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+web: sh setup.sh && streamlit run facility_ranking.py

facility_ranking.py

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+import pandas as pd
+import numpy as np
+import streamlit as st
+import base64  # Standard Python Module
+from io import StringIO, BytesIO  # Standard Python Module
+
+# hide menu and footer
+st.set_page_config(page_title=None, page_icon=None, layout="wide", initial_sidebar_state="auto", menu_items=None)
+hide_streamlit_style = """
+            <style>
+            #MainMenu {visibility: hidden;}
+            footer {visibility: hidden;}
+            </style>
+            """
+st.markdown(hide_streamlit_style, unsafe_allow_html=True)  # unsafe_allow_html allows us to embed html code
+
+
+
+
+
+def generate_excel_download_link(df):
+    # Credit Excel: https://discuss.streamlit.io/t/how-to-add-a-download-excel-csv-function-to-a-button/4474/5
+    towrite = BytesIO()
+    df.to_excel(towrite, encoding="utf-8", index=False, header=True)  # write to BytesIO buffer
+    towrite.seek(0)  # reset pointer
+    b64 = base64.b64encode(towrite.read()).decode()
+    href = f'<a href="data:application/vnd.openxmlformats-officedocument.spreadsheetml.sheet;base64,{b64}" download="facility_ranking_SSM.xlsx">Download </a>'
+    return st.markdown(href, unsafe_allow_html=True)
+
+
+
+
+
+# DE=pd.read_csv('C:/Python/1. Dashboard/Facility ranking/Data/DE.csv')
+# FPE=pd.read_csv('C:/Python/1. Dashboard/Facility ranking/Data/FPE.csv')
+# sd=pd.read_csv('C:/Python/1. Dashboard/Facility ranking/Data/SD.csv')
+
+# width in full screen
+
+
+st.title('AI Ranking-CPHC Project Jhpiego')
+# add two columns
+col1, col2 = st.columns(2)
+with col1:        
+    counts=0
+
+    # file upload
+    upload_file_FPE = st.file_uploader("Upload Facility Profile Entry Data in CSV format", type=['csv'])
+
+    # if upload_file_DE is not None highlight "file not uploaded"
+
+
+    if upload_file_FPE:
+        FPE = pd.read_csv(upload_file_FPE)
+        
+        # check the columns heading available in the file
+        required_col=['NIN_2_HFI', 'HFI_Name', 'PHC_CHC_Type',
+            'State_Name', 'District_Name', 'Taluka_Name', 'Block_Name',
+            'Proposed Date', 'Progressive Date',
+            'Total Population in catchment area of facility']
+        if set(required_col).issubset(FPE.columns):
+            st.subheader("Perfect")
+            st.write(FPE.head(3))
+        else:
+            st.subheader(f"Please upload correct file missing columns are {set(required_col)-set(FPE.columns)}")
+            counts=1
+
+
+    # file upload
+    upload_file_DE = st.file_uploader("Upload Daily Entry Data in CSV format", type=['csv'])
+    if upload_file_DE:
+        DE = pd.read_csv(upload_file_DE)
+        
+        required_col=['NIN ID',  'Entry Date', 'Footfall Male', 'Footfall Female ',
+        'Footfall Others ', 'Footfall Total', 'Patients received medicines ',
+        'Patients availed diagnostic tests',
+        ' Patients availed tele-consulation services ',
+        'Wellness sessions conducted ',
+        'Total participants of Wellness session']
+        if set(required_col).issubset(DE.columns):
+            st.subheader("Perfect")
+            st.write(DE.head(3))
+        else:
+            st.subheader(f"Please upload correct file missing columns are {set(required_col)-set(DE.columns)}")
+            counts=1
+
+
+    # file upload
+    upload_file_SD = st.file_uploader("Upload Service Delivery Data in CSV format", type=['csv'])
+    if upload_file_SD:
+        sd = pd.read_csv(upload_file_SD)
+        
+        required_col=['NIN ID',  'Entry Month', 
+        'HTN Individuals screened Male', 'HTN Individuals screened Female',
+        'HTN Individuals screened Other', 'HTN Newly diagnosed Male',
+        'HTN Newly diagnosed Female', 'HTN Newly diagnosed Other',
+        'HTN On treatment Male', 'HTN On treatment Female',
