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andon.py

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+# AUTOGENERATED! DO NOT EDIT! File to edit: ../../nbs/book/LabellingTracker/11_Andon.ipynb.
+
+# %% auto 0
+__all__ = ['df', 'get_data', 'get_df_extra', 'kpi', 'get_floating_count', 'get_model_count']
+
+# %% ../../nbs/book/LabellingTracker/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/LabellingTracker/11_Andon.ipynb 6
+st.set_page_config(
+    page_title="Andon",
+    layout='wide'
+)
+
+# %% ../../nbs/book/LabellingTracker/11_Andon.ipynb 9
+def get_data(fname="Labelling_Tracking_v28.06.24.xlsx", 
+             sheet_name='MASTER', 
+             date_cols=['Recording_Date', 'Assignment_Date', 'Video_Reception_Date','Target_Date', 'Labelling_Received_Date', 'Verification_Date', 'Completion/Rejection_Date']):
+    df = pd.read_excel(fname, sheet_name=sheet_name, 
+                    parse_dates=date_cols)
+
+    df['Assigned']=df['Assigned'].str.split(".").str.join(" ").str.upper()
+    return df
+# colors
+
+# %% ../../nbs/book/LabellingTracker/11_Andon.ipynb 10
+def get_df_extra(fname):
+    df = get_data(fname=fname, 
+                  sheet_name='MASTER', 
+                  date_cols=['Recording_Date', 'Assignment_Date', 'Video_Reception_Date','Target_Date', 'Labelling_Received_Date', 'Verification_Date', 'Completion/Rejection_Date'])
+
+
+    col_order= pd.read_excel(fname, sheet_name='STATUS')
+    col_order = col_order['STATUS STATES'].tolist()
+
+    colors = dict(zip(col_order, sns.color_palette("Set2", len(col_order))))
+    # col_order
+
+    colors2 = dict(zip(col_order, ['blue', 'red', 'green', 'yellow', 'cyan', 'violet', 'pink', 'magenta']))
+    return df, col_order, colors, colors2
+
+# %% ../../nbs/book/LabellingTracker/11_Andon.ipynb 13
+def kpi(df):
+    cattle_days = df.loc[df['TAG']=='FLOATING', ['CattleFolder/Frame', 'SubFolder']].groupby('CattleFolder/Frame').count().sum().values[0]
+    model_days = df.loc[df['TAG']=='MODEL', ['CattleFolder/Frame', 'SubFolder']].groupby('CattleFolder/Frame').count().sum().values[0]
+    cattle_floating = df.loc[df['TAG']=='FLOATING', ['CattleFolder/Frame']].nunique().values[0]
+    accounts_floating = df.loc[df['TAG']=='FLOATING', 'AccountNumber'].nunique()
+    frames_model = df.loc[df['TAG']=='MODEL', ['AccountNumber', 'CattleFolder/Frame']].groupby('AccountNumber').nunique().sum().values[0]
+    accounts_model = df.loc[df['TAG']=='MODEL', 'AccountNumber'].nunique()
+    count_user_floating = len(set(df.loc[df['TAG']=='FLOATING', 'Assigned'].dropna().str.split('/').sum()))
+    count_model_floating = len(set(df.loc[df['TAG']=='MODEL', 'Assigned'].dropna().str.split('/').sum()))
+    col1, col2, col3, col4 = st.columns(4)
+    col1.metric('Floating Days/Cattle', f'{cattle_days}/{cattle_floating}[{accounts_floating}]')
+    col2.metric('Labellers Floating', count_user_floating)
+    col3.metric('Model Days/Frame',f'{model_days}/{frames_model}[{accounts_model}]')
+    col4.metric('Labellers Model', count_model_floating)
+
+# %% ../../nbs/book/LabellingTracker/11_Andon.ipynb 14
+def get_floating_count(df, col_order, colors):
+    fig, ax = plt.subplots()
+    # df.loc[df['TAG']=='FLOATING', ['AccountNumber', 'AccountName','CattleFolder/Frame']].groupby(['AccountNumber', 'AccountName']).nunique().rename(columns={'CattleFolder/Frame':'Count'}).sort_values(by='Count').plot(kind='barh', ax=ax)
