AI Deploy - Tutorial - Deploy an interactive app for EDA and prediction using Streamlit
How to deploy a Streamlit app for interactive data visualization and prediction
Last updated 31st January, 2023.
AI Deploy is in beta. During the beta-testing phase, the infrastructure’s availability and data longevity are not guaranteed. Please do not use this service for applications that are in production, as this phase is not complete.
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Objective
The objective of this tutorial is to deploy an application for Exploratory Data Analysis (EDA) and interactive prediction.
The use case is the IRIS dataset. It is a collection of data containing information on 50 observations of four different variables: petal length, petal width, sepal length and sepal width.
The first objective is to perform data mining on the source data from the Iris dataset.
Then, the goal is to make predictions using a trained model. We will therefore use a neural network and weights from this notebook to classify iris flowers between different species (Setosa, Versicolor and Virginica). The four features are sent as input to the neural network.
The PyTorch model will be loaded and run, and the prediction probabilities for each class will be extracted. Depending on the input data, it will be possible to visualize the corresponding iris on a PCA plot to compare the input to other data points in our Iris dataset.
In order to do this, you will use Streamlit, a Python framework that turns scripts into a shareable web application. You will also learn how to build and use a custom Docker image for a Streamlit application.
Requirements
- Access to the OVHcloud Control Panel;
- An AI Deploy project created inside a Public Cloud project in your OVHcloud account;
- A user for AI Deploy;
- Docker installed on your local computer;
- Some knowledge about building image and Dockerfile;
- You also should have followed the Step 6 of the Classification of iris varieties within the same species notebook from the GitHub repository. You will be able to save your PyTorch model
model_iris_classification.pth.
Instructions
You are going to follow different steps to build your Streamlit application.
- More information about Streamlit capabilities can be found here.
- Direct link to the full Python file can be found here here.
Here we will mainly discuss how to write the utils.py and app.py codes, the requirements.txt file and the Dockerfile. If you want to see the whole code, please refer to the GitHub repository.
Create the Streamlit application
Two Python files are created for the purpose of loading the PyTorch model and building the Streamlit app.
Define the utils.py file
This Python file is dedicated to load the PyTorch model. You can find the full code here.
First, we have to define the Model class with neural network architecture.
class Model(nn.Module):
def __init__(self):
super().__init__()
# fully connected layer : 4 input features for 4 parameters in X
self.layer1 = nn.Linear(in_features=4, out_features=16)
# fully connected layer
self.layer2 = nn.Linear(in_features=16, out_features=12)
# output layer : 3 output features for 3 species
self.output = nn.Linear(in_features=12, out_features=3)
def forward(self, x):
# activation fonction : reLU
x = F.relu(self.layer1(x))
x = F.relu(self.layer2(x))
x = self.output(x)
return x
Then, we create the function to load model checkpoint.
def load_checkpoint(path):
model = Model()
print("Model display: ", model)
model.load_state_dict(torch.load(path))
model.eval()
return model
The last function allows us to load the model and get predictions.
To learn more about how you can save a model with PyTorch, please refer to the last step "Save the model for future inference" of the notebook.
You can find the PyTorch model model_iris_classification.pth on the GitHub repository.
def load_model(X_tensor):
model = load_checkpoint(path)
predict_out = model(X_tensor)
_, predict_y = torch.max(predict_out, 1)
return predict_out.squeeze().detach().numpy(), predict_y.item()
# pytorch model
path = "model_iris_classification.pth"
Write the app.py file
Load the IRIS dataset for EDA.
@st.cache
def load_data():
# load the iris dataset with sklearn
dataset_iris = load_iris()
# define inputs and output
df_inputs = pd.DataFrame(dataset_iris.data, columns=dataset_iris.feature_names)
df_output = pd.DataFrame(dataset_iris.target, columns=['variety'])
return df_inputs, df_output
Display EDA figure based on source dataset.
@st.cache(allow_output_mutation=True)
def data_visualization(df_inputs, df_output):
df = pd.concat([df_inputs, df_output['variety']], axis=1)
eda = sns.pairplot(data=df, hue="variety", palette=['#0D0888', '#CB4779', '#F0F922'])
return eda
Create a sidebar with sliders.
def create_slider(df_inputs):
# slidebars with min, max and mean (by default) values
sepal_length = st.sidebar.slider(
label='Sepal Length',
min_value=float(df_inputs['sepal length (cm)'].min()),
max_value=float(df_inputs['sepal length (cm)'].max()),
value=float(round(df_inputs['sepal length (cm)'].mean(), 1)),
step=0.1)
sepal_width = st.sidebar.slider(
label='Sepal Width',
min_value=float(df_inputs['sepal width (cm)'].min()),
max_value=float(df_inputs['sepal width (cm)'].max()),
value=float(round(df_inputs['sepal width (cm)'].mean(), 1)),
step=0.1)
petal_length = st.sidebar.slider(
label='Petal Length',
min_value=float(df_inputs['petal length (cm)'].min()),
max_value=float(df_inputs['petal length (cm)'].max()),
value=float(round(df_inputs['petal length (cm)'].mean(), 1)),
step=0.1)
petal_width = st.sidebar.slider(
label='Petal Width',
min_value=float(df_inputs['petal width (cm)'].min()),
max_value=float(df_inputs['petal width (cm)'].max()),
value=float(round(df_inputs['petal width (cm)'].mean(), 1)),
step=0.1)
return sepal_length, sepal_width, petal_length, petal_width
Run a PCA.
