Rapid Prototyping: Build a Micro-App that Scrapes Restaurant Picks from Group Chats

Hook — Stop deciding where to eat; automate the decision

Every dev or team lead has been in a group chat that takes 20+ minutes and 30 messages to land on a restaurant. If you're building scraping workflows for analytics or internal tools, you already know the technical pain points: anti-bot defenses, rate limits, frequent front-end changes. If you're a non-dev creator or product manager, your pain is UX and speed — you want a lightweight way to convert chat suggestions and local listings into an ordered shortlist. This guide shows how to prototype a dining app micro-app that ingests group chat suggestions and local listings, merges them, ranks restaurants, and presents them in a simple UI. We'll focus on practical Playwright recipes, alternative stacks (Scrapy, Puppeteer, HTTP clients), UX patterns for non-developers, and 2026 trends affecting scraping and privacy.

Why this micro-app pattern matters in 2026

Micro apps—small, single-purpose applications owned by individuals or small teams—have surged. The “vibe-coding” era (where creators rapidly assemble apps using AI and glue code) continues to accelerate because of better browser automation, low-code UIs, and affordable managed proxies. In late 2025 and early 2026, three trends sharpened this use case:

• Stronger anti-bot defenses: Many large web properties improved fingerprinting and bot detection. That changes how you design resilient scrapers.
• More accessible APIs and listing data: Places and review APIs are common, but often cost money—hybrid strategies mixing APIs and selective scraping are popular.
• Non-dev creator tooling: No-code front-ends, chat export parsers, and small serverless backends make micro-apps accessible to non-developers.

“Once vibe-coding apps emerged, I started hearing about people with no tech backgrounds successfully building their own apps.” — Rebecca Yu, on building Where2Eat

High-level architecture

Keep the prototype lean. The pattern below balances reliability and speed:

1. Ingest: chat exports (file upload) OR live scrape of web chat (Playwright)
2. Enrich: match suggested names to local listings via Places API or Scrapy crawl
3. Normalize: canonicalize names, geocode addresses, deduplicate
4. Rank: basic scoring (friend votes, rating, distance, price)
5. Present: simple UI or shareable shortlist (mobile first)

Playwright recipe: scrape suggestions from a web group chat (practical)

Playwright is an excellent choice for rapid prototyping because it controls modern browsers, supports multiple languages, and has good dev ergonomics. Below is a safe, practical Node.js recipe: it shows how to load a web chat, wait for message nodes, and extract candidate suggestions. Important: only scrape chats you own or have consent to process; respect platform terms.

When to use this

• You control the account (personal WhatsApp Web, Telegram Web, Slack workspace you own).
• Chat export is not available or you want live extraction for a micro-app UX.

// playwright-scrape-chat.js
const { chromium } = require('playwright');
const fs = require('fs');

(async () => {
  const browser = await chromium.launch({ headless: true });
  const context = await browser.newContext();
  const page = await context.newPage();

  // Example: WhatsApp Web (requires manual QR login first for a headful session)
  await page.goto('https://web.whatsapp.com', { waitUntil: 'networkidle' });

  // Wait for the chat list to render
  await page.waitForSelector('[data-testid="chat-list"]', { timeout: 60000 });

  // Replace with selector for the specific chat item and click to open
  await page.click('text=My Friends Group');
  await page.waitForSelector('.copyable-text', { timeout: 15000 });

  // Scrape last N messages
  const messages = await page.$$eval('.copyable-text', nodes =>
    nodes.slice(-200).map(n => ({
      text: n.innerText,
      timestamp: n.getAttribute('data-pre-plain-text') || ''
    }))
  );

  // Simple filter for likely restaurant lines: capitalized tokens or contains cuisine keywords
  const cuisineKeywords = ['sushi','taco','pizza','bbq','ramen','bistro','cafe','bar','grill'];
  const candidates = messages
    .map(m => m.text)
    .filter(t => t && (/[A-Z][a-z]{2,}/.test(t) || cuisineKeywords.some(k => t.toLowerCase().includes(k))))
    .slice(-50);

  fs.writeFileSync('candidates.json', JSON.stringify({ candidates, scrapedAt: new Date() }, null, 2));
  await browser.close();
  console.log('Saved candidates.json');
})();

Notes:

• For headless auth, use persistent contexts with saved profiles or instruct users to scan a QR and save cookies to reuse sessions.
• Increase robustness by using semantic selectors specific to the chat app. DOM changes are common—plan for quick selector updates.