+    data = df.loc[df['TAG']=='FLOATING', ['AccountNumber', 'AccountName', 'Status', 'CattleFolder/Frame']].drop_duplicates()
+    o = data.groupby(['AccountNumber','AccountName', 'Status'])['CattleFolder/Frame'].count().unstack('Status').fillna(0).sort_values(by='AccountName')
+    sel_cols = [x for x in col_order if x in o.columns]
+    sel_colors = {k:colors[k] for k in sel_cols}
+    o[sel_cols].plot(kind='barh', stacked=True, ax=ax, color=sel_colors)
+    # ax.bar_label(ax.containers[-1])
+    return fig
+
+# %% ../../nbs/book/LabellingTracker/11_Andon.ipynb 16
+def get_model_count(df,col_order, colors):
+    fig, ax = plt.subplots()
+    # df.loc[df['TAG']=='MODEL', ['AccountNumber', 'AccountName','CattleFolder/Frame']].groupby(['AccountNumber', 'AccountName']).count().rename(columns={'CattleFolder/Frame':'Count'}).sort_values(by='Count').plot(kind='barh', ax=ax)
+    data = df.loc[df['TAG']=='MODEL', ['AccountNumber', 'AccountName', 'Status', 'CattleFolder/Frame', 'SubFolder']].drop_duplicates()
+    o = data.groupby(['AccountNumber','AccountName', 'Status'])['CattleFolder/Frame'].count().unstack('Status').fillna(0).sort_values(by='AccountName')
+    sel_cols = [x for x in col_order if x in o.columns]
+    sel_colors = {k:colors[k] for k in sel_cols}
+    o[sel_cols].plot(kind='barh', stacked=True, ax=ax, color=colors)
+    # ax.bar_label(ax.containers[0])
+    return fig
+
+# %% ../../nbs/book/LabellingTracker/11_Andon.ipynb 18
+df = None
+st.write("# Labelling Andon Board")
+if 'processed_df' not in st.session_state:
+    uploaded_file = st.file_uploader("Choose a file", type = 'xlsx')
+    if uploaded_file is not None:
+        df, col_order, colors, colors2=get_df_extra(uploaded_file)
+        st.session_state['processed_df'] = df
+        st.session_state['col_order'] = col_order
+        st.session_state['colors'] = colors
+        st.session_state['colors2'] = colors2
+else:
+    df = st.session_state['processed_df'] 
+    col_order = st.session_state['col_order'] 
+    colors = st.session_state['colors']
+    colors2= st.session_state['colors2']
+
+if df is not None:
+    kpi(df)
+
+    st.write("## Total Available")
+    col1, col2 = st.columns(2)
+    with col1:
+        st.markdown("Number of Cattles per Floating Account")
+        fig = get_floating_count(df, col_order, colors)
+        st.pyplot(fig)
+
+    with col2:
+        st.markdown("Frame-Dates per Model Account")
+        fig2 = get_model_count(df, col_order, colors)
+        st.pyplot(fig2)

app.py

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+# AUTOGENERATED! DO NOT EDIT! File to edit: ../../nbs/book/LabellingTracker/10_App.ipynb.
+
+# %% auto 0
+__all__ = ['andon_page', 'floating_page', 'model_page', 'master_page']
+
+# %% ../../nbs/book/LabellingTracker/10_App.ipynb 3
+import streamlit as st
+
+# %% ../../nbs/book/LabellingTracker/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")
+floating_page = st.Page("floating.py", title="Floating")
+model_page = st.Page("model.py", title="Model")
+master_page = st.Page("master.py", title="Master")
+
+from fastcore.basics import in_notebook
+if not in_notebook():
+    pg = st.navigation([andon_page, floating_page, model_page, master_page])
+    pg.run()

floating.py

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+# AUTOGENERATED! DO NOT EDIT! File to edit: ../../nbs/book/LabellingTracker/13_Floating.ipynb.