@st.cache
def run_pca():
pca = PCA(2)
X = df_inputs.iloc[:, :4]
X_pca = pca.fit_transform(X)
df_pca = pd.DataFrame(pca.transform(X))
df_pca.columns = ['PC1', 'PC2']
df_pca = pd.concat([df_pca, df_output['variety']], axis=1)
return pca, df_pca
Create a function that filters out negative values from the dataframe. Only positive values in the dataframe are kept. If a value is negative, it is set to zero.
def extract_positive_value(prediction):
# f(prediction) = max(0, prediction)
prediction_positive = []
for p in prediction:
if p < 0:
p = 0
prediction_positive.append(p)
return pd.DataFrame({'Species': ['Setosa', 'Versicolor', 'Virginica'], 'Confidence': prediction_positive})
Define a Python function to display image according to Iris species.
def display_img(species):
# define the list of images
list_img = ['setosa.png', 'versicolor.png', 'virginica.png']
return Image.open(list_img[species])
All the functions defined above are called in the main Python file app.py to build the Streamlit app.
You can find this part of the code as well as the different functions defined previously on the GitHub repository.
Write the requirements.txt file for the application
The requirements.txt file will allow us to write all the modules needed to make our application work. This file will be useful when writing the Dockerfile.
pandas==1.4.4
numpy==1.23.2
torch==1.12.1
streamlit==1.12.2
scikit-learn==1.1.2
plotly==5.10.0
Pillow==9.2.0
seaborn==0.12.0
Write the Dockerfile for the application
Your Dockerfile should start with the the FROM instruction indicating the parent image to use. In our case we choose to start from the python:3.8 OVHcloud image:
python:3.8
Create the home directory and add your files to it:
WORKDIR /workspace
ADD . /workspace
Install the requirements.txt file which contains your needed Python modules using a pip install ... command:
RUN pip install -r requirements.txt
Define your default launching command to start the application:
CMD [ "streamlit", "run", "/workspace/app.py", "--server.address=0.0.0.0" ]
Give correct access rights to ovhcloud user (42420:42420):
RUN chown -R 42420:42420 /workspace
ENV HOME=/workspace
Build the Docker image from the Dockerfile
Launch the following command from the Dockerfile directory to build your application image:
docker build . -t streamlit-eda-iris:latest
The dot . argument indicates that your build context (place of the Dockerfile and other needed files) is the current directory.
The -t argument allows you to choose the identifier to give to your image. Usually image identifiers are composed of a name and a version tag <name>:<version>. For this example we chose streamlit-eda-iris:latest.
Please make sure that the docker image you will push in order to run containers using AI products respects the linux/AMD64 target architecture. You could, for instance, build your image using buildx as follows:
docker buildx build --platform linux/amd64 ...
Test it locally (optional)
Launch the following Docker command to launch your application locally on your computer:
docker run --rm -it -p 8501:8501 --user=42420:42420 streamlit-eda-iris:latest
The -p 8501:8501 argument indicates that you want to execute a port redirection from the port 8501 of your local machine into the port 8501 of the Docker container. The port 8501 is the default port used by Streamlit applications.
Don't forget the --user=42420:42420 argument if you want to simulate the exact same behaviour that will occur on AI Deploy apps. It executes the Docker container as the specific OVHcloud user (user 42420:42420).
Once started, your application should be available on http://localhost:8501.
Push the image into the shared registry
The shared registry of AI Deploy should only be used for testing purpose. Please consider attaching your own Docker registry. More information about this can be found here.
Find the address of your shared registry by launching this command:
ovhai registry list
Log in on the shared registry with your usual OpenStack credentials:
docker login -u <user> -p <password> <shared-registry-address>
Push the compiled image into the shared registry:
docker tag streamlit-eda-iris:latest <shared-registry-address>/streamlit-eda-iris:latest
docker push <shared-registry-address>/streamlit-eda-iris:latest
Launch the AI Deploy app
The following command starts a new app running your Streamlit application:
ovhai app run \
--default-http-port 8501 \
--cpu 12 \
<shared-registry-address>/streamlit-eda-iris:latest
--default-http-port 8501 indicates that the port to reach on the app URL is the 8501.
--cpu 12 indicates that we request 12 CPUs for that app.
Consider adding the --unsecure-http attribute if you want your application to be reachable without any authentication.
Go further
- You can imagine deploying an AI model with an other tool: Gradio. Refer to this tutorial.
- Another way to create an AI Deploy app is to use Flask! Here it is.
Feedback
Please send us your questions, feedback and suggestions to improve the service:
- On the OVHcloud Discord server
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