Add prediction app to Iris deployment

.gitignore

@@ -0,0 +1,2 @@
+__pycache__
+.venv

README.md

@@ -9,4 +9,11 @@ app_file: app.py
 pinned: false
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Deeploy Template
+This space holds the template for a Streamlit app using a model deployed with Deeploy.
+
+## Usage
+To use this template, duplicate this space and adjust the `app.py` file to make your own Streamlit app.
+
+## Deployment
+To deploy your app, simply push your changes to your Hugging Face repository. The app will be automatically deployed on Hugging Face spaces.

app.py

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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
+from shap import TreeExplainer
+
+
+# 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")
+
+st.markdown(
+    """
+    <style>
+        section[data-testid="stSidebar"] {
+            width: 300px !important;
+        }
+    </style>
+    """,
+    unsafe_allow_html=True,
+) # Set the side bar width to fit the Deeploy logo
+
+
+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():
+    with st.spinner("Loading prediction and explanation..."):
+        try:
+            # Call the explain endpoint as it also includes the prediction
+            exp = 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.exp = exp
+    st.session_state.evaluation_submitted = False    
+    hide_expander()
+
+def hide_expander():
+    st.session_state.expander_toggle = False
+
+def show_expander():
+    st.session_state.expander_toggle = True
+
+def submit_and_clear(evaluation: str):
+    if evaluation == "yes":
+        st.session_state.evaluation_input["result"] = 0 # Agree with the prediction
+    else:
+        desired_output = not predictions[0]
+        st.session_state.evaluation_input["result"] = 1
+        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.exp = None
+        show_expander()
+    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 "expander_toggle" not in st.session_state:
+    st.session_state.expander_toggle = True
+
+if "exp" not in st.session_state:
+    st.session_state.exp = 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=st.session_state.expander_toggle):
+    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)
+if st.session_state.exp is not None:
+    st.write(st.session_state.exp)
+    # predictions = st.session_state.exp["predictions"]
+    # request_log_id = exp["requestLogId"]
+    # prediction_log_id = exp["predictionLogIds"][0]
+
+    # # exp_df = pd.DataFrame(
+    # #     [exp["explanations"][0]["shap_values"]], columns=exp["featureLabels"]
+    # # )
+    # st.write("Predictions:", predictions)
+
+    # # Evaluation
+    # if st.session_state.evaluation_submitted is False:
+    #     evaluation = st.radio("Do you agree with the prediction?", ("yes", "no"))
+    #     if evaluation == "no":
+    #         desired_output = # TODO
+    #         st.session_state.evaluation_input = {
+    #             "result": 1,
+    #             "value": {"predictions": [desired_output]},
+    #         }
+    #     else:
+    #         st.session_state.evaluation_input = {"result": 0}
+
+    #     submit_button = st.button("Submit evaluation", on_click=submit_and_clear, args=(evaluation,))
+    # else:
+    #     st.success("Evaluation submitted successfully.")
+    
+
+

requirements.txt

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+deeploy==1.2.1
+streamlit==1.29.0
+plotly==5.18.0