Content repurposing

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import requests
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import time
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# ── Config ────────────────────────────────────────────────────
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base_url = "https://api.assemblyai.com"
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headers = {"authorization": "YOUR_API_KEY"}
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audio_url = "https://assembly.ai/wildfires.mp3"
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# ── Step 1: Transcribe with key phrases enabled ──
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data = {
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    "audio_url": audio_url,
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    "speech_models": ["universal-3-pro", "universal-2"],
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    "language_detection": True,
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    "auto_highlights": True,
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}
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response = requests.post(base_url + "/v2/transcript", headers=headers, json=data)
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response.raise_for_status()
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transcript_id = response.json()["id"]
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while True:
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    result = requests.get(f"{base_url}/v2/transcript/{transcript_id}", headers=headers).json()
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    if result["status"] == "completed":
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        break
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    elif result["status"] == "error":
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        raise RuntimeError(f"Transcription failed: {result['error']}")
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    time.sleep(3)
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# ── Step 2: Get paragraph-level content for structure ──
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paragraphs_response = requests.get(
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    f"{base_url}/v2/transcript/{transcript_id}/paragraphs", headers=headers
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)
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paragraphs = paragraphs_response.json()["paragraphs"]
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# ── Step 3: Extract top key phrases ──
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highlights = result.get("auto_highlights_result", {}).get("results", [])
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top_phrases = sorted(highlights, key=lambda x: x["rank"], reverse=True)[:10]
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phrases_list = ", ".join(p["text"] for p in top_phrases)
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# ── Step 4: Generate blog post via LLM Gateway ──
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transcript_text = result["text"]
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llm_response = requests.post(
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    "https://llm-gateway.assemblyai.com/v1/chat/completions",
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    headers=headers,
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    json={
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        "model": "claude-sonnet-4-5-20250929",
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        "messages": [
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            {
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                "role": "user",
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                "content": (
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                    "You are a content writer. Transform this transcript into an engaging "
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                    "blog post.\n\n"
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                    "Requirements:\n"
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                    "- Write a compelling title and subtitle\n"
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                    "- Break the content into 3-5 sections with headers\n"
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                    "- Weave in the key phrases naturally\n"
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                    "- Add a TL;DR at the top\n"
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                    "- End with a call-to-action\n\n"
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                    f"Key phrases: {phrases_list}\n\n"
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                    f"Transcript:\n{transcript_text}"
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                ),
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            }
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        ],
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        "max_tokens": 3000,
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    },
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)
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llm_response.raise_for_status()
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print("=== Blog Post Draft ===\n")
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print(llm_response.json()["choices"][0]["message"]["content"])