Remove Streamlit web interface and CLI, focus on Python API

BREAKING CHANGES:
- Remove `socialmapper` CLI command
- Remove `socialmapper/app.py` (Streamlit web interface)
- Remove `socialmapper/cli.py` (CLI entry point)
- Remove `streamlit` and `streamlit-folium` dependencies

SocialMapper is now a pure Python library. All 5 core functions
remain unchanged: create_isochrone, get_poi, get_census_blocks,
get_census_data, create_map.

Updated documentation:
- README: Removed web interface section
- Quick start guides: Focus on Python API usage
- Security docs: Simplified to match actual implementation
- API reference: Updated function signatures

Added examples/live_api_example.py for real API usage demo.

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

Author: mihiarc

CHANGELOG.md

@@ -5,6 +5,38 @@ All notable changes to this project will be documented in this file.
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
+## [1.0.0] - 2025-01-22
+
+### ⚠️ Breaking Changes
+
+**Removed Streamlit Web Interface and CLI**
+
+SocialMapper is now a pure Python library focused on the API. The Streamlit web interface and CLI entry point have been removed to simplify the package and reduce dependencies.
+
+**Removed:**
+- `socialmapper` CLI command
+- `socialmapper/app.py` (Streamlit web interface)
+- `socialmapper/cli.py` (CLI entry point)
+- `streamlit` and `streamlit-folium` dependencies
+
+**Migration:**
+- Use the Python API directly instead of the web interface
+- All 5 core functions remain unchanged: `create_isochrone`, `get_poi`, `get_census_blocks`, `get_census_data`, `create_map`
+
+### ✨ Features
+
+- **API-Only Focus** - Cleaner, lighter package focused on the Python API
+- **Reduced Dependencies** - Removed Streamlit and related packages
+- **Simplified Installation** - Faster installs with fewer dependencies
+
+### 📚 Documentation
+
+- Updated all documentation to reflect API-only usage
+- Removed references to CLI and web interface
+- Simplified security guide to match actual implementation
+
+---
+
 ## [0.9.0] - 2025-10-08
 
 ### ⚠️ Breaking Changes

README.md

@@ -57,24 +57,9 @@ SocialMapper helps you understand how people connect with important places in th
 - **Calculating Accessibility** - Measure travel distances and identify access patterns
 - **Supporting Multiple Formats** - Export data as CSV, GeoJSON, Parquet, and more
 
-## 🚀 Try It Now - No Coding Required!
+## 🚀 Try It Now
 
-### Web Interface (Easiest)
-
-```bash
-# Install and launch the web UI
-pip install socialmapper
-socialmapper
-```
-
-This opens an interactive web interface where you can:
-- Enter any location (address, city, or coordinates)
-- Select analysis type (isochrone, POI discovery, census data)
-- Configure parameters with sliders and dropdowns
-- View results on an interactive map
-- Export data as CSV or GeoJSON
-
-### Python API (For Developers)
+### Quick Start
 
 ```python
 # Run instant demo analysis - no API keys needed!

docs/ARCHITECTURE.md

@@ -634,7 +634,7 @@ graph LR
 ```python
 # Analyze library accessibility in Portland
 iso = create_isochrone("Portland, OR", travel_time=15, travel_mode="walk")
-libraries = get_poi(iso, categories=["library"], limit=50)
+libraries = get_poi(iso, categories=["education"], limit=50)
 census = get_census_data(iso, variables=["population", "median_income"])
 map_result = create_map(census.data, "population", title="Population Coverage")
 ```

docs/api-reference.md

@@ -132,29 +132,20 @@ Retrieves POIs from OpenStreetMap within a specified area, either defined by tra
 
 #### Available POI Categories
 
-**Food & Drink:**
-- `"restaurant"`, `"cafe"`, `"bar"`, `"fast_food"`, `"pub"`
-
-**Education:**
-- `"school"`, `"university"`, `"college"`, `"library"`, `"kindergarten"`
-
-**Healthcare:**
-- `"hospital"`, `"clinic"`, `"pharmacy"`, `"doctors"`, `"dentist"`
-
-**Recreation:**
-- `"park"`, `"playground"`, `"sports_centre"`, `"swimming_pool"`, `"theatre"`, `"cinema"`
-
-**Shopping:**
-- `"grocery"`, `"supermarket"`, `"convenience"`, `"shopping_mall"`, `"department_store"`
-
-**Finance:**
-- `"bank"`, `"atm"`
-
-**Transportation:**
-- `"bus_station"`, `"subway_entrance"`, `"parking"`, `"fuel"`
-
-**Public Services:**
-- `"police"`, `"fire_station"`, `"post_office"`, `"townhall"`
+Use these high-level category names:
+
+| Category | Includes |
+|----------|----------|
+| `"food_and_drink"` | Restaurants, cafes, bars, bakeries, fast food |
+| `"education"` | Schools, universities, libraries, kindergartens |
+| `"healthcare"` | Hospitals, clinics, pharmacies, doctors, dentists |
+| `"recreation"` | Parks, playgrounds, sports centres, theatres, cinemas |
+| `"shopping"` | Supermarkets, malls, convenience stores, retail |
+| `"services"` | Banks, ATMs, post offices, salons |
+| `"transportation"` | Bus stations, parking, fuel stations |
+| `"accommodation"` | Hotels, hostels, motels |
+| `"religious"` | Churches, mosques, temples |
+| `"utilities"` | Police, fire stations, government offices |
 
 #### Returns
 
@@ -175,26 +166,26 @@ Retrieves POIs from OpenStreetMap within a specified area, either defined by tra
 #### Examples
 
