Update app.py

app.py

@@ -7,107 +7,6 @@ import cx_Oracle as ora
 import pandas as pd
 from pandas_profiling import ProfileReport
 
-
-QueryDatabase=False
-if QueryDatabase:
-    dsn="jdbc:oracle:thin:@//ep15-scan01:1521/cdrpr03_4.uhc.com"
-    user="UHG_801117753"
-    passw="MiscPassword2023$!" # Fake Password - don't share this or run without changing to your ID
-    dsn_tns = ora.makedsn('ep15-scan01', '1521', service_name='cdrpr03_4.uhc.com') 
-
-    # Create a connection object
-    conn = ora.connect(user=user, password=passw, dsn=dsn_tns)
-
-    # Create a cursor object
-    c = conn.cursor()
-
-    # Execute the SQL query and store the result in a pandas dataframe
-    query = """
-    select
-        count(*) as RecordCount, 
-        --Age, SID, MBR_ID, -- Optional toggle - remove these to collapse across members with a record count.
-        TOPICID, TOPIC_DESC, TOPIC, PATHWAY, CATEGORY, INTERVENTION_DESC, 
-        SYMPTOM_DESC, KNOWLEDGE_DESC, BEHAVIOR_DESC, STATUS_DESC, INT_CATEGORY_ID, RSALINEOFBUSINESS, TARGET, 
-        CAREDESCRIPTOR, URGENCY, TOPICSOURCE, SIGNSSYMPTOMS, POC_SGN_SYMP_ID, SPOKENLANGUAGE, HEALTHTOPIC, 
-        CATEGORYID, TARGETID, CAREID, CQM, Gender, Race, AgeGroup  
-        from 
-        (
-        select
-        aa.SBSCR_ID_TXT SID,
-        a.MBR_ID,
-        b.POC_PROB_ID TopicID,
-        b.POC_PROB_DESC Topic_Desc,
-        REPLACE(REPLACE(b.POC_PROB_NM,'/',''),' ','') Topic,
-        a.POC_PROB_SRC_DESC Pathway, 
-        (select POC_INTRVN_CATGY_NM from POC_INTRVN_CATGY pig where pig.POC_INTRVN_CATGY_ID = d.POC_INTRVN_CATGY_ID) as Category, 
-        c.ADD_DESC Intervention_Desc,
-        e.ADD_DESC Symptom_Desc,
-        a.KNW_OTCOME_RT_ADD_DESC KNOWLEDGE_DESC, 
-        a.BHV_OTCOME_RT_ADD_DESC BEHAVIOR_DESC, 
-        a.STS_OTCOME_RT_ADD_DESC STATUS_DESC, 
-        d.POC_INTRVN_CATGY_ID Int_Category_ID,     
-        RSA_POP_TYP_ID,
-        (select ref_desc from ref where ref_nm = 'rsaPopulationType' and ref_cd = RSA_POP_TYP_ID) as RSALineOfBusiness,
-        (select POC_INTRVN_TGT_NM from POC_INTRVN_TGT pit where pit.POC_INTRVN_TGT_ID = d.POC_INTRVN_TGT_ID) as Target, 
-        (select POC_INTRVN_CARE_DESC from POC_INTRVN_CARE pic where pic.POC_INTRVN_CARE_ID = d.POC_INTRVN_CARE_ID) as CareDescriptor,       
-        Case to_char(a.POC_PROB_URGNCY_MOD_ID) when '1' then 'Actual' when '3' then 'Potential' else 'Other' end as Urgency, 
-        (select ref_desc from ref where ref_nm = 'pocProbSourceType' and ref_cd = a.POC_PROB_SRC_TYP_ID) as TopicSource,
-        (select POC_SGN_SYMP_NM from POC_SGN_SYMP pss where pss.POC_SGN_SYMP_ID = e.POC_SGN_SYMP_ID) as SignsSymptoms,
-        e.POC_SGN_SYMP_ID,
-        CALAP_SPOKEN_LANG_TYP_ID SpokenLanguage,
-        REPLACE(b.POC_PROB_NM,'/','') HealthTopic,
