Prompts to create a Google Maps lead scraper

Create a file called test_api.py that makes exactly ONE request to the Maps Data API to confirm my setup works without wasting my quota. It should: load RAPIDAPI_KEY and RAPIDAPI_HOST from .env; read the first coordinate from [COORDINATES_FILE]; call https://maps-data.p.rapidapi.com/searchmaps.php with query=[TEST_KEYWORD], limit=1, country=us, lang=en, offset=0, zoom=13, and that coordinate’s lat/​lng; send the key and host as the x‑rapidapi-key and x‑rapidapi-host headers; print the HTTP status and the first business name. If the status is 401/403, tell me my key is wrong; if 429, tell me I’ve hit my quota.

You are helping me build the first working version of a Google Maps lead scraper for local business prospecting. Create a Python command-line tool that takes a business search term, searches across a coordinate grid, calls the RapidAPI Maps Data API, deduplicates the results, and exports a clean CSV.

Coordinate file: [COORDINATES_FILE] in the project folder (~27,600 US rows). Columns: country code, postal code, City, State, State code, admin name2, admin code2, latitude, longitude. Inspect the file and match the columns tolerantly (case-insensitive).

API contract (use exactly this):

- Method/​URL: GET https://maps-data.p.rapidapi.com/searchmaps.php

- Headers: x‑rapidapi-key (read from env var RAPIDAPI_KEY) and x‑rapidapi-host (read from env var RAPIDAPI_HOST, default maps-data.p.rapidapi.com).

- Query params: query (the keyword), lat, lng, and defaults limit=20, country=us, lang=en, offset=0, zoom=13. Put these defaults in one CONFIG block at the top so I can edit them.

- Response JSON shape: {“data”: [ { …business… }, … ]}. Each business object has these fields (capture all that are present): business_​id, name, full_​address, phone_​number, website, rating, review_​count, timezone, place_​link, types (a list — join into a ​“category” column), price_​level, and state (an open/​closed text — output it as ​“open_​status” so it doesn’t clash with the US state).

Do not hardcode secrets. Read RAPIDAPI_KEY from the environment (it’s in .env). Create scraper.py and requirements.txt.

CLI flow: ask (1) the business keyword; (2) location scope — Entire US / specific state(s) / specific city/​cities; (3) if state scope, accept comma-separated state codes like TX,FL,NY; (4) if city scope, accept comma-separated city names. Filter the coordinate CSV to the chosen scope.

Use 30 parallel threads, process coordinates in batches of 100, and handle: timeouts, HTTP errors, rate limits (429) with exponential backoff, empty/​malformed responses, failed coordinates after max retries, and Ctrl‑C without losing collected results.

Deduplicate by business_​id when present; otherwise by a normalized combination of name + address + phone + website.

Output a CSV named after the query (e.g. restaurants_scraper.csv) with these columns: business_​id, name, category, full_​address, city, state, phone_​number, website, rating, review_​count, price_​level, open_​status, timezone, latitude, longitude, place_​link, source_​keyword, source_​city, source_​state, source_​lat, source_​lng. Also write failed_requests.csv with: keyword, source_​city, source_​state, source_​lat, source_​lng, error, attempts.

Print progress: coordinates selected, current batch, requests completed, raw results, deduped results, output path. Write clean modular functions: load coordinates, filter coordinates, call API, retry, parse response, deduplicate, save CSV, run CLI. After writing the code, give me the exact commands to install dependencies and run the scraper.

Run the scraper against [TEST_KEYWORD] in [CITY_OR_STATE] and show me how many coordinates failed. If failed_requests.csv has more than a few rows, look at the error column and tell me whether it’s a rate-limit issue, a timeout issue, or something else, then suggest a fix.

Update the scraper so it supports comma-separated keywords. Like if someone types “[KEYWORD_1], [KEYWORD_2], [KEYWORD_3]”, it should run a separate search for each keyword, then combine all the results into one CSV and remove duplicates across keywords.

After updating the code, tell me exactly how to run a multi-keyword search from the terminal.

Add support for excluding specific cities.

If the user selects a broad location scope, like an entire state or multiple states, ask whether they want to exclude any cities. The user should be able to enter comma-separated city names to exclude.

For example: [CITY_1], [CITY_2], [CITY_3]

Before scraping, remove any coordinate rows where the city matches one of the excluded cities. Make the matching case-insensitive and trim extra spaces.

After filtering, print how many coordinates were removed and how many coordinates remain.

Do not change the rest of the scraper behavior.

Add optional email enrichment to the scraper. For each business result that has a website, visit the site and try to find a public email address. Check the homepage plus a few common contact pages (/​contact, /contact-us, /​about). Extract visible email addresses with a regex, but filter out junk and placeholders — asset filenames (.png, .jpg, @2x), tracking SDKs (sentry, wixpress), and template placeholders (you@, your@, name@, sample@, example.com, @domain.com, @email.com, info@company). Put the best real email in a column called ​“email” and any others in ​“all_​emails”; leave blank if none found. Use short timeouts and error handling so broken or slow sites don’t stop the scraper, and run it across sites in parallel. Make it optional, since it makes the run slower.