Custom Topic Tags Using LLM Gateway

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import requests
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import time
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base_url = "https://api.assemblyai.com"
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headers = {"authorization": "<YOUR_API_KEY>"}
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# Step 1: Transcribe the audio
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audio_url = "https://storage.googleapis.com/aai-web-samples/meeting.mp4"
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data = {"audio_url": audio_url, "speech_models": ["universal-3-pro"]}
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response = requests.post(base_url + "/v2/transcript", json=data, headers=headers)
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transcript_id = response.json()['id']
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polling_endpoint = base_url + "/v2/transcript/" + transcript_id
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while True:
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    transcription_result = requests.get(polling_endpoint, headers=headers).json()
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    if transcription_result['status'] == 'completed':
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        break
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    elif transcription_result['status'] == 'error':
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        raise RuntimeError(f"Transcription failed: {transcription_result['error']}")
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    else:
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        time.sleep(3)
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# Step 2: Generate topic tags with LLM Gateway
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tag_list = {
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    'Sports': 'News and updates on various athletic events, teams, and sports personalities.',
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    'Politics': 'Coverage and discussion of government activities, policies, and political events.',
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    'Entertainment': 'Information on movies, music, television, celebrities, and arts.',
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    'Technology': 'News and reviews on gadgets, software, tech advancements, and trends.',
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    'Health': 'Articles focusing on medical news, wellness, and health-related topics.',
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    'Business': 'Updates on markets, industries, companies, and economic trends.',
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    'Science': 'News and insights into scientific discoveries, research, and innovations.',
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    'Education': 'Coverage of topics related to schools, educational policies, and learning.',
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    'Travel': 'Information on destinations, travel tips, and tourism news.',
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    'Lifestyle': 'Articles on fashion, hobbies, personal interests, and daily life.',
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    'Environment': 'News and discussion about environmental issues and sustainability.',
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    'Finance': 'Information on personal finance, investments, banking, and economic news.',
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    'World News': 'International news covering global events and issues.',
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    'Crime': 'Reports and updates on criminal activities, law enforcement, and legal cases.',
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    'Culture': 'Coverage of cultural events, traditions, and societal norms.'
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}
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prompt = f"""
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You are a helpful assistant designed to label video content with topic tags.
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I will give you a list of topics and definitions. Select the most relevant topic from the list. Return your selection and nothing else.
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<topics_list>
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{tag_list}
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</topics_list>
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"""
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llm_gateway_data = {
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    "model": "claude-sonnet-4-5-20250929",
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    "messages": [
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        {"role": "user", "content": f"{prompt}\n\n{{{{ transcript }}}}"}
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    ],
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    "transcript_id": transcript_id,
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    "max_tokens": 500
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}
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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=llm_gateway_data
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)
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result = response.json()["choices"][0]["message"]["content"]
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print(result.strip())

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import requests
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import time
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base_url = "https://api.assemblyai.com"
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headers = {"authorization": "<YOUR_API_KEY>"}
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# Transcribe the audio
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audio_url = "https://storage.googleapis.com/aai-web-samples/meeting.mp4"
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data = {"audio_url": audio_url, "speech_models": ["universal-3-pro"]}  # You can also use a URL to an audio or video file on the web
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response = requests.post(base_url + "/v2/transcript", json=data, headers=headers)
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transcript_id = response.json()['id']
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polling_endpoint = base_url + "/v2/transcript/" + transcript_id
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while True:
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    transcription_result = requests.get(polling_endpoint, headers=headers).json()
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    if transcription_result['status'] == 'completed':
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        break
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    elif transcription_result['status'] == 'error':
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        raise RuntimeError(f"Transcription failed: {transcription_result['error']}")
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    else:
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        time.sleep(3)

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tag_list = {
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    'Sports': 'News and updates on various athletic events, teams, and sports personalities.',
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    'Politics': 'Coverage and discussion of government activities, policies, and political events.',
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    'Entertainment': 'Information on movies, music, television, celebrities, and arts.',
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    'Technology': 'News and reviews on gadgets, software, tech advancements, and trends.',
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    'Health': 'Articles focusing on medical news, wellness, and health-related topics.',
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    'Business': 'Updates on markets, industries, companies, and economic trends.',
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    'Science': 'News and insights into scientific discoveries, research, and innovations.',
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    'Education': 'Coverage of topics related to schools, educational policies, and learning.',
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    'Travel': 'Information on destinations, travel tips, and tourism news.',
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    'Lifestyle': 'Articles on fashion, hobbies, personal interests, and daily life.',
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    'Environment': 'News and discussion about environmental issues and sustainability.',
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    'Finance': 'Information on personal finance, investments, banking, and economic news.',
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    'World News': 'International news covering global events and issues.',
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    'Crime': 'Reports and updates on criminal activities, law enforcement, and legal cases.',
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    'Culture': 'Coverage of cultural events, traditions, and societal norms.'
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}

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tag_list = {
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    'Account Issues': 'Problems related to user accounts, such as login difficulties or account access.',
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    'Technical Support': 'Inquiries regarding software or hardware functionality and troubleshooting.',
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    'Billing and Payments': 'Questions or problems about invoices, payments, or subscription plans.',
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    'Product Inquiry': 'Requests for information about product features, capabilities, or availability.',
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    'Service Disruption': 'Reports of outages or interruptions in service performance or availability.'
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}

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prompt = f"""
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You are a helpful assistant designed to label video content with topic tags.
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I will give you a list of topics and definitions. Select the most relevant topic from the list. Return your selection and nothing else.
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<topics_list>
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{tag_list}
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</topics_list>
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"""
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llm_gateway_data = {
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    "model": "claude-sonnet-4-5-20250929",
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    "messages": [
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        {"role": "user", "content": f"{prompt}\n\n{{{{ transcript }}}}"}
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    ],
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    "transcript_id": transcript_id,
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    "max_tokens": 500
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}
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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=llm_gateway_data
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)
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result = response.json()["choices"][0]["message"]["content"]
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print(result.strip())