squaredev-io · momegas · Feb 23, 2023 · Feb 8, 2023 · Feb 10, 2023 · Feb 11, 2023
diff --git a/.streamlit/config.toml b/.streamlit/config.toml
@@ -0,0 +1,6 @@
+[theme]
+base="dark"
+primaryColor="#21babe"
+backgroundColor="#1e2025"
+secondaryBackgroundColor="#252a33"
+
diff --git a/whitebox/.streamlit/config.toml b/whitebox/.streamlit/config.toml
@@ -0,0 +1,6 @@
+[theme]
+base="dark"
+primaryColor="#21babe"
+backgroundColor="#1e2025"
+secondaryBackgroundColor="#252a33"
+
diff --git a/whitebox/sdk/whitebox.py b/whitebox/sdk/whitebox.py
@@ -1,8 +1,8 @@
 from enum import Enum
 import numpy as np
 import pandas as pd
-from whitebox.schemas.model import ModelCreateDto, ModelType
-from typing import Dict, Optional
+from whitebox.schemas.model import ModelCreateDto, ModelType, ModelUpdateDto
+from typing import Dict, Optional, Union
 import requests
 import logging
 from fastapi import status
@@ -75,6 +75,35 @@ def get_model(self, model_id: str) -> dict:
 
         return result.json()
 
+    def get_models(self) -> Union[dict, None]:
+        """
+        Returns all the models. If no models exist, returns None.
+        """
+        result = requests.get(
+            url=f"{self.host}/{self.api_version}/models",
+            headers={"api-key": self.api_key},
+        )
+        if result.status_code == status.HTTP_404_NOT_FOUND:
+            return None
+
+        return result.json()
+
+    def update_model(self, model_id: str, body: ModelUpdateDto):
+        """
+        Updates a model by its id. If any error occurs, returns False.
+        """
+        result = requests.put(
+            url=f"{self.host}/{self.api_version}/models/{model_id}",
+            json=body,
+            headers={"api-key": self.api_key},
+        )
+        logger.info(result.json())
+
+        if result.status_code == status.HTTP_200_OK:
+            return True
+
+        return False
+
     def delete_model(self, model_id: str):
         """
         Deletes a model by its id. If any error occurs, returns False.
@@ -169,6 +198,20 @@ def log_inferences(
 
         return False
 
+    def get_inferences(self, model_id: str):
+        """
+        Given a specific model id, this endpoint fetches all the inferences.
+        If some of the required data isn't found, returns None.
+        """
+        result = requests.get(
+            url=f"{self.host}/{self.api_version}/inference-rows?model_id={model_id}",
+            headers={"api-key": self.api_key},
+        )
+        if result.status_code == status.HTTP_404_NOT_FOUND:
+            return None
+
+        return result.json()
+
     def get_xai_row(self, inference_row_id: str):
         """
         Given a specific inference row id, this endpoint produces an explainability report for this inference.
@@ -191,8 +234,8 @@ def create_model_monitor(
         metric: MonitorMetrics,
         severity: AlertSeverity,
         email: str,
-        feature: Optional[str],
         lower_threshold: Optional[float],
+        feature: Optional[Union[str, None]] = None,
     ) -> dict:
         """
         Creates a monitor for a model.
@@ -218,6 +261,18 @@ def create_model_monitor(
         logger.info(result.json())
         return result.json()
 
+    def get_monitors(self, model_id: str) -> dict:
+        """
+        Returns all monitors for a model.
+        """
+        result = requests.get(
+            url=f"{self.host}/{self.api_version}/model-monitors?modelId={model_id}",
+            headers={"api-key": self.api_key},
+        )
+
+        logger.info(result.json())
+        return result.json()
+
     def get_alerts(self, model_id: str = "") -> dict:
         """
         Returns all alerts for a model.

