Turn a topic into a researched LinkedIn post

from clickruntime import on_event, credentials, http, ai, notify

STYLE = "Practical, first person, short paragraphs, no hashtags."

def post_to_linkedin(text):
    token = credentials.connection("linkedin", "work")
    me = http.get("https://api.linkedin.com/v2/userinfo", headers={"Authorization": f"Bearer {token}"}).json()["sub"]
    r = http.post("https://api.linkedin.com/rest/posts", headers={"Authorization": f"Bearer {token}", "LinkedIn-Version": "202409", "X-Restli-Protocol-Version": "2.0.0"},
                  json={"author": f"urn:li:person:{me}", "commentary": text, "visibility": "PUBLIC", "lifecycleState": "PUBLISHED",
                        "distribution": {"feedDistribution": "MAIN_FEED", "targetEntities": [], "thirdPartyDistributionChannels": []}})
    r.raise_for_status()
    return r.headers.get("x-restli-id", "")

@on_event
def handle(event):
    topic = ((event.json or {}).get("topic") or "").strip()
    if not topic:
        return {"error": "no topic"}
    results = http.get("https://serpapi.com/search", params={"engine": "google", "q": topic, "api_key": credentials.secret("SERPAPI_API_KEY")}).json().get("organic_results", [])[:6]
    facts = "\n".join(f"- {r['title']}: {r.get('snippet', '')}" for r in results)
    text = ai.generate(f"Topic: {topic}\nResults:\n{facts}\nWrite a LinkedIn post under 200 words.", system=f"{STYLE} Use only facts from the results.", max_output_tokens=400).text.strip()
    post_id = post_to_linkedin(text)
    notify.email_me(f"Posted on LinkedIn: {topic}", f"{text}\n\nhttps://www.linkedin.com/feed/update/{post_id}")
    return {"post": post_id}