 ```python
-# Find restaurants within 5km radius (default)
+# Find food and drink POIs within 5km radius (default)
 pois = get_poi(
     location="Seattle, WA",
-    categories=["restaurant", "cafe"]
+    categories=["food_and_drink"]
 )
-print(f"Found {len(pois)} restaurants and cafes")
-# Output: Found 75 restaurants and cafes
+print(f"Found {len(pois)} food and drink places")
+# Output: Found 75 food and drink places
 
 # POIs within 15-minute drive
 pois = get_poi(
     location=(47.6062, -122.3321),
     travel_time=15,
-    categories=["hospital", "clinic"]
+    categories=["healthcare"]
 )
 print(f"Healthcare facilities: {len(pois)}")
 for poi in pois[:3]:
     print(f"  {poi['name']}: {poi['distance_km']:.2f} km away")
 # Output: Healthcare facilities: 12
 #   Seattle Medical Center: 0.54 km away
-#   Harbor view Medical: 1.23 km away
+#   Harborview Medical: 1.23 km away
 
 # All POIs within radius (no category filter)
 pois = get_poi(

docs/getting-started/index.md

@@ -15,10 +15,10 @@ Create your first community analysis in minutes.
 ### I'm New to Programming
 
 **Start here if you're new to Python:**
-    
+
 1. Follow the **[Installation Guide](installation.md)** for setup help
 2. Try the **[Quick Start Guide](quick-start.md)** for your first analysis
-3. Use the command line interface - no coding required!
+3. Use the **demo module** to explore without API keys
 4. Explore the **[examples](https://github.com/mihiarc/socialmapper/tree/main/examples)**
 
 ### I'm a Python Developer

docs/getting-started/quick-start.md

@@ -30,22 +30,22 @@ from socialmapper import (
     get_census_data
 )
 
-# 1. Find libraries in the area
-print("Finding libraries in Raleigh...")
-libraries = get_poi(
+# 1. Find education facilities in the area
+print("Finding education facilities in Raleigh...")
+edu_pois = get_poi(
     location=(35.7796, -78.6382),  # Downtown Raleigh, NC
-    categories=["library"],
+    categories=["education"],
     limit=10
 )
-print(f"Found {len(libraries)} libraries")
+print(f"Found {len(edu_pois)} education facilities")
 
-# 2. Create a 15-minute driving isochrone for the first library
-print("\nAnalyzing first library...")
-library = libraries[0]
-print(f"Library: {library['name']}")
+# 2. Create a 15-minute driving isochrone for the first POI
+print("\nAnalyzing first education facility...")
+poi = edu_pois[0]
+print(f"Facility: {poi['name']}")
 
 isochrone = create_isochrone(
-    location=(library['lat'], library['lon']),
+    location=(poi['lat'], poi['lon']),
     travel_time=15,
     travel_mode="drive"
 )
@@ -64,11 +64,10 @@ demographics = get_census_data(
 )
 
 # 5. Calculate totals
-total_pop = sum(d.get('population', 0) for d in demographics.values())
-avg_income = sum(
-    d.get('median_income', 0) for d in demographics.values()
-    if d.get('median_income', 0) > 0
-) / len([d for d in demographics.values() if d.get('median_income', 0) > 0])
+total_pop = sum(d.get('population', 0) for d in demographics.data.values())
+incomes = [d.get('median_income', 0) for d in demographics.data.values()
+           if d.get('median_income', 0) > 0]
+avg_income = sum(incomes) / len(incomes) if incomes else 0
 
 print(f"\n📊 Results:")
 print(f"Population within 15 minutes: {total_pop:,}")
@@ -94,21 +93,21 @@ Each function returns simple Python data structures (dicts, lists) that are easy
 
 ## Try Different Analyses
 
-### Find Restaurants
+### Find Food and Drink Places
 
 ```python
 from socialmapper import get_poi
 
-# Find restaurants within 5km
-restaurants = get_poi(
+# Find food and drink within 5km
+pois = get_poi(
     location=(35.7796, -78.6382),  # Raleigh, NC
-    categories=["restaurant", "cafe"],
+    categories=["food_and_drink"],
     limit=50
 )
 
-print(f"Found {len(restaurants)} restaurants")
-for r in restaurants[:5]:
-    print(f"  {r['name']}: {r['distance_km']:.2f} km away")
+print(f"Found {len(pois)} food and drink places")
+for poi in pois[:5]:
+    print(f"  {poi['name']}: {poi['distance_km']:.2f} km away")
 ```
 
