Update app.py

app.py

@@ -1,7 +1,7 @@
 import streamlit as st
 import pandas as pd
 import random
-from datetime import datetime
+from datetime import datetime, timedelta
 
 # Helper function to generate a random date within the last year
 def random_date():
@@ -9,69 +9,33 @@ def random_date():
     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
+# Function to load and cache 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
+def load_catalog():
+    products = {
+        # Products and categories as defined previously...
+        "Review Date": [random_date() for _ in range(50)]
+    }
+    return pd.DataFrame(products)
+
+# Cached function for filtering the catalog
 @st.cache
-def filter_catalog(catalog, category=None, search_query=None):
+def filter_catalog(catalog, category=None, cyber_approved=None, accessibility_approved=None, privacy_approved=None, search_query=None):
     filtered = catalog
     if category:
         filtered = filtered[filtered["Category"].isin(category)]
+    if cyber_approved is not None:
+        filtered = filtered[filtered["Cyber Approved"] == cyber_approved]
+    if accessibility_approved is not None:
+        filtered = filtered[filtered["Accessibility Approved"] == accessibility_approved]
+    if privacy_approved is not None:
+        filtered = filtered[filtered["Privacy Approved"] == privacy_approved]
     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)
-
-
-
+# Load the catalog
+catalog = load_catalog()
 
 # Streamlit app layout
 st.title("Enterprise Software Product Catalog")
@@ -79,25 +43,20 @@ st.write("This is the source of truth for app approval statuses within the enter
 
 # 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]
-
+cyber_approved = st.sidebar.checkbox("Cyber Approved", value=True, key="cyber")
+accessibility_approved = st.sidebar.checkbox("Accessibility Approved", value=True, key="accessibility")
+privacy_approved = st.sidebar.checkbox("Privacy Approved", value=True, key="privacy")
+search_query = st.sidebar.text_input("Search Products", key="search")
 
+# Apply filters based on user input
+filtered_catalog = filter_catalog(
+    catalog,
+    category=category,
+    cyber_approved=cyber_approved,
+    accessibility_approved=accessibility_approved,
+    privacy_approved=privacy_approved,
+    search_query=search_query
+)
 
 # Display the filtered product catalog
 st.dataframe(filtered_catalog)
-
-