+
+# %% auto 0
+__all__ = ['df', 'get_floating_grp_data', 'get_floating_summary', 'get_floating_hist', 'get_step_df', 'get_gantt']
+
+# %% ../../nbs/book/LabellingTracker/13_Floating.ipynb 2
+import streamlit as st
+import pandas as pd
+
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import plotly.express as px
+import seaborn as sns
+
+# %% ../../nbs/book/LabellingTracker/13_Floating.ipynb 6
+st.set_page_config(
+    page_title="Floating",
+    page_icon="👋",
+    layout='wide'
+)
+
+# %% ../../nbs/book/LabellingTracker/13_Floating.ipynb 8
+# st.sidebar.success("Select a demo above.")
+
+# %% ../../nbs/book/LabellingTracker/13_Floating.ipynb 11
+def get_floating_grp_data(df):
+    grp_df = df.loc[df['TAG']=='FLOATING', ['Trial_Num', 'AccountNumber', 'AccountName', 'CattleFolder/Frame', 'TAG', 'Assigned',
+                                            'Recording_Date', 'Video_Reception_Date', 'Assignment_Date', 
+                                            'Target_Date', 'Labelling_Received_Date','Verification_Date', 'Completion/Rejection_Date']].groupby(['Trial_Num',
+                                                                                                                                                 'AccountNumber',
+                                                                                                                                                 'AccountName', 
+                                                                                                                                                 'TAG', 
+                                                                                                                                                 'CattleFolder/Frame', 
+                                                                                                                                                 ],
+                                                                                            as_index=False).agg({'Recording_Date':['min','max'], 
+                                                                                                                    'Video_Reception_Date':'max',
+                                                                                                                    'Assignment_Date':'min',
+                                                                                                                    'Target_Date':'max',
+                                                                                                                    'Labelling_Received_Date':'max',
+                                                                                                                    'Verification_Date':'max',
+                                                                                                                    'Completion/Rejection_Date':'max',
+                                                                                                                    'Assigned': 'sum'})
+
+    flat_cols = ["_".join(i).rstrip('_') for i in grp_df.columns];# flat_cols
+    grp_df.columns = flat_cols
+    grp_df['Recording'] = (grp_df['Recording_Date_max'] - grp_df['Recording_Date_min']).dt.days+1
+    grp_df['Waiting4Video'] = (grp_df['Video_Reception_Date_max'] - grp_df['Recording_Date_max']).dt.days