+        'HTN On treatment Other', 'DM Individuals screened Male',
+        'DM Individuals screened Female', 'DM Individuals screened Other',
+        'DM Newly diagnosed Male', 'DM Newly diagnosed Female',
+        'DM Newly diagnosed Other', 'DM On treatment Male',
+        'DM On treatment Female', 'DM On treatment Other',
+        'OC Individuals screened Male', 'OC Individuals screened Female',
+        'OC Individuals screened Other', 'OC Newly diagnosed Male',
+        'OC Newly diagnosed Female', 'OC Newly diagnosed Other',
+        'OC On treatment male', 'OC On treatment Female',
+        'OC On treatment Other', 'BC Individuals screened female',
+        'BC Newly diagnosed female', 'BC On treatment female',
+        'CC Individuals screened female', 'CC Newly diagnosed female',
+        'CC On treatment female', 'Individuals referred for screening male',
+        'Individuals referred for screening female',
+        'Individuals referred for screening other', 'Newly diagnosed Male',
+        'Newly diagnosed Female', 'Newly diagnosed Other', 'On treatment Male',
+        'On treatment Female', 'On treatment Other',
+        'Total Patients received antihypertensive medicines at this centre',
+        'Total Patients received ant-diabetic medicines at this centre',
+        'Medicines_TPR_AO_M', 'Total TB patients received DOTS from the centre',
+        'Medicines_CSOM_EOM', 'Availability of functional BP apparatus',
+        'Availability of functional glucometer', 'Closing stock of glucostrips',
+        'Performance/team based incentives for MO/SN/CHO',
+        'Team based incentives for ASHA/MPW']
+        if set(required_col).issubset(sd.columns):
+            st.subheader("Perfect")
+            st.write(sd.head(3))
+        else:
+            st.subheader(f"Please upload correct file missing columns are {set(required_col)-set(sd.columns)}")
+            counts=1
+
+    # if upload_file_FPE and upload_file_DE and upload_file_SD is not None:
+            # markdown processing
+        st.markdown('''
+        # *************************Processing Done: Please check next section**************************
+        ''')
+    st.subheader("This tool is developed by : Surendra Mehta Data Analyst Jhpiego India all rights reserved")
+with col2:
+
+    if counts==0 and upload_file_FPE and upload_file_DE and upload_file_SD:
+    
+
+
+
+        # Facility PE cleaning **********************************************************************************************************
+        FPE.rename(columns={'NIN_2_HFI':'NIN ID'}, inplace=True)
+
+        # Total Population in catchment area of facility remove any commas and convert in int format
+        FPE['Total Population in catchment area of facility']=FPE['Total Population in catchment area of facility'].replace({',': ''}, regex=True)
+        FPE['Total Population in catchment area of facility']=FPE['Total Population in catchment area of facility'].astype(int)
+
+        col=['NIN ID', 'HFI_Name', 'PHC_CHC_Type',
+            'State_Name', 'District_Name', 'Taluka_Name', 'Block_Name',
+            'Proposed Date', 'Progressive Date',
+            'Total Population in catchment area of facility']
+        FPE=FPE[col]
+        geo=['HFI_Name', 'PHC_CHC_Type','State_Name', 'District_Name', 'Taluka_Name', 'Block_Name']
+
+        # fill NA if any value is missing in geo 
+        FPE[geo]=FPE[geo].fillna('NA')
+        # total facility 
+        total_facility=FPE.shape[0]
+        st.write(f"Total facility in facility profile entry is :{total_facility}")
+
+        
+        # DE cleaning *******************************************************************************************************************
+        #Entry Date convert in date format in format of 2022-05-09
+        
+        DE['Entry Date'] = pd.to_datetime(DE['Entry Date'], format='%Y-%m-%d')
+        DE.head(3)
+        # add month-year column from entry date
