Create app.py

app.py

@@ -0,0 +1,107 @@
+import streamlit as st
+import pandas as pd
+import numpy as np
+
+# Function to generate synthetic data
+def generate_synthetic_data(num_members):
+    unique_ids = [f"MEM_{i:05d}" for i in range(1, num_members + 1)]
+    primary_keys = [f"PPK_{i:05d}" for i in range(1, num_members + 1)]
+
+    # Synthetic Enrollments
+    enrollments_data = {
+        "MEM_AGE": np.random.randint(18, 80, num_members),
+        "MEM_MSA_NAME": np.random.choice(["DETROIT", "HONOLULU", "LOS ANGELES"], num_members),
+        "MEM_STAT": np.random.choice(["ACTIVE", "INACTIVE"], num_members),
+        "MEMBER_ID": unique_ids,
+        "PRIMARY_PERSON_KEY": primary_keys,
+        "PAYER_LOB": np.random.choice(["MEDICAID", "COMMERCIAL", "MEDICARE"], num_members),
+        "PAYER_TYPE": np.random.choice(["PPO", "HMO"], num_members),
+        "PRIMARY_CHRONIC_CONDITION_ROLLUP_DESC": np.random.choice(["Cancer", "Diabetes", "Hypertension"], num_members),
+        "PROD_TYPE": np.random.choice(["DENTAL", "VISION", "MEDICAL"], num_members),
+        "RELATION": np.random.choice(["SUBSCRIBER", "DEPENDENT"], num_members),
+    }
+    enrollments_df = pd.DataFrame(enrollments_data)
+
+    # Synthetic Members
+    members_data = {
+        "MEM_ETHNICITY": np.random.choice(["Hispanic", "Non-Hispanic", None], num_members),
+        "MEM_GENDER": np.random.choice(["M", "F"], num_members),
+        "MEM_MSA_NAME": enrollments_data["MEM_MSA_NAME"],
+        "MEM_STATE": np.random.choice(["MI", "HI", "CA"], num_members),
+        "MEM_ZIP3": np.random.randint(100, 999, num_members),
+        "MEMBER_ID": unique_ids,
+        "PRIMARY_PERSON_KEY": primary_keys,
+    }
+    members_df = pd.DataFrame(members_data)
+
+    # Synthetic Providers
+    providers_data = {
+        "PROV_CLINIC_STATE": np.random.choice(["MI", "HI", "CA"], num_members),
+        "PROV_CLINIC_ZIP": np.random.randint(10000, 99999, num_members),
+        "PROV_KEY": [f"PK_{i:05d}" for i in range(1, num_members + 1)],
+        "PROV_TAXONOMY": np.random.choice(["208100000X", "207RE0101X"], num_members),
+        "PROV_TYPE": np.random.choice(["Type1", "Type2"], num_members),
+    }
+    providers_df = pd.DataFrame(providers_data)
+
+    # Synthetic Services
+    services_data = {
+        "MEMBER_ID": unique_ids,
+        "PRIMARY_PERSON_KEY": primary_keys,
+        "Sum of AMT_ALLOWED": np.random.uniform(1000, 10000, num_members),
+        "Sum of AMT_BILLED": np.random.uniform(1000, 15000, num_members),
+        "RELATION": enrollments_data["RELATION"],
+        "SERVICE_SETTING": np.random.choice(["OUTPATIENT", "INPATIENT"], num_members),
+    }
+    services_df = pd.DataFrame(services_data)
+
+    return enrollments_df, members_df, providers_df, services_df
+
+
+# Streamlit App
+st.title("Synthetic Medical Billing Data Generator")
+
+# Slider for number of members
+num_members = st.slider("Select number of unique members:", min_value=10, max_value=1000, step=10, value=100)
+
+# Generate synthetic data
+enrollments_df, members_df, providers_df, services_df = generate_synthetic_data(num_members)
+
+# Display dataframes
+st.subheader("Preview of Generated Data")
+st.write("Enrollments Data")
+st.dataframe(enrollments_df.head())
+st.write("Members Data")
+st.dataframe(members_df.head())
+st.write("Providers Data")
+st.dataframe(providers_df.head())
+st.write("Services Data")
+st.dataframe(services_df.head())
+
+# Allow downloading the generated files
+st.subheader("Download Synthetic Data")
+st.download_button(
+    label="Download Enrollments Data",
+    data=enrollments_df.to_csv(index=False),
+    file_name="Synthetic_Enrollments.csv",
+    mime="text/csv",
+)
+st.download_button(
+    label="Download Members Data",
+    data=members_df.to_csv(index=False),
+    file_name="Synthetic_Members.csv",
+    mime="text/csv",
+)
+st.download_button(
+    label="Download Providers Data",
+    data=providers_df.to_csv(index=False),
+    file_name="Synthetic_Providers.csv",
+    mime="text/csv",
+)
+st.download_button(
+    label="Download Services Data",
+    data=services_df.to_csv(index=False),
+    file_name="Synthetic_Services.csv",
+    mime="text/csv",
+)
+