+    grp_df['Waiting4Assignment'] = (grp_df['Assignment_Date_min'] - grp_df['Video_Reception_Date_max']).dt.days
+    grp_df['Labelling'] = (grp_df['Target_Date_max'] - grp_df['Assignment_Date_min']).dt.days
+    grp_df['Waiting4Labels'] = (grp_df['Labelling_Received_Date_max'] - grp_df['Target_Date_max']).dt.days
+    grp_df['Waiting4Verification'] = (grp_df['Verification_Date_max'] - grp_df['Labelling_Received_Date_max']).dt.days
+    grp_df['Waiting4Completion'] = (grp_df['Completion/Rejection_Date_max'] - grp_df['Verification_Date_max']).dt.days
+    grp_df['Labelling_Duration'] = grp_df['Labelling'] + grp_df['Waiting4Labels'].fillna(0)
+    return grp_df
+
+# %% ../../nbs/book/LabellingTracker/13_Floating.ipynb 13
+def get_floating_summary(df):
+    fig, ax = plt.subplots()
+    grp_df = get_floating_grp_data(df)
+    states = ['Recording','Waiting4Video', 'Waiting4Assignment', 'Labelling', 'Waiting4Labels', 'Waiting4Verification','Waiting4Completion']
+    colors = dict(zip(states, ['blue', 'red', 'green', 'yellow', 'cyan', 'violet', 'pink']))
+    grp_df[['AccountNumber','Assigned_sum', 'AccountName', 'CattleFolder/Frame','Recording','Waiting4Video', 'Waiting4Assignment', 'Labelling', 'Waiting4Labels', 'Waiting4Verification','Waiting4Completion']].set_index(['AccountNumber', 'AccountName', 'CattleFolder/Frame']).plot(kind='barh', stacked=True, ax=ax, color=colors);
+    return fig
+
+# %% ../../nbs/book/LabellingTracker/13_Floating.ipynb 14
+def get_floating_hist(df, col):
+    grp_df = get_floating_grp_data(df)
+    fig, ax = plt.subplots()
+    grp_df[col].plot(kind='hist', ax=ax, legend=True)
+    avg = grp_df[col].mean()
+    count = grp_df[col].count()
+    ax.axvline(avg, color='red', label=f'mean={avg}')
+    ax.set_title(f'{col}[ mean={avg:.2f}, count={count:.2f} ]')
+    return fig
+# get_floating_hist(df, 'Recording_Duration')
+
+# %% ../../nbs/book/LabellingTracker/13_Floating.ipynb 15
+def get_step_df(grp_df, start_step, end_step, step_name):
+    df_e = grp_df[['AccountNumber', 'AccountName','Assigned_sum', 'CattleFolder/Frame']].copy()
+    df_e['Start'] = grp_df[start_step]
+    df_e['End'] = grp_df[end_step]
+    df_e['Step'] = step_name
+    return df_e
+
+
+# %% ../../nbs/book/LabellingTracker/13_Floating.ipynb 17
+def get_gantt(df):
+    steps = [
+            {'start_step': 'Recording_Date_min', 'end_step' :'Recording_Date_max', 'step_name' : 'Recording'},
+            {'start_step': 'Recording_Date_max', 'end_step' :'Video_Reception_Date_max', 'step_name' : 'Waiting4Video'},
+            {'start_step': 'Video_Reception_Date_max', 'end_step' :'Assignment_Date_min', 'step_name' : 'Waiting4Assignment'},
+            {'start_step': 'Assignment_Date_min', 'end_step' :'Target_Date_max', 'step_name' : 'Labelling'},
+            {'start_step': 'Target_Date_max', 'end_step' :'Labelling_Received_Date_max', 'step_name' : 'Waiting4Labels'},
+            {'start_step': 'Labelling_Received_Date_max', 'end_step' :'Verification_Date_max', 'step_name' : 'Waiting4Verification'},
+            {'start_step': 'Verification_Date_max', 'end_step' :'Completion/Rejection_Date_max', 'step_name' : 'Waiting4Completion'},
+        ]
+    grp_df = get_floating_grp_data(df)
+    df_concat = pd.concat(get_step_df(grp_df,  start_step=step['start_step'], end_step=step['end_step'], step_name=step['step_name']) for step in steps)
