Upload app.py

app.py

@@ -0,0 +1,168 @@
+#pass="Leswhdc2023$!"
+
+import streamlit as st
+import pandas as pd
+import plotly.express as px
+import cx_Oracle as ora
+import pandas as pd
+from pandas_profiling import ProfileReport
+
+
+QueryDatabase=False
+if QueryDatabase:
+    dsn="jdbc:oracle:thin:@//ep15-scan01:1521/cdrpr03_4.uhc.com"
+    user="UHG_801117753"
+    passw="MiscPassword2023$!" # 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")
+
+RunProfiler=False
+if uploaded_file is not None:
+    if RunProfiler:
+
+        # Load the data using pandas
+        df = pd.read_csv(uploaded_file)
+
+        # Generate the pandas profiling report
+        profile = ProfileReport(df, explorative=True)
+
+        # Display the pandas profiling report using streamlit
+        st.header("Data Profiling Report")
+        st.write(profile.to_html(), unsafe_allow_html=True)
+
+        # Display word statistics for each categorical string column
+        cat_cols = df.select_dtypes(include='object').columns
+        st.header("Word Statistics for Categorical Columns")
+        for col in cat_cols:
+            st.subheader(col)
+            word_count = df[col].str.split().apply(len).value_counts().sort_index()
+            st.bar_chart(word_count)
+
+    # Grouped count by each feature
+    num_cols = df.select_dtypes(include=['float', 'int']).columns
+    st.header("Grouped Count by Each Feature")
+    for col in num_cols:
+        st.subheader(col)
+        count_by_feature = df.groupby(col).size().reset_index(name='count')
+        st.bar_chart(count_by_feature)
+
+
+# Upload a CSV dataset
+uploaded_file = st.file_uploader("Upload your dataset", type=["csv"])
+if uploaded_file is not None:
+    # Load the dataset and display the first 5 rows
+    df = pd.read_csv(uploaded_file)
+    st.dataframe(df.head())
+
+    # Generate a treemap or sunburst plot based on data types
+    numerical_cols = df.select_dtypes(include=["float", "int"]).columns
+    categorical_cols = df.select_dtypes(include=["object"]).columns
+
+    fig = px.treemap(df, path=categorical_cols)
+    st.plotly_chart(fig)
+        
+    #if len(numerical_cols) >= 2:
+    #    fig = px.scatter_matrix(df, dimensions=numerical_cols)
+    #    st.plotly_chart(fig)
+    #elif len(categorical_cols) >= 2:
+    #    fig = px.treemap(df, path=categorical_cols)
+    #    st.plotly_chart(fig)
+    #else:
+    #    fig = px.sunburst(df, path=categorical_cols + numerical_cols)
+    #    st.plotly_chart(fig)