Implement a Sales Playbook 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 sales call
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with open("./sales-call.mp3", "rb") as f:
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    response = requests.post(base_url + "/v2/upload", headers=headers, data=f)
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upload_url = response.json()["upload_url"]
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data = {"audio_url": upload_url}
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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: Evaluate with LLM Gateway
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context = "There are sales interactions between a salesperson who is selling an internet plan to customers who are warm leads."
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answer_format = """
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Answer with JSON in the following format:
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{
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    "Answer": "<answer_options>",
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    "Reason": "<justification for the answer in one sentence including quotes>"
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}
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"""
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questions = [
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    {
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        "question": "Did the salesperson start the conversation with a professional greeting?",
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        "answer_options": ["Poor", "Satisfactory", "Excellent"]
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    },
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    {
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        "question": "How well did the salesperson answer questions during the call?",
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        "answer_options": ["Poor", "Good", "Excellent"]
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    },
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    {
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        "question": "Did the salesperson discuss next steps clearly?",
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        "answer_options": ["Yes", "No"]
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    }
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]
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for q in questions:
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    prompt = f"""
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{q['question']}
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Context: {context}
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Answer Options: {', '.join(q['answer_options'])}
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{answer_format}
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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\nTranscript: {transcription_result['text']}"}
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        ],
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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(f"Question: {q['question']}")
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    print(f"Answer: {result}")
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    print()

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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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with open("./sales-call.mp3", "rb") as f:
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    response = requests.post(base_url + "/v2/upload", headers=headers, data=f)
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upload_url = response.json()["upload_url"]
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data = {"audio_url": upload_url}  # 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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        print(f"Transcription completed: {transcript_id}")
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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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context = "There are sales interactions between a salesperson who is selling an internet plan to customers who are warm leads."
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answer_format = """
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Answer with JSON in the following format:
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{
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    "Answer": "<answer_options>",
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    "Reason": "<justification for the answer in one sentence including quotes>"
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}
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"""

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questions = [
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    {
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        "question": "Did the salesperson start the conversation with a professional greeting?",
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        "answer_options": ["Poor", "Satisfactory", "Excellent"]
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    },
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    {
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        "question": "How well did the salesperson answer questions during the call?",
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        "answer_options": ["Poor", "Good", "Excellent"]
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    },
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    {
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        "question": "Did the salesperson discuss next steps clearly?",
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        "answer_options": ["Yes", "No"]
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    }
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]

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for q in questions:
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    prompt = f"""
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{q['question']}
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Context: {context}
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Answer Options: {', '.join(q['answer_options'])}
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{answer_format}
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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\nTranscript: {transcription_result['text']}"}
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        ],
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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(f"Question: {q['question']}")
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    print(f"Answer: {result}")
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    print()