+    df_concat['label'] = df['AccountName'] +"_"+ df['CattleFolder/Frame']
+    df_concat.loc[df_concat['Step']=='Recording', 'Start']  = df_concat.loc[df_concat['Step']=='Recording', 'Start'] - pd.Timedelta(days=1)
+    # # df_concat
+
+    states = [s['step_name'] for s in steps]; # states
+    colors = dict(zip(states, ['blue', 'red', 'green', 'yellow', 'cyan', 'violet', 'pink']))
+
+    fig = px.timeline(data_frame=df_concat, x_start='Start', x_end='End', y='CattleFolder/Frame', color='Step', color_discrete_map=colors, hover_data=['Assigned_sum', 'AccountName', 'AccountNumber'])
+    # fig.update_layout(legend=dict( 
+    #     orientation="h", 
+    
+    # )) 
+    return fig
+
+# %% ../../nbs/book/LabellingTracker/13_Floating.ipynb 21
+df = None
+st.write("# Floating Details")
+if 'processed_df' not in st.session_state:
+    st.write("Please go to andon page and upload data")
+else:
+    df = st.session_state['processed_df'] 
+    col_order = st.session_state['col_order'] 
+    colors = st.session_state['colors']
+    colors2= st.session_state['colors2']
+    st.markdown("## Summary Floating Durations")
+    with st.container(border=True):
+        st.pyplot(get_floating_summary(df))
+        ncols = 3
+        dcols = st.columns(ncols)
+        for i, col in enumerate(['Recording','Waiting4Video', 'Waiting4Assignment', 'Labelling_Duration', 'Waiting4Verification', 'Waiting4Completion']):
+            with dcols[i%ncols]:
+                st.pyplot(get_floating_hist(df, col), use_container_width=True)
+
+    st.markdown("## Timeline")
+    with st.container(border=True):
+        st.plotly_chart(get_gantt(df), theme="streamlit", use_container_width=True)

master.py

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+# AUTOGENERATED! DO NOT EDIT! File to edit: ../../nbs/book/LabellingTracker/12_MasterTable.ipynb.
+
+# %% auto 0
+__all__ = ['df']
+
+# %% ../../nbs/book/LabellingTracker/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/LabellingTracker/12_MasterTable.ipynb 6
+st.set_page_config(
+    page_title="Master",
+    page_icon="👋",
+    layout='wide'
+)
+
+# %% ../../nbs/book/LabellingTracker/12_MasterTable.ipynb 8
+# st.sidebar.success("Select a demo above.")
+
+# %% ../../nbs/book/LabellingTracker/12_MasterTable.ipynb 10
+df = None
+st.write("# Master Table")
+if 'processed_df' not in st.session_state:
+    st.write("Please go to andon page and upload data")
+else:
+    df = st.session_state['processed_df'] 
+    col_order = st.session_state['col_order'] 
+    colors = st.session_state['colors']
+    colors2= st.session_state['colors2']
+    st.table(data=df)

model.py

@@ -0,0 +1,140 @@
+# AUTOGENERATED! DO NOT EDIT! File to edit: ../../nbs/book/LabellingTracker/14_Model.ipynb.
+
+# %% auto 0
+__all__ = ['df', 'get_model_grp_data', 'get_model_summary', 'get_model_hist', 'get_step_df', 'get_gantt']
+
+# %% ../../nbs/book/LabellingTracker/14_Model.ipynb 2
+import streamlit as st
+import pandas as pd
+
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import plotly.express as px
+import seaborn as sns
+
+# %% ../../nbs/book/LabellingTracker/14_Model.ipynb 6
+st.set_page_config(
+    page_title="Model",
+    page_icon="👋",
+    layout='wide'
+)
+
+# %% ../../nbs/book/LabellingTracker/14_Model.ipynb 8
+# st.sidebar.success("Select a demo above.")
