Trend-to-Content Pipeline

import os, requests, time

# --- Auth setup same as Job 1 ---

SUB = "marketing"
BRAND = "B2B marketing analytics platform. Audience: Marketing directors/VPs. Tone: Data-driven, practical, contrarian."

# 1. Rising posts
rising = [p["data"] for p in requests.get(f"{RD}/r/{SUB}/rising",
    headers=RD_H, params={"limit": 20, "raw_json": 1}).json()["data"]["children"]]

# 2. Brand alignment via Mave
aligned = []
for post in rising:
    check = requests.post(f"{MV_BASE}/mave/chat", headers=MV_H, json={
        "message": f"Evaluate trend for brand alignment (score 1-10).\n\nTREND: {post['title']}\nCONTEXT: {post.get('selftext','')[:300]}\nSUBREDDIT: /r/{SUB} | Score: {post.get('score',0)}\n\nBRAND: {BRAND}\n\nIf 7+: suggest angle + format. Estimate trend velocity."
    }).json()
    content = check.get("content", "")
    lines = [l for l in content.split("\n") if "/10" in l or "score" in l.lower()]
    try: score = int("".join(c for c in (lines[0] if lines else "0") if c.isdigit())[:2])
    except: score = 0
    if score >= 7:
        aligned.append({"title": post["title"], "score": post.get("score",0), "alignment": score, "angle": content[:300]})
    time.sleep(0.5)

print(f"Evaluated: {len(rising)} | Aligned: {len(aligned)}")

# 3. Generate content
for trend in aligned[:5]:
    gen = requests.post(f"{MV_BASE}/generations", headers=MV_H, json={
        "prompt": f"Write a LinkedIn article (600-800 words) based on:\n\nTREND: {trend['title']}\nANGLE: {trend['angle'][:200]}\n\nHook: Open with Reddit insight. Body: Data + frameworks. CTA: Free trial. Tone: data-driven, contrarian.",
    }).json()
    print(f"\n{'='*50}\nTREND: {trend['title']} | Alignment: {trend['alignment']}/10")
    print(gen.get("output", gen.get("content", ""))[:500])

// --- Auth setup same as Job 1 ---

const SUB = "marketing";
const BRAND = "B2B marketing analytics. Audience: Marketing directors/VPs. Tone: Data-driven, contrarian.";

// 1. Rising posts
const rising = (await (await fetch(`${RD}/r/${SUB}/rising?limit=20&raw_json=1`, { headers: RD_H })).json())
  .data.children.map(p => p.data);

// 2. Brand alignment
const aligned = [];
for (const post of rising) {
  const check = await fetch(`${MV_BASE}/mave/chat`, { method: "POST", headers: MV_H,
    body: JSON.stringify({ message: `Evaluate trend for brand alignment (1-10).\n\nTREND: ${post.title}\nCONTEXT: ${(post.selftext||"").slice(0,300)}\n\n${BRAND}\n\nIf 7+: suggest angle. Estimate velocity.` }),
  }).then(r => r.json());
  const line = (check.content || "").split("\n").find(l => l.includes("/10")) || "";
  const score = parseInt((line.match(/\d+/) || ["0"])[0], 10);
  if (score >= 7) aligned.push({ title: post.title, score: post.score||0, alignment: score, angle: (check.content||"").slice(0,300) });
  await new Promise(r => setTimeout(r, 500));
}

console.log(`Evaluated: ${rising.length} | Aligned: ${aligned.length}`);

// 3. Generate
for (const trend of aligned.slice(0, 5)) {
  const gen = await fetch(`${MV_BASE}/generations`, { method: "POST", headers: MV_H,
    body: JSON.stringify({ prompt: `LinkedIn article (600-800 words).\n\nTREND: ${trend.title}\nANGLE: ${trend.angle.slice(0,200)}\n\nHook with Reddit insight. Data + frameworks. CTA: free trial.` }),
  }).then(r => r.json());
  console.log(`\n${"=".repeat(50)}\nTREND: ${trend.title} | Alignment: ${trend.alignment}/10`);
  console.log((gen.output || gen.content || "").slice(0, 500));
}