 ### Analyze Walking Distance
@@ -126,9 +125,9 @@ iso = create_isochrone(
 # Get population data
 blocks = get_census_blocks(polygon=iso)
 geoids = [b['geoid'] for b in blocks]
-data = get_census_data(geoids, ["population"])
+data = get_census_data(location=geoids, variables=["population"])
 
-total = sum(d.get('population', 0) for d in data.values())
+total = sum(d.get('population', 0) for d in data.data.values())
 print(f"Population within 10-min walk: {total:,}")
 ```
 
@@ -147,9 +146,9 @@ for name, coords in locations.items():
     iso = create_isochrone(coords, travel_time=15)
     blocks = get_census_blocks(polygon=iso)
     geoids = [b['geoid'] for b in blocks]
-    data = get_census_data(geoids, ["population"])
+    data = get_census_data(location=geoids, variables=["population"])
 
-    pop = sum(d.get('population', 0) for d in data.values())
+    pop = sum(d.get('population', 0) for d in data.data.values())
     print(f"{name}: {pop:,} people within 15 minutes")
 ```
 
@@ -169,12 +168,12 @@ iso = create_isochrone((35.7796, -78.6382), travel_time=15)
 # Get census data
 blocks = get_census_blocks(polygon=iso)
 geoids = [b['geoid'] for b in blocks]
-census_data = get_census_data(geoids, ["population"])
+census_data = get_census_data(location=geoids, variables=["population"])
 
 # Add population to blocks
 for block in blocks:
     geoid = block['geoid']
-    block['population'] = census_data.get(geoid, {}).get('population', 0)
+    block['population'] = census_data.data.get(geoid, {}).get('population', 0)
 
 # Create map
 create_map(
@@ -201,8 +200,8 @@ if len(blocks) > 50:
     sample_blocks = blocks[:50]
     geoids = [b['geoid'] for b in sample_blocks]
 
-    data = get_census_data(geoids, ["population"])
-    sample_pop = sum(d.get('population', 0) for d in data.values())
+    data = get_census_data(location=geoids, variables=["population"])
+    sample_pop = sum(d.get('population', 0) for d in data.data.values())
 
     # Estimate total
     estimated_total = int(sample_pop * len(blocks) / len(sample_blocks))

docs/index.md

@@ -111,7 +111,7 @@ print(f"Population within 15 minutes: {total_pop:,}")
 ## Why SocialMapper?
 
 - **Free and Open Source** - No licensing fees or restrictions
-- **Easy to Use** - Simple Python API and command line interface
+- **Easy to Use** - Simple 5-function Python API
 - **Reliable Data** - Uses OpenStreetMap and US Census Bureau
 - **Fast** - Optimized caching and efficient algorithms ([see performance guide](performance.md))
 - **Flexible** - Analyze any location type at any scale

docs/quick-start.md

@@ -22,9 +22,9 @@ demo.list_available_demos()
 result = demo.quick_start("Portland, OR")
 
 # That's it! You've analyzed:
-# ✓ 15-minute walk from downtown Portland
-# ✓ Found 8 libraries
-# ✓ Reached 45,000+ people
+# ✓ 15-minute drive from downtown Portland
+# ✓ Found 4 libraries
+# ✓ Reached 29,000+ people
 # ✓ Analyzed demographics
 ```
 
@@ -63,10 +63,10 @@ result = demo.quick_start("Durham, NC")
 
 ```python
 # Library accessibility analysis
-demo.show_libraries("Portland, OR", travel_time=10)
+demo.show_libraries("Portland, OR", travel_time=15)
 
-# Food desert analysis
-demo.show_food_access("Chapel Hill, NC", travel_mode="walk")
+# Food access analysis
+demo.show_food_access("Durham, NC")
 ```
 
 ## Customize Your Analysis
@@ -95,14 +95,13 @@ The demo returns a dictionary with:
 ```python
 {
     "location": "Portland, OR",
-    "travel_time": 15,
-    "travel_mode": "walk",
-    "area_sq_km": 7.2,
-    "poi_count": 8,
-    "pois": [...],  # List of POIs with names and distances
-    "total_population": 45678,
-    "median_income": 68234,
-    "demographics": {...}  # Detailed demographic breakdown
+    "area_sq_km": 125.4,
+    "poi_count": 4,
+    "pois": [...],           # List of POIs with names and distances
+    "total_population": 29118,
+    "median_income": 74746,
+    "census_blocks": [...],  # Census block group data
+    "isochrone": {...}       # GeoJSON travel-time polygon
 }
 ```
 
@@ -133,7 +132,7 @@ from socialmapper import create_isochrone, get_poi, get_census_data
 
 # Now works with ANY location in the US
 isochrone = create_isochrone("Seattle, WA", travel_time=15)
-pois = get_poi("Seattle, WA", categories=["library"])
+pois = get_poi("Seattle, WA", categories=["education"])
 census = get_census_data(location=isochrone, variables=["population"])
 ```