import os, requests, time
X = os.environ["X_BEARER_TOKEN"]; MV = os.environ["MAVERA_API_KEY"]
X_BASE = "https://api.x.com/2"; MV_BASE = "https://app.mavera.io/api/v1"
X_H = {"Authorization": f"Bearer {X}"}
MV_H = {"Authorization": f"Bearer {MV}", "Content-Type": "application/json"}
TWEET_ID = "1234567890123456789"
# 1. Fetch first tweet + conversation_id
first = requests.get(f"{X_BASE}/tweets/{TWEET_ID}", headers=X_H,
params={"tweet.fields": "conversation_id,created_at,public_metrics,author_id",
"expansions": "author_id", "user.fields": "name,username"})
if first.status_code == 429:
time.sleep(int(first.headers.get("x-rate-limit-reset", time.time()+60)) - int(time.time()))
first = requests.get(f"{X_BASE}/tweets/{TWEET_ID}", headers=X_H,
params={"tweet.fields": "conversation_id,created_at,public_metrics,author_id",
"expansions": "author_id", "user.fields": "name,username"})
first.raise_for_status(); fd = first.json()
tweet = fd["data"]; conv_id = tweet.get("conversation_id", TWEET_ID)
author = fd.get("includes",{}).get("users",[{}])[0]
uname = author.get("username","unknown")
# 2. Fetch thread
chain = [tweet]; nt = None
for _ in range(5):
params = {"query": f"conversation_id:{conv_id} from:{uname}", "max_results": 100,
"tweet.fields": "created_at,public_metrics", "sort_order": "recency"}
if nt: params["next_token"] = nt
r = requests.get(f"{X_BASE}/tweets/search/recent", headers=X_H, params=params)
if r.status_code == 429:
time.sleep(int(r.headers.get("x-rate-limit-reset", time.time()+60)) - int(time.time()))
r = requests.get(f"{X_BASE}/tweets/search/recent", headers=X_H, params=params)
r.raise_for_status()
for t in r.json().get("data",[]):
if t["id"] != tweet["id"]: chain.append(t)
nt = r.json().get("meta",{}).get("next_token")
if not nt: break
time.sleep(1)
chain.sort(key=lambda t: t.get("created_at",""))
thread_text = "\n\n".join(f"Tweet {i+1}/{len(chain)}:\n{t['text']}" for i, t in enumerate(chain))
likes = sum(t.get("public_metrics",{}).get("like_count",0) for t in chain)
print(f"Thread: {len(chain)} tweets | {likes:,} likes by @{uname}")
# 3. Brand voice + Generate
bv = requests.post(f"{MV_BASE}/brand-voices", headers=MV_H, json={
"name": f"Thread Voice: @{uname}", "samples": [thread_text],
"description": f"Voice from {len(chain)}-tweet thread, {likes:,} likes.",
}).json()
blog = requests.post(f"{MV_BASE}/generations", headers=MV_H, json={
"brand_voice_id": bv["id"], "app_template": "blog_post",
"prompt": f"Convert this thread into a blog post.\n\nTHREAD BY @{uname} ({len(chain)} tweets, {likes:,} likes):\n\n{thread_text}\n\n"
"Title: SEO-friendly (<60 chars). Meta: <160 chars. Structure: intro → 4-6 H2 sections → conclusion + CTA. "
"Length: 1,200-1,800 words. Preserve thread energy. Expand each tweet with examples and data. "
"Include 2-3 placeholder internal links.",
}).json()
print(f"\n{'='*60}\nBLOG POST\n{'='*60}")
print(blog.get("output", blog.get("content",""))[:2500])
