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Added first version of Dashboard

andon.py

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+# AUTOGENERATED! DO NOT EDIT! File to edit: ../../nbs/book/EventTracker/11_Andon.ipynb.
+
+# %% auto 0
+__all__ = ['df', 'farms', 'selected_farms', 'filter_df', 'generate_discrete_colors', 'get_discrete_colormap', 'get_summary',
+           'get_event_metrics', 'get_algo_performance', 'get_pie', 'get_data', 'get_farms', 'kpi']
+
+# %% ../../nbs/book/EventTracker/11_Andon.ipynb 2
+import streamlit as st
+import pandas as pd
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+import plotly.express as px
+
+# %% ../../nbs/book/EventTracker/11_Andon.ipynb 6
+st.set_page_config(
+    page_title="Andon",
+    layout='wide'
+)
+
+# %% ../../nbs/book/EventTracker/11_Andon.ipynb 11
+def generate_discrete_colors(n):
+    colors = px.colors.qualitative.Plotly
+    return [colors[i % len(colors)] for i in range(n)]
+
+generate_discrete_colors(5)
+
+# %% ../../nbs/book/EventTracker/11_Andon.ipynb 12
+def get_discrete_colormap(classes):
+    colors = generate_discrete_colors(len(classes))
+    color_discrete_map = dict(zip(classes, colors))
+    return color_discrete_map
+
+# %% ../../nbs/book/EventTracker/11_Andon.ipynb 13
+def get_summary(df, col_a='Type of Alert', col_b='Ground Truth'):
+    cal_df = df[[col_a, col_b]].groupby([col_a, col_b]).value_counts().reset_index()
+
+    
+    color_discrete_map = get_discrete_colormap(df[col_b].dropna().unique())
+    fig = px.bar(cal_df, y=col_a, 
+                x='count', 
+                color=col_b, 
+                color_discrete_map=color_discrete_map, 
+                orientation='h', 
+                title=f'Summary: {col_a}|{col_b}')
+
+    fig.update_layout(
+        margin=dict(l=10, r=10, t=40, b=40),
+        yaxis=dict(automargin=True)
+    )
+
+    return fig
+
+# %% ../../nbs/book/EventTracker/11_Andon.ipynb 16
+def get_event_metrics(validated_df, 
+                      event_type_cols=['Heat', 'Missed Heat'], 
+                      event_type='Heat'):
+    val_event_df = validated_df[validated_df['Type of Alert'].isin(event_type_cols)]
+    a = val_event_df['Ground Truth'].value_counts()
+    
+    recall  = a.get('TRUE', 0)*100/(a.get('TRUE', 0)+a.get('MISSED', np.finfo(float).eps))
+    precision = a.get('TRUE', 0)*100/(a.get('TRUE', 0)+a.get('FALSE', np.finfo(float).eps))
+
+    return {
+        f'Validated_{event_type}_Recall': recall,
+        f'Validated_{event_type}_Precision': precision,
+    }
+
+# %% ../../nbs/book/EventTracker/11_Andon.ipynb 17
+def get_algo_performance(df):
+    confirmed_df = df[df['Ground Truth'].isin(['TRUE','FALSE', 'MISSED', 'EXCLUDE', 'OTHER'])]
+    validated_df = df[df['Ground Truth'].isin(['TRUE','FALSE', 'MISSED'])]
+    missed_df = df[df['Ground Truth'].isin(['MISSED'])]
+    heat_metrics = get_event_metrics(validated_df, 
+                        event_type_cols=['Heat', 'Missed Heat'], 
+                        event_type='Heat')
+    health_metrics = get_event_metrics(validated_df, 
+                        event_type_cols=['Health', 'Missed Health'], 
+                        event_type='Health')
+
+    data_coverage_metrics = {'Total':len(df),
+                             'Confirmed': len(confirmed_df),
+                             'Validated': len(validated_df),
+                             'Missed': len(missed_df),
+                             'Confirmed_Percentage': len(confirmed_df)*100/len(df),
+                             'Validated_Percentage': len(validated_df)*100/len(df),
+                             'Val_On_Confirm_Percentage': len(validated_df)*100/len(confirmed_df),
+                             'Missed_On_Val_Percentage': len(missed_df)*100/len(validated_df) }
+    performance_metrics = {**data_coverage_metrics,**heat_metrics, **health_metrics}
+    return performance_metrics
+
+# %% ../../nbs/book/EventTracker/11_Andon.ipynb 20
+def get_pie(df, colname):
+    df_dist = df[colname].value_counts().reset_index()
+    fig = px.pie(df_dist, values='count', 
+                names=colname, 
+                title=f'{colname}',
+                color = colname,
+                color_discrete_map = get_discrete_colormap(df[colname].dropna().unique())
+                )
+    return fig
+
+# %% ../../nbs/book/EventTracker/11_Andon.ipynb 35
+def get_data(url="https://docs.google.com/spreadsheets/d/1TbqmmSzXtY8DwolVo9rN7M1QdZxLG30nro2cqrvn2CA/export?format=csv&#gid=294763682"):
+    df = pd.read_csv(url).dropna(how='all')
+    df['Alert Date'] = df['Alert Date'].str.replace("//", "-")
+    df = df.dropna(subset=['Alert Date'])
+    df['Type of Alert'] = df['Type of Alert'].str.strip()
+    return df
+# colors
+
+# %% ../../nbs/book/EventTracker/11_Andon.ipynb 36
+def get_farms(df):
+    return df['Dairy Farm'].dropna().unique().tolist()
+
+# %% ../../nbs/book/EventTracker/11_Andon.ipynb 39
+def kpi(df, ncols = 7):
+    metrics = get_algo_performance(df)
+    dcols = st.columns(ncols)
+    for (i, (k, v)) in enumerate(metrics.items()):
+        with dcols[i%ncols]:
+            st.metric(k, np.round(v, 2))
+
+# %% ../../nbs/book/EventTracker/11_Andon.ipynb 40
+#|eval: false
+df = get_data()
+farms = get_farms(df)
+selected_farms = st.sidebar.multiselect('Select farms', farms, max_selections=len(farms))
+filter_df = df
+if selected_farms: 
+    str_farms = "|".join(selected_farms)
+    st.write(f"Selected_farms: {str_farms}")
+    filter_df = df[df['Dairy Farm'].isin(selected_farms)]
+else:
+    st.write("Selected_farms: All")
+# st.write(farms)
+kpi(filter_df,ncols=4)
+st.plotly_chart(get_pie(filter_df, colname = 'Type of Alert'))
+st.plotly_chart(get_pie(filter_df, colname = 'Ground Truth'))
+st.plotly_chart(get_pie(filter_df, colname = 'Dairy Farm'))
+st.plotly_chart(get_summary(filter_df, col_a='Type of Alert', col_b='Ground Truth'))
+st.plotly_chart(get_summary(filter_df, col_b='Type of Alert', col_a='Dairy Farm'))
+st.plotly_chart(get_summary(filter_df, col_b='Ground Truth', col_a='Dairy Farm'))
+# 