+        DE['Month-Year'] = DE['Entry Date'].dt.strftime('%b-%Y')
+        DE['Total'] = DE["Footfall Male"] + DE["Footfall Female "]+DE["Footfall Others "]
+        # if wellness session conducted is yes then 1 else 0
+        DE['Wellness sessions conducted ']=DE['Wellness sessions conducted '].replace({'Yes': 1, 'No': 0})
+        DE['Reporting']=1
+
+        DE_col=['NIN ID','Reporting', 'Total',
+            ' Patients availed tele-consulation services ',
+            'Wellness sessions conducted ',
+                'Month-Year']
+        DE=DE[DE_col]
+        #groupby
+        DE=DE.groupby(['NIN ID','Month-Year']).sum().reset_index()
+
+
+        #total facility in DE
+        st.write(f"Total facility in DE is :{DE['NIN ID'].nunique()}") 
+
+        # SD cleaning ********************************************************************************************************************
+
+        #Entry Date convert in date format in format of 2022-11-30
+        sd['Entry Month'] = pd.to_datetime(sd['Entry Month'], format='%Y-%m-%d')
+
+        # # add month-year column from entry date
+        sd['Month-Year'] = sd['Entry Month'].dt.strftime('%b-%Y')
+
+        #show all the columns in view
+        pd.set_option('display.max_columns', None)
+
+        int_col=['Individuals empanelled',
+            'Community Based Assessment Checklist filled',
+            'HTN Individuals screened Male', 'HTN Individuals screened Female',
+            'HTN Individuals screened Other', 'HTN Newly diagnosed Male',
+            'HTN Newly diagnosed Female', 'HTN Newly diagnosed Other',
+            'HTN On treatment Male', 'HTN On treatment Female',
+            'HTN On treatment Other', 'DM Individuals screened Male',
+            'DM Individuals screened Female', 'DM Individuals screened Other',
+            'DM Newly diagnosed Male', 'DM Newly diagnosed Female',
+            'DM Newly diagnosed Other', 'DM On treatment Male',
+            'DM On treatment Female', 'DM On treatment Other',
+            'OC Individuals screened Male', 'OC Individuals screened Female',
+            'OC Individuals screened Other', 'OC Newly diagnosed Male',
+            'OC Newly diagnosed Female', 'OC Newly diagnosed Other',
+            'OC On treatment male', 'OC On treatment Female',
+            'OC On treatment Other', 'BC Individuals screened female',
+            'BC Newly diagnosed female', 'BC On treatment female',
+            'CC Individuals screened female', 'CC Newly diagnosed female',
+            'CC On treatment female', 'Individuals referred for screening male',
+            'Individuals referred for screening female',
+            'Individuals referred for screening other', 'Newly diagnosed Male',
+            'Newly diagnosed Female', 'Newly diagnosed Other', 'On treatment Male',
+            'On treatment Female', 'On treatment Other',
+            'Total Patients received antihypertensive medicines at this centre',
+            'Total Patients received ant-diabetic medicines at this centre','Medicines_TPR_AO_M',"Closing stock of glucostrips"]
+
+
+        # convert all the int columns in int format
+        sd_int=sd[int_col].head()
+        # remove any commas in the int columns
+        sd[sd_int.columns] = sd[sd_int.columns].replace({',': ''}, regex=True)
+        #convert all the int columns in int format
+        sd[int_col] = sd[int_col].apply(pd.to_numeric, errors='coerce', axis=1)
+
+        # add total column in sd_int
+
+        #HTN
+        sd['HTN screened '] = sd['HTN Individuals screened Male']+ sd['HTN Individuals screened Female']+ sd['HTN Individuals screened Other']
+        sd['HTN diagnosed '] = sd['HTN Newly diagnosed Male']+ sd['HTN Newly diagnosed Female']+ sd['HTN Newly diagnosed Other']
+        sd['HTN on treatment '] = sd['HTN On treatment Male']+ sd['HTN On treatment Female']+ sd['HTN On treatment Other']
+
+        #DM
