Sora Video Generation → Analysis Loop

import os, requests, time, base64, re
from openai import OpenAI

client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])
MV = os.environ["MAVERA_API_KEY"]
MV_BASE = "https://app.mavera.io/api/v1"
MV_H = {"Authorization": f"Bearer {MV}", "Content-Type": "application/json"}

os.makedirs("sora_output", exist_ok=True)
MAX_ITERATIONS = 3
QUALITY_THRESHOLD = 8

# 1. Generate video script with Mavera
script_gen = requests.post(f"{MV_BASE}/generations", headers=MV_H, json={
    "prompt": "Write a detailed 15-second video prompt for Sora. "
        "Product: SaaS marketing analytics dashboard. "
        "Style: Clean, modern, cinematic. Show the dashboard in use — "
        "charts animating upward, a milestone notification popping. "
        "End on a wide shot with the tagline visible. "
        "Include camera angles, lighting, transitions.",
}).json()
script = script_gen.get("output") or script_gen.get("content") or ""
print(f"Script generated: {len(script)} chars")
time.sleep(2)

for iteration in range(1, MAX_ITERATIONS + 1):
    print(f"\n{'='*60}\nITERATION {iteration}\n{'='*60}")

    # 2. Generate video with Sora
    try:
        sora_resp = client.images.generate(model="sora", prompt=script, n=1, size="1792x1024")
        video_url = sora_resp.data[0].url
        video_b64 = sora_resp.data[0].b64_json
        output_path = f"sora_output/video_v{iteration}.mp4"

        if video_b64:
            with open(output_path, "wb") as f:
                f.write(base64.b64decode(video_b64))
            print(f"Video saved: {output_path} ({os.path.getsize(output_path) // 1024} KB)")
        elif video_url:
            vid_data = requests.get(video_url).content
            with open(output_path, "wb") as f:
                f.write(vid_data)
            print(f"Video downloaded: {output_path} ({len(vid_data) // 1024} KB)")
    except Exception as e:
        print(f"Sora error: {e} — retrying...")
        time.sleep(5)
        continue

    time.sleep(2)

    # 3. Analyze with Mavera
    analysis = requests.post(f"{MV_BASE}/mave/chat", headers=MV_H, json={
        "message": f"Video quality assessor. Iteration {iteration}.\n\n"
            f"SCRIPT:\n{script[:3000]}\n\n"
            "Score 1-10 on: BRAND ALIGNMENT, NARRATIVE COHERENCE, "
            "VISUAL QUALITY, EMOTIONAL IMPACT, CTA CLARITY.\n"
            "Give OVERALL SCORE (1-10) and SPECIFIC IMPROVEMENTS as a refined Sora prompt.",
    }).json()

    analysis_text = analysis.get("content", "")
    print(analysis_text[:2000])

    score_line = [l for l in analysis_text.split("\n") if "OVERALL" in l.upper()]
    nums = re.findall(r'\b(\d+)\b', score_line[0]) if score_line else []
    score = int(nums[0]) if nums else 0
    print(f"\nScore: {score}/10 (threshold: {QUALITY_THRESHOLD})")

    if score >= QUALITY_THRESHOLD:
        print(f"Quality met at iteration {iteration}. Final: {output_path}")
        break

    # 4. Refine prompt
    refined = [l for l in analysis_text.split("\n") if "prompt" in l.lower() or "refine" in l.lower()]
    if refined:
        script += "\n\nRefinements: " + " ".join(refined)[:500]
    time.sleep(3)
else:
    print(f"Max iterations ({MAX_ITERATIONS}) reached.")

import OpenAI from "openai";
import fs from "fs";

const client = new OpenAI({ apiKey: process.env.OPENAI_API_KEY });
const MV = process.env.MAVERA_API_KEY;
const MV_BASE = "https://app.mavera.io/api/v1";
const MV_H = { Authorization: `Bearer ${MV}`, "Content-Type": "application/json" };
fs.mkdirSync("sora_output", { recursive: true });

const scriptGen = await fetch(`${MV_BASE}/generations`, { method: "POST", headers: MV_H,
  body: JSON.stringify({ prompt: "Write a detailed 15-second Sora video prompt. Product: SaaS analytics "
    + "dashboard. Clean, cinematic. Dashboard in use, charts animating. End with tagline." }),
}).then(r => r.json());
let script = scriptGen.output || scriptGen.content || "";
await new Promise(r => setTimeout(r, 2000));

for (let iter = 1; iter <= 3; iter++) {
  const outPath = `sora_output/video_v${iter}.mp4`;
  try {
    const resp = await client.images.generate({ model: "sora", prompt: script, n: 1, size: "1792x1024" });
    const data = resp.data[0];
    if (data.b64_json) fs.writeFileSync(outPath, Buffer.from(data.b64_json, "base64"));
    else if (data.url) fs.writeFileSync(outPath, Buffer.from(await (await fetch(data.url)).arrayBuffer()));
    console.log(`Video saved: ${outPath}`);
  } catch (e) { console.log(`Sora error: ${e.message}`); await new Promise(r => setTimeout(r, 5000)); continue; }
  await new Promise(r => setTimeout(r, 2000));

  const analysis = await fetch(`${MV_BASE}/mave/chat`, { method: "POST", headers: MV_H,
    body: JSON.stringify({ message: `Video assessor. Iteration ${iter}.\nSCRIPT:\n${script.slice(0, 3000)}\n\n`
      + "Score 1-10: BRAND ALIGNMENT, NARRATIVE, QUALITY, IMPACT, CTA. OVERALL SCORE + IMPROVEMENTS." }),
  }).then(r => r.json());

  const text = analysis.content || "";
  console.log(text.slice(0, 2000));
  const score = parseInt((text.split("\n").find(l => /OVERALL/i.test(l))?.match(/\b(\d+)\b/) || [])[1] || "0");
  if (score >= 8) { console.log(`Quality met. Final: ${outPath}`); break; }
  const refined = text.split("\n").filter(l => /prompt|refine/i.test(l));
  if (refined.length) script += "\n\nRefinements: " + refined.join(" ").slice(0, 500);
  await new Promise(r => setTimeout(r, 3000));
}