Alternative: Parse uploaded chat exports (recommended for non-dev creators)

For creators who don't want the friction of browser automation, allow users to upload exported chat text (WhatsApp .txt, Slack JSON). Parsing is straightforward and far easier to keep compliant because it uses user-provided files.

// parse-whatsapp-export.js (Node)
const fs = require('fs');
const text = fs.readFileSync('WhatsAppChat.txt', 'utf8');

// WhatsApp exports often start lines like "[12/31/20, 10:02 PM] Name: Message"
const lines = text.split(/\n+/);
const suggestions = lines
  .filter(l => /:\s/.test(l))
  .map(l => l.split(/:\s(.+)/).slice(1).join(': '))
  .filter(Boolean)
  .filter(l => /\b(pizza|sushi|tacos|ramen|place|restaurant|cafe)\b/i)
  .slice(-100);

console.log(suggestions);

Enrich candidates against local listings

Once you have a list of candidate strings, match them to canonical local listings. Options:

• Prefer official APIs: Google Places, Yelp Fusion, Foursquare. These provide stable IDs, addresses, ratings, and photos.
• Fallback scraping: If an item doesn't match API results (rare), use a focused Scrapy spider or a Playwright fetch to target the restaurant web page or review aggregator.
• Geolocation: narrow results by user's city bounding box to reduce ambiguity.

// simple fetch to Google Places (pseudo)
// Use environment variables for API keys and respect quotas.
const fetch = require('node-fetch');
async function searchPlace(query, lat, lng) {
  const params = new URLSearchParams({
    input: query,
    inputtype: 'textquery',
    fields: 'place_id,name,formatted_address,rating,geometry',
    key: process.env.GOOGLE_PLACES_KEY,
    locationbias: `point:${lat},${lng}`
  });
  const res = await fetch(`https://maps.googleapis.com/maps/api/place/findplacefromtext/json?${params}`);
  return res.json();
}

Scrapy cookbook: crawl local listing pages for extra metadata

If you need deep crawling (menus, full reviews) and want to scale a crawl, Scrapy is efficient. Use Scrapy for batch backfills or if you have a list of listing URLs to scrape. Keep crawls narrow and cache results.

# scrapy spider: listings_spider.py
import scrapy

class ListingSpider(scrapy.Spider):
    name = 'listings'

    start_urls = ['https://example-city-guide.com/restaurants']

    def parse(self, response):
        for card in response.css('.restaurant-card'):
            yield {
                'name': card.css('.name::text').get(),
                'url': card.css('a::attr(href)').get(),
                'rating': card.css('.rating::text').get(),
                'cuisine': card.css('.cuisine::text').get(),
            }
        next_page = response.css('a.next::attr(href)').get()
        if next_page:
            yield response.follow(next_page, self.parse)

Deduplication and canonicalization

Expect duplicates: “Joe’s Pizza”, “Joes Pizza NYC”, “Joe's Pizzeria”. Strategy:

• Normalize names: lowercase, strip punctuation, remove stopwords (the, cafe, restaurant).
• Fuzzy match: use token-set ratio (fuzzywuzzy) or trigram similarity.
• Location tie-breaker: geocode addresses; if two candidates are within 200m, merge.

Ranking model for a micro-app

Start simple and iterate. Example weighted score:

• Friend votes / mentions: 40%
• Average rating (from API): 30%
• Distance from user: 20%
• Price / dietary fit: 10%

Allow quick toggles: "Near me", "Open now", "Budget", "Vegan options". That keeps the UX simple for non-devs.

Anti-blocking & resilience strategies (practical)

In 2026, anti-bot defenses are more sophisticated. Use these patterns to keep your prototype reliable without escalating to risky behavior:

• Prefer APIs for high-value sources (Google, Yelp). Reserve scraping for edge cases.
• Cache aggressively—store search results for 24–72 hours.
• Use polite crawling: respect robots.txt, throttle requests, add jitter.
• Rotate identities carefully: user-agent pools, but avoid heavy fingerprint spoofing unless you run legal/compliance review.
• Managed proxy providers: use residential or ISP proxies sparingly and within terms.

