fio-l1-paris-kml-pois

Status: done
Score: 91
Duration: 2:10
Cost: 0.33¢
Model: google/gemma-4-26b-a4b-it

Map (reference ← swipe → agent)

0:00
A colleague handed me `paris_late_night_pois`. Our internal map server only takes flat GeoJSON or GeoParquet, so please convert it to `paris_pois.geojson`, one row per placemark. Keep `name`, include the source's classification/grouping as a `category` column, and pull each entry's 'last verified' date out of the HTML blurb into a `verified_date` column as an ISO date. We want to flag stale records.
0:00
System info 
{
  "adapter": "openrouter",
  "model": "google/gemma-4-26b-a4b-it",
  "base_url": "https://openrouter.ai/api/v1"
}
0:01
Bash ls -F 
ls -F

paris_late_night_pois.kml
0:02
Bash head -n 50 paris_late_night_pois.kml 
head -n 50 paris_late_night_pois.kml

<?xml version="1.0" encoding="UTF-8"?>
<kml xmlns="http://www.opengis.net/kml/2.2">
<Document>
  <name>Late-night POIs (RATP night-bus study)</name>
  <Folder>
    <name>Cafés ouverts tard</name>
    <Placemark>
      <name>% Arabica</name>
      <description><![CDATA[<b>% Arabica</b><br/>Cat&eacute;gorie&nbsp;: Cafés ouverts tard<br/><a href="https://example.org/poi/0">Voir la fiche</a><br/>Derni&egrave;re v&eacute;rification&nbsp;: 2026-01-01]]></description>
      <Point>
        <coordinates>2.324676,48.854156,0</coordinates>
      </Point>
    </Placemark>
    <Placemark>
      <name>1T. rue Scribe</name>
      <description><![CDATA[<b>1T. rue Scribe</b><br/>Cat&eacute;gorie&nbsp;: Cafés ouverts tard<br/><a href="https://example.org/poi/1">Voir la fiche</a><br/>Derni&egrave;re v&eacute;rification&nbsp;: 2026-08-14]]></description>
      <Point>
        <coordinates>2.329774,48.870581,0</coordinates>
      </Point>
    </Placemark>
    <Placemark>
      <name>1ndix Café</name>
      <description><![CDATA[<b>1ndix Café</b><br/>Cat&eacute;gorie&nbsp;: Cafés ouverts tard<br/><a href="https://example.org/poi/2">Voir la fiche</a><br/>Derni&egrave;re v&eacute;rification&nbsp;: 2026-03-27]]></description>
      <Point>
        <coordinates>2.341361,48.880764,0</coordinates>
      </Point>
    </Placemark>
    <Placemark>
      <name>2 Steps Café</name>
      <description><![CDATA[<b>2 Steps Café</b><br/>Cat&eacute;gorie&nbsp;: Cafés ouverts tard<br/><a href="https://example.org/poi/3">Voir la fiche</a><br/>Derni&egrave;re v&eacute;rification&nbsp;: 2026-10-12]]></description>
      <Point>
        <coordinates>2.376626,48.866261,0</coordinates>
      </Point>
    </Placemark>
    <Placemark>
      <name>3234 Café - Trente Deux Trente Quatre</name>
      <description><![CDATA[<b>3234 Café - Trente Deux Trente Quatre</b><br/>Cat&eacute;gorie&nbsp;: Cafés ouverts tard<br/><a href="https://example.org/poi/4">Voir la fiche</a><br/>Derni&egrave;re v&eacute;rification&nbsp;: 2026-05-25]]></description>
      <Point>
        <coordinates>2.376249,48.853167,0</coordinates>
      </Point>
    </Placemark>
    <Placemark>
      <name>360 Cafe</name>
      <description><![CDATA[<b>360 Cafe</b><br/>Cat&eacute;gorie&nbsp;: Cafés ouverts tard<br/><a href="https://example.org/poi/5">Voir la fiche</a><br/>Derni&egrave;re v&eacute;rification&nbsp;: 2026-12-10]]></description>
      <Point>
        <coordinates>2.321838,48.842293,0</coordinates>
      </Point>
    </Placemark>
    <Placemark>
      <name>3w Cafe Paris</name>
1:00
Write /work/solve.py
content · 93 lines · py 
import geopandas as gpd
import pandas as pd
import re
from bs4 import BeautifulSoup
import json

def extract_info_from_description(description_html):
    """
    Parses the HTML description to extract category and verified_date.
    Example format:
    <b% Arabica</b><br/>Cat&eacute;gorie&nbsp;: Cafés ouverts tard<br/><a href="...">Voir la fiche</a><br/>Derni&egrave;re v&eacute;rification&nbsp;: 2026-01-01
    """
    # Decode HTML entities first (BeautifulSoup handles this well)
    soup = BeautifulSoup(description_html, 'html.parser')
    text = soup.get_text(separator='|') # Use a separator to avoid merging words
    
    # Let's look at the text specifically. 
    # The structure seems to be: Name | Catégorie : <category> | Voir la fiche | Dernière vérification : <date>
    # Note: BeautifulSoup get_text might lose some context if not careful, 
    # but since we are looking for specific labels, it should be fine.
    