-        a.POC_PROB_ID HealthTopicID,
-        d.POC_INTRVN_CATGY_ID CategoryID,
-        d.POC_INTRVN_TGT_ID TargetID,
-        d.POC_INTRVN_CARE_ID CareID,
-        c.CQM_IND CQM,
-
-        aa.GDR_CD Gender, 
-        aa.RACE_CD Race,
-        (2023 - EXTRACT(year FROM aa.BTH_DT)) Age,
-        Case --Five age groups: 0-18, 19-44, 45-64, 65-84, and 85 and over
-                when ((2023 - EXTRACT(year FROM aa.BTH_DT))>=0 and (2023 - EXTRACT(year FROM aa.BTH_DT))<=18) then 'Age0to18' 
-                when ((2023 - EXTRACT(year FROM aa.BTH_DT))> 18 and (2023 - EXTRACT(year FROM aa.BTH_DT))<=44) then 'Age19to44' 
-                when ((2023 - EXTRACT(year FROM aa.BTH_DT))> 44 and (2023 - EXTRACT(year FROM aa.BTH_DT))<=64) then 'Age44to64' 
-                when ((2023 - EXTRACT(year FROM aa.BTH_DT))> 64 and (2023 - EXTRACT(year FROM aa.BTH_DT))<=84) then 'Age64to84' 
-                when ((2023 - EXTRACT(year FROM aa.BTH_DT))> 85) then 'Age85andOver' 
-                else 'Other' end as AgeGroup
-        
-    from MBR_POC_PROB a   -- select * from MBR_POC_PROB where MBR_ID=117179570
-        join MBR aa on a.MBR_ID = aa.MBR_ID  --and a.POC_PROB_URGNCY_MOD_ID = 1 --actual
-        join STG_HSR.POC_PROB b on a.POC_PROB_ID = b.POC_PROB_ID 
-        join MBR_POC_PROB_INTRVN c on 
-            (a.MBR_POC_PROB_ID = c.MBR_POC_PROB_ID and c.REMV_FROM_PLN_LIST_IND=0)
-        join POC_INTRVN d on c.POC_INTRVN_ID = d.POC_INTRVN_ID 
-        left outer join MBR_POC_PROB_SGN_SYMP e 
-            on (e.MBR_POC_PROB_ID = c.MBR_POC_PROB_ID) 
-        where a.POC_PROB_URGNCY_MOD_ID = 1 and 
-
-            -- Optional toggle - 1 versus 120 days.
-            a.CHG_DTTM > sysdate - 1    -- 1 Day
-            -- a.CHG_DTTM > sysdate - 1    -- 182 Days = 6 Months
-            
-        ) i
-        group by 
-        --Age, SID, MBR_ID, -- Optional toggle - remove these to collapse across members with a record count.
-        TOPICID, TOPIC_DESC, TOPIC, PATHWAY, CATEGORY, INTERVENTION_DESC, SYMPTOM_DESC, KNOWLEDGE_DESC, BEHAVIOR_DESC, STATUS_DESC,INT_CATEGORY_ID,RSALINEOFBUSINESS, 
-        TARGET, CAREDESCRIPTOR, URGENCY, TOPICSOURCE,   SIGNSSYMPTOMS, POC_SGN_SYMP_ID, SPOKENLANGUAGE, HEALTHTOPIC, 
-        CATEGORYID, TARGETID, CAREID, CQM, Gender, Race, AgeGroup    
-        -- Optional toggle:
-        --order by MBR_ID desc
-        order by TOPICID desc -- orders by Count
-            """
-    df = pd.read_sql(query, con=conn)
-    # Close the cursor and connection
-    c.close()
-    conn.close()
-    # Show the dataframe in a streamlit grid
-    st.dataframe(df)
-
-# automatic visualizer
-
-# st.set_page_config(page_title="File Upload and Profiling", layout="wide")
-
 st.title("File Upload and Profiling")
 
 # uploaded_file = st.file_uploader("Upload a CSV file", type="csv")