Privacy, compliance, and ethical notes

Important: group chat scraping intersects with privacy and platform ToS. Best practices:

• Only process chats you own or have explicit consent for.
• Anonymize personal names when storing or sharing results.
• Provide an opt-out for chat members.
• For EU/UK users, ensure data handling aligns with GDPR (legal basis, data retention, deletion endpoints).
• Document data sources and keep an audit log for scraping operations.

UX notes for non-dev creators (build for quick wins)

Non-dev creators need a simple flow. These UX decisions helped micro-apps like Where2Eat succeed:

• File-first onboarding: let users upload chat exports or paste messages; fewer auth hurdles.
• One-click shortlist: show top 3 picks with reason badges ("Most mentioned", "Best rated", "Closest").
• Editable vote deck: allow group members to quickly upvote or pass on suggestions.
• Shareable snapshot: generate a short link or small printable card for the table decision.
• Explainability: show why an item ranked well—mentions count, rating, distance.

Developer-facing considerations

Prototype first, then harden. Short checklist:

• Store results in a simple DB (SQLite for local prototypes; PostgreSQL for multi-user).
• Use a queue (RabbitMQ/SQS) for enrichment jobs so the UI responds fast.
• Expose a simple API for the UI: /upload-chat, /suggestions, /vote.
• Instrument metrics: scrape success rate, enrichment failures, latency.

Scaling and cost control

As usage grows, control costs:

• Cache API results and only call Places/Yelp when needed.
• Batch enrichment jobs during off-peak to use cheaper compute.
• Limit live scraping; prefer user-submitted files for scale.

Case study — prototype timeline (realistic)

From experience: a two-day proof-of-concept is realistic if you scope to file upload + Places API. Example timeline:

1. Day 1: UI + upload parser + candidate extraction
2. Day 2: Enrichment via Places API + naive ranking + shareable UI
3. Week 2: Add Playwright live mode, caching, and opt-in group auth

Future predictions: what to watch in 2026+

• Generative matching: LLMs will continue to improve fuzzy matching between casual chat text and canonical business names—expect plug-and-play LLM-based resolvers.
• Edge scraping: the push to serverless edge functions will reduce latency for enrichment jobs but also require careful secret management.
• Privacy-first designs: more micro-apps will default to local processing (on-device or client-side parsing) to avoid sharing chat content server-side.
• Stricter platform policies: platform owners will formalize rules around automated access for personal bots—expect more opt-in APIs for user-owned data.

Quick reference: stack recommendations

• Browser automation: Playwright (Node/Python) for rapid prototypes, Puppeteer as a lighter alternative.
• Crawling: Scrapy for batch crawls and extraction pipelines.
• APIs: Google Places, Yelp Fusion, Foursquare for canonical business data.
• Storage: SQLite for single-user, Postgres for multi-user apps.
• Queueing: SQS/RabbitMQ for enrichment tasks.

Actionable takeaways

• Start with file uploads for the lowest friction and best compliance path.
• Use Playwright for live extraction when you control the account and need UI-driven data.
• Enrich via official APIs first to avoid fragile scraping; reserve Scrapy for deep crawls.
• Design the UX around few clicks: upload > shortlist > share.
• Respect privacy — get consent, anonymize, and allow deletion.

Final notes and next steps

Building a dining micro-app is an excellent starter project for scraping stacks because the domain is bounded and the signal (restaurant names) is clear. Start simple: parse chat exports, enrich with a Places API, and present a ranked shortlist. Add Playwright live scraping later if you need it. As you scale, monitor anti-bot signals and prioritize user privacy.

Try this now

Clone a small repo that implements chat upload + Places API enrichment, or run the Playwright script above against a test chat you control. Keep iterations tight: ship a single-search shortlist and get feedback from real users in the group.

Call to action

Ready to prototype your dining micro-app? Download the sample repo, import a chat export, and run the Playwright recipe. If you want a checklist or a one-page template (upload endpoint, enrichment worker, ranking logic, share UI), get the starter kit and sample UI wireframes we've prepared for creators and dev teams—perfect for non-dev founders who want to launch fast.

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