diff --git a/whitebox/streamlit/app.py b/whitebox/streamlit/app.py
@@ -0,0 +1,95 @@
+import numpy as np
+import pandas as pd
+import streamlit as st
+from typing import Dict, Union
+
+from tabs.drifting import *
+from tabs.sidebar import *
+from tabs.overview import *
+from tabs.performance import *
+from tabs.inferences import *
+from tabs.monitors import *
+from tabs.alerts import *
+from cards import *
+from utils.transformation import get_model_from_name
+
+from whitebox import Whitebox
+
+# wb = Whitebox(
+#     host="http://127.0.0.1:8000",
+#     api_key="c37b902f5af13c43af33652770d7c51008f5e18b0cf4cf9cc870ab93bea98f3f",
+# )
+st.set_option("deprecation.showPyplotGlobalUse", False)
+
+# ----------------------------------------------
+def format_evaluation_metrics_binary(
+    accuracy: float,
+    precision: float,
+    recall: float,
+    f1: float,
+    tn: int,
+    fp: int,
+    fn: int,
+    tp: int,
+) -> Dict[str, Union[int, float]]:
+    formated_metrics_for_binary = {
+        "accuracy": accuracy,
+        "precision": precision,
+        "recall": recall,
+        "f1": f1,
+        "true_negative": tn,
+        "false_positive": fp,
+        "false_negative": fn,
+        "true_positive": tp,
+    }
+
+    return formated_metrics_for_binary
+
+
+evaluation_metrics_binary = format_evaluation_metrics_binary(
+    0.64, 0.5, 0.11, 0.72, 1200, 600, 840, 260
+)
+evaluation_metrics_binary_df = pd.DataFrame(evaluation_metrics_binary, index=[0])
+base_evaluation_metrics_binary_df = evaluation_metrics_binary_df[
+    ["accuracy", "precision", "recall", "f1"]
+]
+# Conf matrix
+first_part = [
+    evaluation_metrics_binary["true_positive"],
+    evaluation_metrics_binary["false_positive"],
+]
+second_part = [
+    evaluation_metrics_binary["false_negative"],
+    evaluation_metrics_binary["true_negative"],
+]
+cm = np.array([first_part, second_part])
+
+# -----------------------------------
+overview, performance, drifting, inferences, monitors, alerts = st.tabs(
+    ["Overview", "Performance", "Drifting", "Inferences", "Monitors", "Alerts"]
+)
+model_option, models_list, checkbox, wb = create_sidebar()
+
+if checkbox:
+    model = get_model_from_name(models_list, model_option)
+    pred_column = model["prediction"]
+    model_id = model["id"]
+    model_type = model["type"]
+    # TODO: Need to connect this one with the db.
+    with overview:
+        create_overview_tab(model, cm, base_evaluation_metrics_binary_df)
+
+    with performance:
+        create_performance_tab(wb, model_id, model_type)
+
+    with drifting:
+        create_drift_tab(wb, model_id)
+
+    with inferences:
+        create_inferences_tab(wb, model_id, pred_column)
+
+    with monitors:
+        create_monitors_tab(wb, model_id, model_type)
+
+    with alerts:
+        create_alerts_tab(wb, model_id)
diff --git a/whitebox/streamlit/cards.py b/whitebox/streamlit/cards.py
@@ -0,0 +1,30 @@
+import streamlit as st
+
+
+def card1(text1, text2):
+    st.markdown(
+        f"""
+    <div class="card text-white bg-dark mb-3" style="max-width: 280rem;">
+        <div class="card-body">
+            <p class="card-text"><b>{text1}</b></p>
+            <p class="card-text">{text2}</p>
+        </div>
+    </div>
+    """,
+        unsafe_allow_html=True,
+    )
+
+
+def card(text1, text2, text3):
+    st.markdown(
+        f"""
+    <div class="card text-white bg-dark mb-3" style="width: 44rem;">
+        <div class="card-body">
+            <h5 class="card-title"><b>{text1}</b></h5>
+            <h6 class="card-subtitle mb-2 text-muted">type: {text2}</h6>
+            <p class="card-text">{text3}</p>
+        </div>
+    </div>
+    """,
+        unsafe_allow_html=True,
+    )
diff --git a/whitebox/streamlit/classification_test_data copy.csv b/whitebox/streamlit/classification_test_data copy.csv
@@ -0,0 +1,11 @@
+y_testing_binary,y_prediction_binary,y_testing_multi,y_prediction_multi
+0,0,0,1
+0,0,2,2
+0,1,0,0
+1,1,1,1
+0,0,2,0
+1,0,1,1
+1,0,1,0
+1,1,2,2
+0,1,0,1
+1,1,1,1
diff --git a/whitebox/streamlit/classification_test_data.csv b/whitebox/streamlit/classification_test_data.csv
@@ -0,0 +1,11 @@
+y_testing_binary,y_prediction_binary,y_testing_multi,y_prediction_multi
+0,0,0,1
+0,0,2,2
+0,1,0,0
+1,1,1,1
+0,0,2,0
+1,0,1,1
+1,0,1,0
+1,1,2,2
+0,1,0,1
+1,1,1,1
diff --git a/whitebox/streamlit/config/config_readme.toml b/whitebox/streamlit/config/config_readme.toml
@@ -0,0 +1,51 @@
+[app]
+app_intro = """
+This streamlit app allows you to visualise and monitor the results and all related analytics produced by Whitebox.
+
+All you have to do is to select your desired model which is stored in your database and then navigate through the different tabs.
+"""
+[tooltips]
+model_option = """
+These are the models that are saved in your connected database.
+"""
+overview_performance = """
+These are the model's performance metrics after it was trained with the training data. 
+"""
+monitor_name = """
+Add your preference name for the new monitor. 
+"""
+monitor_use_case = """
+Add your preference type of monitor. 
+"""
+stat_thresh_monitor = """
+Enter a value between 0 and 1 with max two decimal points, e.g. 0.25. 
+"""
+alert_trig_monitor = """
+Alert will trigger if below the lower threshold or above the upper threshold. 
+"""
+alert_severity_monitor = """
+What alert severity should be associated with the notifications being sent?
+"""
+notifications_monitor = """
+Notifications will be sent via email. Please provide your email below:
+"""
+host = """
+Your host and port combined in a url.
+"""
+api_key = """
+Your api key as was created from the initialisation of Whitebox.
+"""
+model_name = """
+Your desired model name.
+"""
+model_description = """
+Your desired model description.
+"""
+model_type = """
+Your desired model type.
+"""
+target_column = """
+Your desired target column as depicted in your data.
+"""
+[links]
+repo = "https://github.com/squaredev-io/whitebox"
diff --git a/whitebox/streamlit/mock/alerts.json b/whitebox/streamlit/mock/alerts.json
@@ -0,0 +1,20 @@
+[
+    {
+        "id": 44,
+        "model_monitor_id": 1,
+        "timestamp": "2023-03-14",
+        "description": "fell below the threshold"
+    },
+    {
+        "id": 45,
+        "model_monitor_id": 1,
+        "timestamp": "2023-03-16",
+        "description": "fell below the threshold"
+    },
+    {
+        "id": 46,
+        "model_monitor_id": 3,
+        "timestamp": "2023-03-16",
+        "description": "fell increasinlgy below the threshold"
+    }
+]