+        sd['DM screened '] = sd['DM Individuals screened Male']+ sd['DM Individuals screened Female']+ sd['DM Individuals screened Other']
+        sd['DM diagnosed '] = sd['DM Newly diagnosed Male']+ sd['DM Newly diagnosed Female']+ sd['DM Newly diagnosed Other']
+        sd['DM on treatment '] = sd['DM On treatment Male']+ sd['DM On treatment Female']+ sd['DM On treatment Other']
+
+        #DM
+        sd['OC screened '] = sd['OC Individuals screened Male']+ sd['OC Individuals screened Female']+ sd['OC Individuals screened Other']
+        sd['OC diagnosed '] = sd['OC Newly diagnosed Male']+ sd['OC Newly diagnosed Female']+ sd['OC Newly diagnosed Other']
+        sd['OC on treatment '] = sd['OC On treatment male']+ sd['OC On treatment Female']+ sd['OC On treatment Other']
+
+
+        #referred
+        sd['TB_Referred'] = sd['Individuals referred for screening male']+ sd['Individuals referred for screening female']+ sd['Individuals referred for screening other']
+
+        #Newly diagnosed
+        sd['TB_Newly diagnosed'] = sd['Newly diagnosed Male']+ sd['Newly diagnosed Female']+ sd['Newly diagnosed Other']
+
+        #On treatment
+        sd['TB_On treatment'] = sd['On treatment Male']+ sd['On treatment Female']+ sd['On treatment Other']
+        # if yes than 1 else 0 add both
+        sd["equipments_BP_gluco"]=sd["Availability of functional BP apparatus"].replace({'Yes': 1, 'No': 0})+sd["Availability of functional glucometer"].replace({'Yes': 1, 'No': 0})
+        sd["pbi_tbi"]=sd["Performance/team based incentives for MO/SN/CHO"].replace({'Yes': 1, 'No': 0})+sd["Team based incentives for ASHA/MPW"].replace({'Yes': 1, 'No': 0})
+
+
+        selected_col=['NIN ID', 'Facility Name', 'Facility Type', 'State', 'District',
+            'Taluka', 'Block', 'Entry Month',
+                    'Month-Year',
+            'HTN screened ', 'DM screened ',
+            'OC screened ','BC Individuals screened female', 'TB_Referred','equipments_BP_gluco',"pbi_tbi"]
+        sd=sd[selected_col]
+
+
+        # group by NIN-ID and month-year sum
+        sd=sd.groupby(['NIN ID','Month-Year']).sum().reset_index()
+
+        # total facility in SD
+        st.write("Total facility in SD",sd['NIN ID'].nunique())
+
+
+
+        df=pd.merge(DE, sd, on=['NIN ID','Month-Year'], how='outer')
+        #merge with FPE
+        df=pd.merge(FPE,df, on=['NIN ID'], how='outer')
+
+
+
+        #df add a target population column with 80 percent of the total population
+        df['Target Population']=round(df['Total Population in catchment area of facility']*0.8,0)
+
+
+
+        # # all float value in one list
+        # float_col=df.select_dtypes(include=['float']).columns.tolist()
+
+        # # convert all the float columns in int format
+        # df[float_col] = df[float_col].apply(pd.to_numeric, errors='coerce', axis=1)
+
+        # cal_Reporting map value>=20 than 15, IF value >=10 than 10 else 0
+        df['cal_Reporting']=np.where(df['Reporting']>=20,15,np.where(df['Reporting']>=10,10,0))
+        # cal_footfall map if 'Total'/'Target Population'>=1, 15,if 'Total'/'Target Population'>=.8, 10,if 'Total'/'Target Population'>=.5,5, else 0
+
+        df['cal_footfall']=np.where((df['Total']/df['Target Population'])>=1,15,np.where((df['Total']/df['Target Population'])>=.8,10,np.where((df['Total']/df['Target Population'])>=.5,5,0)))
+        # Equipment value AV4=2,10,IF value =1,5,0
+        df['cal_Equipment']=np.where(df['equipments_BP_gluco']==2,10,np.where(df['equipments_BP_gluco']==1,5,0))
+        # pbi_tbi value AV4=2,10,IF value =1,5,0
+        df['cal_pbi_tbi']=np.where(df['pbi_tbi']==2,10,np.where(df['pbi_tbi']==1,5,0))
+        # tb refered value referred/total >=30,10 ,if referred/total >=20,5, else 0
+        df['cal_tb']=np.where((df['TB_Referred']/df['Total'])>=.3,10,np.where((df['TB_Referred']/df['Total'])>=.2,5,0))
+        # teleconsultation value AM4>=30,5,IF(AM4>=15,3,IF(AM4>=1,1,0