    # Alternatively, just use regex on the raw HTML if it's predictable
    # Category extraction: Cat&eacute;gorie&nbsp;: (.*?)<br/>
    # Date extraction: Derni&egrave;re v&eacute;rification&nbsp;: (.*?)<br/> or similar
    
    # Let's try regex on the raw string as it might be more robust for these specific entities
    category = None
    verified_date = None
    
    # Try to find category
    cat_match = re.search(r'Cat&eacute;gorie&nbsp;:\s*(.*?)(?:<br\s*/?>|$)', description_html, re.IGNORECASE)
    if cat_match:
        category = cat_match.group(1).strip()
        # Clean up any remaining html entities if any
        category = BeautifulSoup(category, 'html.parser').get_text()

    # Try to find date
    # The date format in the sample is YYYY-MM-DD
    date_match = re.search(r'Derni&egrave;re v&eacute;rification&nbsp;:\s*(\d{4}-\d{2}-\d{2})', description_html, re.IGNORECASE)
    if date_match:
        verified_date = date_match.group(1)

    return category, verified_date

def process_kml(input_file, output_file):
    # Read KML file
    # KML is just XML, geopandas can read it if fiona is installed with KML support
    # If not, we might need to parse it manually.
    try:
        gdf = gpd.read_file(input_file)
    except Exception as e:
        print(f"Error reading KML: {e}")
        return

    print(f"Read {len(gdf)} features from {input_file}")

    categories = []
    verified_dates = []

    for idx, row in gdf.iterrows():
        desc = row.get('description', '')
        cat, date = extract_info_from_description(desc)
        categories.append(cat)
        verified_dates.append(date)

    # Add new columns
    gdf['category'] = categories
    gdf['verified_date'] = pd.to_datetime(verified_dates, errors='coerce')

    # The requirement: "Keep 'name', include... 'category' column, and ... 'verified_date' column"
    # It doesn't explicitly say to drop others, but usually "Keep X" implies a subset.
    # However, for a map server, we usually just want the essential attributes.
    # Let's keep name, category, verified_date and the geometry.
    
    # Check what columns we have
    print("Original columns:", gdf.columns.tolist())
    
    # Filter columns
    # We need to keep 'name' (if it exists in gdf), 'category', 'verified_date', and 'geometry'
    keep_cols = ['name', 'category', 'verified_date', 'geometry']
    # Ensure 'name' exists. In KML, it's usually a column.
    available_cols = [c for c in keep_cols if c in gdf.columns]
    
    final_gdf = gdf[available_cols].copy()

    # Save to GeoJSON
    final_gdf.to_file(output_file, driver='GeoJSON')
    print(f"Saved {len(final_gdf)} features to {output_file}")
    print("Columns in output:", final_gdf.columns.tolist())

if __name__ == "__main__":
    process_kml('paris_late_night_pois.kml', 'paris_pois.geojson')

Wrote /work/solve.py (3776 chars).
1:02
Bash python solve.py 
python solve.py

Traceback (most recent call last):
  File "/work/solve.py", line 4, in <module>
    from bs4 import BeautifulSoup
ModuleNotFoundError: No module named 'bs4'
1:06
Bash pip install --user beautifulsoup4 
pip install --user beautifulsoup4

Collecting beautifulsoup4
  Downloading beautifulsoup4-4.15.0-py3-none-any.whl.metadata (3.8 kB)
Collecting soupsieve>=1.6.1 (from beautifulsoup4)
  Downloading soupsieve-2.8.4-py3-none-any.whl.metadata (4.6 kB)
Collecting typing-extensions>=4.0.0 (from beautifulsoup4)
  Downloading typing_extensions-4.15.0-py3-none-any.whl.metadata (3.3 kB)
Downloading beautifulsoup4-4.15.0-py3-none-any.whl (109 kB)
Downloading soupsieve-2.8.4-py3-none-any.whl (37 kB)
Downloading typing_extensions-4.15.0-py3-none-any.whl (44 kB)
Installing collected packages: typing-extensions, soupsieve, beautifulsoup4

