app.py

# type: ignore -- ignores linting import issues when using multiple virtual environments
import streamlit.components.v1 as components
import streamlit as st
import pandas as pd
import logging
from deeploy import Client


# reset Plotly theme after streamlit import
import plotly.io as pio


pio.templates.default = "plotly"

logging.basicConfig(level=logging.INFO)

st.set_page_config(layout="wide")

st.title("Your title")


def get_model_url():
    """Function to get Deeploy model URL and split it into workspace and deployment ID."""
    model_url = st.text_area(
        "Model URL (without the /explain endpoint, default is the demo deployment)",
        "https://api.app.deeploy.ml/workspaces/708b5808-27af-461a-8ee5-80add68384c7/deployments/9155091a-0abb-45b3-8b3b-24ac33fa556b/",
        height=125,
    )
    elems = model_url.split("/")
    try:
        workspace_id = elems[4]
        deployment_id = elems[6]
    except IndexError:
        workspace_id = ""
        deployment_id = ""
    return model_url, workspace_id, deployment_id


def ChangeButtonColour(widget_label, font_color, background_color="transparent"):
    """Function to change the color of a button (after it is defined)."""
    htmlstr = f"""
        <script>
            var elements = window.parent.document.querySelectorAll('button');
            for (var i = 0; i < elements.length; ++i) {{ 
                if (elements[i].innerText == '{widget_label}') {{ 
                    elements[i].style.color ='{font_color}';
                    elements[i].style.background = '{background_color}'
                }}
            }}
        </script>
        """
    components.html(f"{htmlstr}", height=0, width=0)

def predict_callback():
    with st.spinner("Loading prediction..."):
        try:
            print("Request body: ", request_body)
            # Call the explain endpoint as it also includes the prediction
            pred = client.predict(
                request_body=request_body, deployment_id=deployment_id
            )
        except Exception as e:
            logging.error(e)
            st.error(
                "Failed to get prediction."
                + "Check whether you are using the right model URL and token for predictions. "
                + "Contact Deeploy if the problem persists."
            )
            return
        st.session_state.pred = pred
    st.session_state.evaluation_submitted = False    


def submit_and_clear(evaluation: str):
    if evaluation == "yes":
        st.session_state.evaluation_input["result"] = 0 # Agree with the prediction
    else:
        # Disagree with the prediction
        st.session_state.evaluation_input["result"] = 1
        # In binary classification problems we can just flip the prediction
        desired_output = not predictions[0]
        st.session_state.evaluation_input["value"] = {"predictions": [desired_output]}
    try:
        # Call the explain endpoint as it also includes the prediction
        client.evaluate(
            deployment_id, request_log_id, prediction_log_id, st.session_state.evaluation_input
        )
        st.session_state.evaluation_submitted = True
        st.session_state.pred = None
    except Exception as e:
        logging.error(e)
        st.error(
            "Failed to submit feedback."
            + "Check whether you are using the right model URL and token for evaluations. "
            + "Contact Deeploy if the problem persists."
        )


# Define defaults for the session state
if "pred" not in st.session_state:
    st.session_state.pred = None

if "evaluation_submitted" not in st.session_state:
    st.session_state.evaluation_submitted = False

# Define sidebar for configuration of Deeploy connection
with st.sidebar:
    st.image("deeploy_logo_wide.png", width=250)

    # Ask for model URL and token
    host = st.text_input("Host (Changing is optional)", "app.deeploy.ml")
    model_url, workspace_id, deployment_id = get_model_url()
    deployment_token = st.text_input("Deeploy Model Token", "my-secret-token")
    if deployment_token == "my-secret-token":
        st.warning("Please enter Deeploy API token.")

    # In case you need to debug the workspace and deployment ID:
    # st.write("Values below are for debug only:")
    # st.write("Workspace ID: ", workspace_id)
    # st.write("Deployment ID: ", deployment_id)

    client_options = {
        "host": host,
        "deployment_token": deployment_token,
        "workspace_id": workspace_id,
    }
    client = Client(**client_options)

# For debugging the session state you can uncomment the following lines:
# with st.expander("Debug session state", expanded=False):
#     st.write(st.session_state)

# Input (for IRIS dataset)
with st.expander("Input values for prediction", expanded=True):
    st.write("Please input the values for the model.")
    col1, col2 = st.columns(2)
    with col1:
        sep_len = st.number_input("Sepal length", value=1.0, step=0.1, key="Sepal length")
        sep_wid = st.number_input("Sepal width", value=1.0, step=0.1, key="Sepal width")
    with col2:
        pet_len = st.number_input("Petal length", value=1.0, step=0.1, key="Petal length")
        pet_wid = st.number_input("Petal width", value=1.0, step=0.1, key="Petal width")

request_body = {
    "instances": [
        [
            sep_len,
            sep_wid,
            pet_len,
            pet_wid,
        ],
    ]
}

# Predict and explain
predict_button = st.button("Predict", on_click=predict_callback)
if st.session_state.pred is not None:
    st.write(st.session_state.pred)