+
+# %% ../../nbs/book/LabellingTracker/14_Model.ipynb 11
+def get_model_grp_data(df):
+    grp_df = df.loc[df['TAG']=='MODEL', ['Trial_Num', 'AccountNumber', 'AccountName', 'CattleFolder/Frame','SubFolder', 'TAG', 'Assigned',
+                                            'Recording_Date', 'Video_Reception_Date', 'Assignment_Date', 
+                                            'Target_Date', 'Labelling_Received_Date','Verification_Date', 'Completion/Rejection_Date']].groupby(['Trial_Num',
+                                                                                                                                                 'AccountNumber',
+                                                                                                                                                 'AccountName', 
+                                                                                                                                                 'TAG', 
+                                                                                                                                                 'CattleFolder/Frame', 
+                                                                                                                                                 'SubFolder'
+                                                                                                                                                 ],
+                                                                                            as_index=False).agg({'Recording_Date':['min','max'], 
+                                                                                                                    'Video_Reception_Date':'max',
+                                                                                                                    'Assignment_Date':'min',
+                                                                                                                    'Target_Date':'max',
+                                                                                                                    'Labelling_Received_Date':'max',
+                                                                                                                    'Verification_Date':'max',
+                                                                                                                    'Completion/Rejection_Date':'max',
+                                                                                                                    'Assigned': 'sum'})
+
+    flat_cols = ["_".join(i).rstrip('_') for i in grp_df.columns];# flat_cols
+    grp_df.columns = flat_cols
+    grp_df['Recording'] = (grp_df['Recording_Date_max'] - grp_df['Recording_Date_min']).dt.days+1
+    grp_df['Waiting4Video'] = (grp_df['Video_Reception_Date_max'] - grp_df['Recording_Date_max']).dt.days
+    grp_df['Waiting4Assignment'] = (grp_df['Assignment_Date_min'] - grp_df['Video_Reception_Date_max']).dt.days
+    grp_df['Labelling'] = (grp_df['Target_Date_max'] - grp_df['Assignment_Date_min']).dt.days
+    grp_df['Waiting4Labels'] = (grp_df['Labelling_Received_Date_max'] - grp_df['Target_Date_max']).dt.days
+    grp_df['Waiting4Verification'] = (grp_df['Verification_Date_max'] - grp_df['Labelling_Received_Date_max']).dt.days
+    grp_df['Waiting4Completion'] = (grp_df['Completion/Rejection_Date_max'] - grp_df['Verification_Date_max']).dt.days
+    grp_df['Labelling_Duration'] = grp_df['Labelling'] + grp_df['Waiting4Labels'].fillna(0)
+    return grp_df
+
+# %% ../../nbs/book/LabellingTracker/14_Model.ipynb 14
+def get_model_summary(df, kind='sum'):
+    fig, ax = plt.subplots()
+    grp_df = get_model_grp_data(df)
+    states = ['Recording','Waiting4Video', 'Waiting4Assignment', 'Labelling', 'Waiting4Labels', 'Waiting4Verification','Waiting4Completion']
+    colors = dict(zip(states, ['blue', 'red', 'green', 'yellow', 'cyan', 'violet', 'pink']))
+    grp_df[['AccountNumber','AccountName', 'CattleFolder/Frame',