Successfully installed beautifulsoup4-4.15.0 soupsieve-2.8.4 typing-extensions-4.15.0
1:09
Bash python solve.py 
python solve.py

Read 20 features from paris_late_night_pois.kml
Original columns: ['id', 'Name', 'description', 'timestamp', 'begin', 'end', 'altitudeMode', 'tessellate', 'extrude', 'visibility', 'drawOrder', 'icon', 'geometry', 'category', 'verified_date']
Saved 20 features to paris_pois.geojson
Columns in output: ['category', 'verified_date', 'geometry']
/usr/local/lib/python3.14/site-packages/pyogrio/geopandas.py:382: UserWarning: More than one layer found in 'paris_late_night_pois.kml': 'Cafés ouverts tard' (default), 'Bibliothèques de nuit', 'Tours et infos touristiques'. Specify layer parameter to avoid this warning.
  result = read_func(
1:57
Write /work/solve.py
content · 96 lines · py 
import geopandas as gpd
import pandas as pd
import re
from bs4 import BeautifulSoup

def extract_info_from_description(description_html):
    """
    Parses the HTML description to extract category and verified_date.
    """
    if not description_html:
        return None, None
    
    # Using regex on raw HTML for robustness with entities
    # The description in KML often contains HTML entities like &eacute;
    
    category = None
    verified_date = None
    
    # Category: Cat&eacute;gorie&nbsp;: (.*?)<br/>
    # We'll look for "Catégorie" or its entity-encoded versions
    cat_match = re.search(r'Cat(?:&eacute;gorie|égorie)&nbsp;:\s*(.*?)(?:<br\s*/?>|$)', description_html, re.IGNORECASE)
    if cat_match:
        category = cat_match.group(1).strip()
        # Decode entities
        category = BeautifulSoup(category, 'html.parser').get_text()

    # Date: Derni&egrave;re v&eacute;rification&nbsp;: 2026-01-01
    date_match = re.search(r'Derni(?:&egrave;re|ère) v(?:&eacute;rification|érification)&nbsp;:\s*(\d{4}-\d{2}-\d{2})', description_html, re.IGNORECASE)
    if date_match:
        verified_date = date_match.group(1)

    return category, verified_date

def process_kml(input_file, output_file):
    # The KML has multiple layers. We want all of them? 
    # The prompt says "paris_late_night_pois", but the KML has multiple folders.
    # Let's try to read all layers if possible, or just realize the user probably wants all POIs in the file.
    # However, geopandas' read_file only reads one layer by default.
    # Let's use fiona to get all layers and combine them.
    import fiona
    
    all_gdfs = []
    layers = fiona.listlayers(input_file)
    print(f"Layers found: {layers}")
    
    for layer in layers:
        gdf = gpd.read_file(input_file, layer=layer)
        all_gdfs.append(gdf)
    
    if not all_gdfs:
        print("No layers found.")
        return
        
    gdf = pd.concat(all_gdfs, ignore_index=True)
    print(f"Combined {len(all_gdfs)} layers into {len(gdf)} features")

    # The KML column for name might be 'Name' or 'name'. 
    # Let's standardize.
    if 'Name' in gdf.columns and 'name' not in gdf.columns:
        gdf['name'] = gdf['Name']
    elif 'Name' in gdf.columns and 'name' in gdf.columns:
        # If both exist, name is usually preferred if it's lowercase, but let's check
        pass

    categories = []
    verified_dates = []

    for idx, row in gdf.iterrows():
        desc = row.get('description', '')
        cat, date = extract_info_from_description(desc)
        categories.append(cat)
        verified_dates.append(date)

    gdf['category'] = categories
    gdf['verified_date'] = pd.to_datetime(verified_dates, errors='coerce')

    # Final selection
    # We need to ensure 'name' is present.
    if 'name' not in gdf.columns:
        # Fallback to 'Name' if 'name' wasn't created
        gdf['name'] = gdf.get('Name', 'Unknown')

    keep_cols = ['name', 'category', 'verified_date', 'geometry']
    # Some columns might be missing if the KML is weird
    available_cols = [c for c in keep_cols if c in gdf.columns]
    
    final_gdf = gdf[available_cols].copy()

    # Save to GeoJSON
    final_gdf.to_file(output_file, driver='GeoJSON')
    print(f"Saved {len(final_gdf)} features to {output_file}")
    print("Columns in output:", final_gdf.columns.tolist())

if __name__ == "__main__":
    process_kml('paris_late_night_pois.kml', 'paris_pois.geojson')

