app.py

import streamlit as st
import pandas as pd
import random
from datetime import datetime

# Helper function to generate a random date within the last year
def random_date():
    start_date = datetime.now() - timedelta(days=365)
    random_days = random.randint(0, 365)
    return (start_date + timedelta(days=random_days)).strftime("%Y-%m-%d")

# Cache the function that loads the product catalog
@st.cache
    
# Define the products and their categories
products = {
    "Product Name": [
        "Notepad++", "WinRAR", "7-Zip", "CCleaner", "TeamViewer",
        "FileZilla", "PuTTY", "WinSCP", "Everything", "Greenshot",
        "Visual Studio Code", "JetBrains IntelliJ IDEA", "Sublime Text", "Atom", "Eclipse",
        "PyCharm", "NetBeans", "Xcode", "Android Studio", "GitLab",
        "Norton Antivirus", "McAfee Total Protection", "Kaspersky Internet Security", "Bitdefender Antivirus Plus", "Avast Free Antivirus",
        "Sophos Home", "Trend Micro Antivirus+", "ESET NOD32 Antivirus", "F-Secure SAFE", "Malwarebytes",
        "Microsoft Office 365", "Google Workspace", "Slack", "Trello", "Asana",
        "Zoom", "Evernote", "Notion", "Dropbox", "Adobe Acrobat Reader",
        "Adobe Photoshop", "Adobe Illustrator", "Adobe Premiere Pro", "Final Cut Pro", "Sketch",
        "Blender", "Autodesk Maya", "CorelDRAW", "GIMP", "Inkscape"
    ],
    "Category": [
        "Utility Tools", "Utility Tools", "Utility Tools", "Utility Tools", "Utility Tools",
        "Utility Tools", "Utility Tools", "Utility Tools", "Utility Tools", "Utility Tools",
        "Development Tools", "Development Tools", "Development Tools", "Development Tools", "Development Tools",
        "Development Tools", "Development Tools", "Development Tools", "Development Tools", "Development Tools",
        "Security Software", "Security Software", "Security Software", "Security Software", "Security Software",
        "Security Software", "Security Software", "Security Software", "Security Software", "Security Software",
        "Productivity Software", "Productivity Software", "Productivity Software", "Productivity Software", "Productivity Software",
        "Productivity Software", "Productivity Software", "Productivity Software", "Productivity Software", "Productivity Software",
        "Creative Software", "Creative Software", "Creative Software", "Creative Software", "Creative Software",
        "Creative Software", "Creative Software", "Creative Software", "Creative Software", "Creative Software"
    ],
    "Cyber Approved": [random.choice([True, False]) for _ in range(50)],
    "Accessibility Approved": [random.choice([True, False]) for _ in range(50)],
    "Privacy Approved": [random.choice([True, False]) for _ in range(50)],
    "Review Date": [random_date() for _ in range(50)]
}

# Example of caching a complex filtering operation
@st.cache
def filter_catalog(catalog, category=None, search_query=None):
    filtered = catalog
    if category:
        filtered = filtered[filtered["Category"].isin(category)]
    if search_query:
        filtered = filtered[filtered["Product Name"].str.contains(search_query, case=False)]
    return filtered



# Create the DataFrame
catalog = pd.DataFrame(products)

# Apply filters based on user input
category = st.sidebar.multiselect("Filter by Category", options=catalog["Category"].unique())
search_query = st.sidebar.text_input("Search Products")

filtered_catalog = filter_catalog(catalog, category, search_query)



# Display the DataFrame for verification
print(catalog)




# Streamlit app layout
st.title("Enterprise Software Product Catalog")
st.write("This is the source of truth for app approval statuses within the enterprise.")

# Sidebar for filters
category = st.sidebar.multiselect("Filter by Category", options=catalog["Category"].unique())
cyber_approved = st.sidebar.checkbox("Cyber Approved")
accessibility_approved = st.sidebar.checkbox("Accessibility Approved")
privacy_approved = st.sidebar.checkbox("Privacy Approved")


# Filtering logic
filtered_catalog = catalog
if category:
    filtered_catalog = filtered_catalog[filtered_catalog["Category"].isin(category)]
if cyber_approved:
    filtered_catalog = filtered_catalog[filtered_catalog["Cyber Approved"] == True]
if accessibility_approved:
    filtered_catalog = filtered_catalog[filtered_catalog["Accessibility Approved"] == True]
if privacy_approved:
    filtered_catalog = filtered_catalog[filtered_catalog["Privacy Approved"] == True]



# Display the filtered product catalog
st.dataframe(filtered_catalog)