Local Business Market Research with Structured Data

import requests, os, json
from collections import defaultdict

H = {"x-api-key": os.environ["SCAVIO_API_KEY"]}
BASE = "https://api.scavio.dev/api/v1/search"

def get_local_landscape(category: str, location: str) -> list[dict]:
    """Pull local pack results for a business category in a location."""
    query = f"{category} in {location}"
    resp = requests.post(BASE, headers=H,
        json={"platform": "google", "query": query}, timeout=10)
    data = resp.json()

    local_pack = data.get("local_pack", [])
    businesses = []
    for biz in local_pack:
        businesses.append({
            "name": biz.get("name"),
            "rating": biz.get("rating"),
            "reviews": biz.get("reviews"),
            "type": biz.get("type"),
            "address": biz.get("address"),
            "category": category,
            "location": location
        })
    return businesses

def build_landscape(categories: list[str], locations: list[str]) -> dict:
    """Build full competitive landscape across categories and areas."""
    landscape = defaultdict(list)

    for cat in categories:
        for loc in locations:
            businesses = get_local_landscape(cat, loc)
            landscape[cat].extend(businesses)

    # Analyze each category
    analysis = {}
    for cat, businesses in landscape.items():
        if not businesses:
            continue
        ratings = [b["rating"] for b in businesses if b["rating"]]
        reviews = [b["reviews"] for b in businesses if b["reviews"]]
        analysis[cat] = {
            "total_businesses": len(businesses),
            "avg_rating": round(sum(ratings) / len(ratings), 2) if ratings else 0,
            "avg_reviews": round(sum(reviews) / len(reviews)) if reviews else 0,
            "top_rated": sorted(businesses, key=lambda x: x.get("rating", 0), reverse=True)[:3],
            "most_reviewed": sorted(businesses, key=lambda x: x.get("reviews", 0), reverse=True)[:3],
            "low_competition": [b for b in businesses if (b.get("reviews") or 0) < 20]
        }
    return analysis

# Food & Beverage competitive analysis
fb_categories = ["coffee shop", "thai restaurant", "pizza delivery", "juice bar"]
austin_areas = ["downtown Austin TX", "South Congress Austin TX", "East Austin TX"]

landscape = build_landscape(fb_categories, austin_areas)

# Find underserved categories (low avg reviews = less established competition)
for cat, data in landscape.items():
    if data["avg_reviews"] < 50:
        print(f"Opportunity: {cat} - avg only {data['avg_reviews']} reviews")
        print(f"  Low-competition spots: {len(data['low_competition'])}")