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# 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)
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