+        df['cal_teleconsultation']=np.where(df[' Patients availed tele-consulation services ']>=30,5,np.where(df[' Patients availed tele-consulation services ']>=15,3,np.where(df[' Patients availed tele-consulation services ']>=1,1,0)))
+        # wellness and yoga AQ6>=10,5,IF(AQ6>=5,3,0)
+        df['cal_wellness']=np.where(df['Wellness sessions conducted ']>=10,5,np.where(df['Wellness sessions conducted ']>=5,3,0))
+        # save the df in csv
+        
+
+        # pivot table 
+        df_pivot=df.pivot_table(index=["NIN ID","HFI_Name","PHC_CHC_Type","State_Name","District_Name","Taluka_Name","Block_Name"],columns='Month-Year',
+        values=['HTN screened ','DM screened ','OC screened ','BC Individuals screened female','cal_Reporting','cal_footfall','cal_Equipment',
+            'cal_pbi_tbi','cal_tb','cal_teleconsultation','cal_wellness'],aggfunc='sum').reset_index()
+
+        # total unique facility in all 3 uploaded data
+        st.write(f"Total facility in all 3 uploaded data {df_pivot['NIN ID'].nunique()}")
+
+        col=[col[0] + '_' + col[1] for col in df_pivot.columns]
+        df_pivot.columns = col
+        df_pivot = df_pivot.reset_index()
+
+
+
+        # filter all columns having "screened" or refered
+        screened_col=[col for col in df_pivot.columns if 'screened' in col]
+
+        # import minmaxscaler
+        from sklearn.preprocessing import MinMaxScaler
+
+        # apply minmaxscaler on screened_col
+        scaler = MinMaxScaler()
+        df_pivot[screened_col] = scaler.fit_transform(df_pivot[screened_col])
+
+        # for each month-year calculate the sum of all the columns 
+        unique_month=df['Month-Year'].unique()
+        #drop blank value
+        unique_month=unique_month[~pd.isnull(unique_month)]
+
+        for i in unique_month:
+            df_pivot[i+'_sum']=df_pivot[[col for col in df_pivot.columns if i in col]].sum(axis=1)
+            # add rank column and based on sum of each month-year rank the facility
+            df_pivot[i+'_rank']=df_pivot[i+'_sum'].rank(ascending=False)
+
+
+
+        # For Below month ranking will be calculated st.write(unique_month)
+        st.subheader('Below month ranking will be calculated')
+        st.write(unique_month)
+        
+        # calculate the sum of all the month-year sum
+        df_pivot['Overall Score']=df_pivot[[col for col in df_pivot.columns if 'sum' in col]].sum(axis=1)
+
+
+        
+        
+
+        # keep only ranking and overall score columns
+        sum=[col for col in df_pivot.columns if 'sum' in col]
+        rank=[col for col in df_pivot.columns if 'rank' in col]
+
+        # add both list in one list
+        sum_rank=sum+rank
+
+
+        # all sum and rank columns in df_pivot convert in int format
+        df_pivot[sum_rank] = df_pivot[sum_rank].apply(pd.to_numeric, errors='coerce', axis=1)
+
+        df_rank=df_pivot[['NIN ID_','HFI_Name_','PHC_CHC_Type_', 'State_Name_','District_Name_','Taluka_Name_',
+        'Block_Name_','Overall Score']+sum_rank]
+
+            # df subheader 
+        st.subheader("Download the Ranking")
+        generate_excel_download_link(df_rank)
+        
+            # df subheader pivot table
+        st.subheader("Detailed calculation download")
+        generate_excel_download_link(df_pivot)
+        
+            # subheader download all 3 files analysis
+        st.subheader("Download all 3 files analysis")
+        generate_excel_download_link(df)
+
+        # df subheader
+        
+
+            
+        
+

requirements.txt

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+numpy==1.23.5
+pandas==1.5.2
+scikit_learn==1.2.0
+streamlit==1.16.0

setup.sh

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+mkdir -p ~/.streamlit/
+
+echo "\
+[general]\n\
+email = \"your-email@domain.com\"\n\
+" > ~/.streamlit/credentials.toml
+
+echo "\
+[server]\n\
+headless = true\n\
+enableCORS=false\n\
+port = $PORT\n\
+" > ~/.streamlit/config.toml