+            'Recording','Waiting4Video', 'Waiting4Assignment', 'Labelling', 'Waiting4Labels', 'Waiting4Verification','Waiting4Completion']].groupby(['AccountNumber', 'AccountName', 'CattleFolder/Frame'], as_index=False).agg(kind).set_index(['AccountNumber', 'AccountName', 'CattleFolder/Frame']).plot(kind='barh', stacked=True, ax=ax, color=colors, legend=False);
+    fig.legend(loc='upper right')
+    return fig
+
+# %% ../../nbs/book/LabellingTracker/14_Model.ipynb 16
+def get_model_hist(df, col):
+    grp_df = get_model_grp_data(df)
+    fig, ax = plt.subplots()
+    grp_df[col].plot(kind='hist', ax=ax, legend=True)
+    avg = grp_df[col].mean()
+    count = grp_df[col].count()
+    ax.axvline(avg, color='red', label=f'mean={avg}')
+    ax.set_title(f'{col}[ mean={avg:.2f}, count={count:.2f} ]')
+    return fig
+# get_floating_hist(df, 'Recording_Duration')
+
+# %% ../../nbs/book/LabellingTracker/14_Model.ipynb 17
+def get_step_df(grp_df, start_step, end_step, step_name):
+    df_e = grp_df[['AccountNumber', 'AccountName','Assigned_sum', 'CattleFolder/Frame', 'SubFolder']].copy()
+    df_e['Start'] = grp_df[start_step]
+    df_e['End'] = grp_df[end_step]
+    df_e['Step'] = step_name
+    return df_e
+
+
+# %% ../../nbs/book/LabellingTracker/14_Model.ipynb 19
+def get_gantt(df):
+    steps = [
+            {'start_step': 'Recording_Date_min', 'end_step' :'Recording_Date_max', 'step_name' : 'Recording'},
+            {'start_step': 'Recording_Date_max', 'end_step' :'Video_Reception_Date_max', 'step_name' : 'Waiting4Video'},
+            {'start_step': 'Video_Reception_Date_max', 'end_step' :'Assignment_Date_min', 'step_name' : 'Waiting4Assignment'},
+            {'start_step': 'Assignment_Date_min', 'end_step' :'Target_Date_max', 'step_name' : 'Labelling'},
+            {'start_step': 'Target_Date_max', 'end_step' :'Labelling_Received_Date_max', 'step_name' : 'Waiting4Labels'},
+            {'start_step': 'Labelling_Received_Date_max', 'end_step' :'Verification_Date_max', 'step_name' : 'Waiting4Verification'},
+            {'start_step': 'Verification_Date_max', 'end_step' :'Completion/Rejection_Date_max', 'step_name' : 'Waiting4Completion'},
+        ]
+    grp_df = get_model_grp_data(df)
+    df_concat = pd.concat(get_step_df(grp_df,  start_step=step['start_step'], end_step=step['end_step'], step_name=step['step_name']) for step in steps)
+    df_concat['label'] = df_concat[['AccountNumber', 'AccountName', 'CattleFolder/Frame']].apply(lambda row: "_".join(row), axis=1)
+    df_concat.loc[df_concat['Step']=='Recording', 'Start']  = df_concat.loc[df_concat['Step']=='Recording', 'Start'] - pd.Timedelta(days=1)
+    # # df_concat
+
+    states = [s['step_name'] for s in steps]; # states
+    colors = dict(zip(states, ['blue', 'red', 'green', 'yellow', 'cyan', 'violet', 'pink']))
+
+    fig = px.timeline(data_frame=df_concat, x_start='Start', x_end='End', y='label', color='Step', color_discrete_map=colors, hover_data=['Assigned_sum', 'AccountName', 'AccountNumber'])
+    # fig.update_layout(legend=dict( 
+    #     orientation="h", 
+    
+    # )) 
+    return fig
+
+# %% ../../nbs/book/LabellingTracker/14_Model.ipynb 23
+df = None
+st.write("# Model Details")
+if 'processed_df' not in st.session_state:
+    st.write("Please go to andon page and upload data")
+else:
+    df = st.session_state['processed_df'] 
+    st.markdown("## Summary Model Durations")
+    st.markdown(" Below plot includes a summary of all the frames in different model farms. Process includes recording and sharing video everyday with non sequential sharing schedule")
+    with st.container(border=True):
+        tab_sum, tab_mean = st.tabs(['Sum', 'Mean'])
+        with tab_sum:
+            st.pyplot(get_model_summary(df))
+        with tab_mean:
+            st.pyplot(get_model_summary(df, kind='mean'))
+        ncols = 3
+        dcols = st.columns(ncols)
+        for i, col in enumerate(['Recording','Waiting4Video', 'Waiting4Assignment', 'Labelling_Duration', 'Waiting4Verification', 'Waiting4Completion']):
+            with dcols[i%ncols]:
+                st.pyplot(get_model_hist(df, col), use_container_width=True)
+
+    st.markdown("## Timeline")
+    with st.container(border=True):
+        st.plotly_chart(get_gantt(df), theme="streamlit", use_container_width=True)

requirements.txt

@@ -0,0 +1,4 @@
+fastcore 
+pandas 
+matplotlib 
+plotly