app.py

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+# AUTOGENERATED! DO NOT EDIT! File to edit: ../../nbs/book/EventTracker/10_App.ipynb.
+
+# %% auto 0
+__all__ = ['andon_page', 'master_page']
+
+# %% ../../nbs/book/EventTracker/10_App.ipynb 3
+import streamlit as st
+
+# %% ../../nbs/book/EventTracker/10_App.ipynb 5
+# def menu():
+#     st.sidebar.page_link(st.Page("andon.py", default=True), label="Andon")
+#     st.sidebar.page_link(st.Page("floating.py", url_path=".."), label="Floating")
+#     st.sidebar.page_link(st.Page("model.py", url_path=".."), label="Model")
+#     st.sidebar.page_link(st.Page("master.py", url_path=".."), label="Master")
+#     # st.sidebar.page_link("pages/2_mapping_demo.py", label="Mapping Demo")
+#     # st.sidebar.page_link("pages/3_dataframe_demo.py", label="Dataframe Demo")
+
+
+andon_page = st.Page("andon.py", default=True, title="Andon")
+master_page = st.Page("master.py", title="Master")
+
+
+
+# %% ../../nbs/book/EventTracker/10_App.ipynb 6
+#| eval: false
+from fastcore.basics import in_notebook
+if not in_notebook():
+    pg = st.navigation([andon_page, master_page])
+    pg.run()

master.py

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+# AUTOGENERATED! DO NOT EDIT! File to edit: ../../nbs/book/EventTracker/12_MasterTable.ipynb.
+
+# %% auto 0
+__all__ = ['df', 'get_data']
+
+# %% ../../nbs/book/EventTracker/12_MasterTable.ipynb 2
+import streamlit as st
+import pandas as pd
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+
+
+# %% ../../nbs/book/EventTracker/12_MasterTable.ipynb 6
+st.set_page_config(
+    page_title="Master",
+    page_icon="👋",
+    layout='wide'
+)
+
+# %% ../../nbs/book/EventTracker/12_MasterTable.ipynb 7
+# st.sidebar.success("Select a demo above.")
+
+# %% ../../nbs/book/EventTracker/12_MasterTable.ipynb 9
+def get_data(url="https://docs.google.com/spreadsheets/d/1TbqmmSzXtY8DwolVo9rN7M1QdZxLG30nro2cqrvn2CA/export?format=csv&#gid=294763682"):
+    df = pd.read_csv(url).dropna(how='all')
+    df['Alert Date'] = df['Alert Date'].str.replace("//", "-")
+    df = df.dropna(subset=['Alert Date'])
+    df['Type of Alert'] = df['Type of Alert'].str.strip()
+    return df
+
+# %% ../../nbs/book/EventTracker/12_MasterTable.ipynb 10
+#|eval: false
+df = get_data()
+st.write("# Master Table")
+
+st.table(data=df)

requirements.txt

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+fastcore 
+pandas 
+matplotlib 
+plotly
+seaborn
+openpyxl