The function takes one parameter:

- `inp`: the input image as a `numpy` array

Then, the function adds a batch dimension, passes it through the model, and returns:

- `confidences`: the 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 drag-and-drop image component. To create this input, we can use the `"gradio.inputs.Image"` class, which creates the component and handles the preprocessing to convert that to a numpy array. We will instantiate the class with a parameter that automatically preprocesses the input image to be 224 pixels by 224 pixels, which is the size that MobileNet expects.

The output component will be a `"label"`, which displays the top labels in a nice form. Since we don't want to show all 1,000 class labels, we will customize it to show only the top 3 images.

Finally, we'll add one more parameter, the `examples`, which allows us to prepopulate our interfaces with a few predefined examples. The code for Gradio looks like this:

```python
import gradio as gr

gr.Interface(fn=classify_image,
             inputs=gr.Image(width=224, height=224),
             outputs=gr.Label(num_top_classes=3),
             examples=["banana.jpg", "car.jpg"]).launch()
```

This produces the following interface, which you can try right here in your browser (try uploading your own examples!):

<gradio-app space="gradio/keras-image-classifier">

---

And you're done! That's all the code you need to build a web demo for an image classifier. If you'd like to share with others, try setting `share=True` when you `launch()` the Interface!