import os, requests, time
YELP = os.environ["YELP_API_KEY"]
MV = os.environ["MAVERA_API_KEY"]
YELP_H = {"Authorization": f"Bearer {YELP}"}
MV_BASE = "https://app.mavera.io/api/v1"
MV_H = {"Authorization": f"Bearer {MV}", "Content-Type": "application/json"}
# 1. Pull top competitors
CATEGORY = "pizza"
LOCATION = "Brooklyn, NY"
r = requests.get("https://api.yelp.com/v3/businesses/search",
headers=YELP_H,
params={"term": CATEGORY, "location": LOCATION, "limit": 10,
"sort_by": "review_count"})
r.raise_for_status()
competitors = r.json().get("businesses", [])
# 2. Get detailed profiles
profiles = []
for biz in competitors[:6]:
detail = requests.get(f"https://api.yelp.com/v3/businesses/{biz['id']}",
headers=YELP_H).json()
revs = requests.get(f"https://api.yelp.com/v3/businesses/{biz['id']}/reviews",
headers=YELP_H).json().get("reviews", [])
profiles.append({
"name": detail.get("name", ""),
"rating": detail.get("rating", 0),
"reviews": detail.get("review_count", 0),
"price": detail.get("price", "N/A"),
"categories": ", ".join(c.get("title", "") for c in detail.get("categories", [])),
"hours": "Open" if not detail.get("is_closed") else "Closed",
"neighborhood": detail.get("location", {}).get("city", LOCATION),
"excerpt": revs[0].get("text", "")[:200] if revs else "No reviews",
})
time.sleep(0.5)
# 3. Create local consumer personas
CONSUMER_ARCHETYPES = [
{"name": "Busy Parent", "desc": "Family of 4, ordering takeout 3x/week. Values speed, portion size, kid-friendliness. Budget: $30-50/order."},
{"name": "Foodie Millennial", "desc": "Late 20s, Instagram-active. Values quality, ambiance, unique menu items. Willing to pay premium."},
{"name": "Budget College Student", "desc": "NYU student. Eats out daily. Values price, late hours, portion size. Budget: under $15."},
{"name": "Local Regular", "desc": "Lives in the neighborhood 10+ years. Values consistency, knowing the staff, supporting local. Goes weekly."},
]
persona_ids = []
for arch in CONSUMER_ARCHETYPES:
p = requests.post(f"{MV_BASE}/personas", headers=MV_H, json={
"name": f"Yelp Consumer: {arch['name']}",
"description": f"{arch['desc']} Location: {LOCATION}.",
"demographic": {"location": LOCATION},
"psychographic": {"dining_persona": arch["name"]},
}).json()
persona_ids.append({"id": p["id"], "name": arch["name"]})
time.sleep(0.2)
# 4. Build stimulus
competitor_cards = "\n\n".join(
f"({chr(65+i)}) {p['name']}\n"
f" Rating: {p['rating']}/5 ({p['reviews']} reviews) | Price: {p['price']}\n"
f" Categories: {p['categories']}\n"
f" Neighborhood: {p['neighborhood']}\n"
f" Recent review: \"{p['excerpt']}\""
for i, p in enumerate(profiles)
)
# 5. Focus Group
fg = requests.post(f"{MV_BASE}/focus-groups", headers=MV_H, json={
"name": f"{CATEGORY.title()} Preference Study — {LOCATION}",
"persona_ids": [p["id"] for p in persona_ids],
"questions": [
{"type": "ranking", "text": f"Rank these {CATEGORY} places from first choice to last:\n\n{competitor_cards}"},
"Explain why you ranked your #1 choice first. What specific attribute sealed it?",
"What would make you switch from your current favorite to a new option?",
"Describe your ideal ordering experience for this category in 2 sentences.",
"If a new place opened in your neighborhood, what ONE thing would make you try it?",
],
"context": f"Consumer preference study: {CATEGORY} in {LOCATION}.\n\n{competitor_cards}",
"responses_per_persona": 3,
}).json()
# 6. Poll
for _ in range(20):
time.sleep(5)
data = requests.get(f"{MV_BASE}/focus-groups/{fg['id']}", headers=MV_H).json()
if data.get("status") == "completed":
break
print(f"=== {CATEGORY.title()} Preference Study — {LOCATION} ===")
for resp in data.get("responses", []):
persona = next((p["name"] for p in persona_ids if p["id"] == resp.get("persona_id")), "?")
print(f"\n[{persona}] {resp.get('question','')[:60]}")
print(f" → {resp.get('answer','')[:300]}")