Wrote /work/solve.py (3461 chars).
1:58
Bash python solve.py 
python solve.py

Layers found: ['Cafés ouverts tard', 'Bibliothèques de nuit', 'Tours et infos touristiques']
Combined 3 layers into 45 features
Saved 45 features to paris_pois.geojson
Columns in output: ['name', 'category', 'verified_date', 'geometry']
2:01
Bash head -n 20 paris_pois.geojson 
head -n 20 paris_pois.geojson

{
"type": "FeatureCollection",
"name": "paris_pois",
"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:OGC:1.3:CRS84" } },
"features": [
{ "type": "Feature", "properties": { "name": "% Arabica", "category": "Cafés ouverts tard", "verified_date": "2026-01-01T00:00:00" }, "geometry": { "type": "Point", "coordinates": [ 2.324676, 48.854156, 0.0 ] } },
{ "type": "Feature", "properties": { "name": "1T. rue Scribe", "category": "Cafés ouverts tard", "verified_date": "2026-08-14T00:00:00" }, "geometry": { "type": "Point", "coordinates": [ 2.329774, 48.870581, 0.0 ] } },
{ "type": "Feature", "properties": { "name": "1ndix Café", "category": "Cafés ouverts tard", "verified_date": "2026-03-27T00:00:00" }, "geometry": { "type": "Point", "coordinates": [ 2.341361, 48.880764, 0.0 ] } },
{ "type": "Feature", "properties": { "name": "2 Steps Café", "category": "Cafés ouverts tard", "verified_date": "2026-10-12T00:00:00" }, "geometry": { "type": "Point", "coordinates": [ 2.376626, 48.866261, 0.0 ] } },
{ "type": "Feature", "properties": { "name": "3234 Café - Trente Deux Trente Quatre", "category": "Cafés ouverts tard", "verified_date": "2026-05-25T00:00:00" }, "geometry": { "type": "Point", "coordinates": [ 2.376249, 48.853167, 0.0 ] } },
{ "type": "Feature", "properties": { "name": "360 Cafe", "category": "Cafés ouverts tard", "verified_date": "2026-12-10T00:00:00" }, "geometry": { "type": "Point", "coordinates": [ 2.321838, 48.842293, 0.0 ] } },
{ "type": "Feature", "properties": { "name": "3w Cafe Paris", "category": "Cafés ouverts tard", "verified_date": "2026-07-23T00:00:00" }, "geometry": { "type": "Point", "coordinates": [ 2.32753, 48.85156, 0.0 ] } },
{ "type": "Feature", "properties": { "name": "48 Collagen Caf√©", "category": "Cafés ouverts tard", "verified_date": "2026-02-08T00:00:00" }, "geometry": { "type": "Point", "coordinates": [ 2.34046, 48.87484, 0.0 ] } },
{ "type": "Feature", "properties": { "name": "86 Champs Elysées", "category": "Cafés ouverts tard", "verified_date": "2026-09-21T00:00:00" }, "geometry": { "type": "Point", "coordinates": [ 2.303767, 48.871387, 0.0 ] } },
{ "type": "Feature", "properties": { "name": "A la Ville de Paris", "category": "Cafés ouverts tard", "verified_date": "2026-04-06T00:00:00" }, "geometry": { "type": "Point", "coordinates": [ 2.34428, 48.87053, 0.0 ] } },
{ "type": "Feature", "properties": { "name": "A minima cafe", "category": "Cafés ouverts tard", "verified_date": "2026-11-19T00:00:00" }, "geometry": { "type": "Point", "coordinates": [ 2.38129, 48.8453, 0.0 ] } },
{ "type": "Feature", "properties": { "name": "AD DictCafé", "category": "Cafés ouverts tard", "verified_date": "2026-06-04T00:00:00" }, "geometry": { "type": "Point", "coordinates": [ 2.390577, 48.849002, 0.0 ] } },
{ "type": "Feature", "properties": { "name": "ANGIOLO", "category": "Cafés ouverts tard", "verified_date": "2026-01-17T00:00:00" }, "geometry": { "type": "Point", "coordinates": [ 2.331682, 48.854538, 0.0 ] } },
{ "type": "Feature", "properties": { "name": "Abaita", "category": "Cafés ouverts tard", "verified_date": "2026-08-02T00:00:00" }, "geometry": { "type": "Point", "coordinates": [ 2.368446, 48.864406, 0.0 ] } },
{ "type": "Feature", "properties": { "name": "Absinthe Café", "category": "Cafés ouverts tard", "verified_date": "2026-03-15T00:00:00" }, "geometry": { "type": "Point", "coordinates": [ 2.357228, 48.865564, 0.0 ] } },
2:09