Analyze The Sentiment Of A Customer Call using LLM Gateway

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import requests
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import time
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API_KEY = "YOUR_API_KEY"
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audio_file_path = "./meeting.mp3"
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# ------------------------------------------
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# Step 1: Upload the audio file
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# ------------------------------------------
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def upload_file(filename):
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    with open(filename, "rb") as f:
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        upload_url = "https://api.assemblyai.com/v2/upload"
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        headers = {"authorization": API_KEY}
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        response = requests.post(upload_url, headers=headers, data=f)
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        response.raise_for_status()
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        return response.json()["upload_url"]
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audio_url = upload_file(audio_file_path)
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print(f"Uploaded audio file. URL: {audio_url}")
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# ------------------------------------------
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# Step 2: Request transcription
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# ------------------------------------------
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transcript_request = requests.post(
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    "https://api.assemblyai.com/v2/transcript",
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    headers={"authorization": API_KEY, "content-type": "application/json"},
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    json={"audio_url": audio_url, "speech_models": ["universal-3-pro"]},
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)
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transcript_id = transcript_request.json()["id"]
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# Poll until completed
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while True:
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    polling_response = requests.get(
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        f"https://api.assemblyai.com/v2/transcript/{transcript_id}",
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        headers={"authorization": API_KEY},
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    )
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    status = polling_response.json()["status"]
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    if status == "completed":
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        transcript_text = polling_response.json()["text"]
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        break
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    elif status == "error":
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        raise RuntimeError(f"Transcription failed: {polling_response.json()['error']}")
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    else:
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        print(f"Transcription status: {status}")
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        time.sleep(3)
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print("\nTranscription complete.\n")
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# ------------------------------------------
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# Step 3: Define questions
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# ------------------------------------------
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agent_context = "The agent is trying to get the customer to go through with the update to their car."
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customer_context = "The customer is calling to check how much it would cost to update the map in his car."
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answer_format = "<answer in one sentence> <reason in one sentence>"
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questions = [
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    {
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        "question": "What was the overall sentiment of the call?",
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        "context": customer_context,
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        "answer_format": answer_format,
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    },
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    {
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        "question": "What was the sentiment of the agent in this call?",
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        "context": agent_context,
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        "answer_format": answer_format,
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    },
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    {
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        "question": "What was the sentiment of the customer in this call?",
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        "context": customer_context,
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        "answer_format": answer_format,
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    },
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    {
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        "question": "What quote best demonstrates the customer's level of interest?",
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        "context": customer_context,
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        "answer_format": answer_format,
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    },
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    {
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        "question": "Provide a quote from the agent that demonstrates their level of enthusiasm.",
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        "context": agent_context,
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        "answer_format": answer_format,
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    },
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]
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# ------------------------------------------
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# Step 4: Build prompt for the LLM
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# ------------------------------------------
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question_strs = []
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for q in questions:
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    q_str = f"Question: {q['question']}"
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    if q.get("context"):
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        q_str += f"\nContext: {q['context']}"
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    if q.get("answer_format"):
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        q_str += f"\nAnswer Format: {q['answer_format']}"
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    question_strs.append(q_str)
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questions_prompt = "\n\n".join(question_strs)
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prompt = f"""
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You are an expert at analyzing call transcripts.
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Given the series of questions below, answer them accurately and concisely.
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When context or answer format is provided, use it to guide your answers.
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Transcript:
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{transcript_text}
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Questions:
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{questions_prompt}
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"""
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# ------------------------------------------
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# Step 5: Query the LLM Gateway
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# ------------------------------------------
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headers = {"authorization": API_KEY}
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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={
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        "model": "claude-sonnet-4-5-20250929",
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        "messages": [{"role": "user", "content": prompt}],
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        "max_tokens": 2000,
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    },
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)
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response_json = response.json()
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llm_output = response_json["choices"][0]["message"]["content"]
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# ------------------------------------------
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# Step 6: Parse and display the results
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# ------------------------------------------
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print("\n--- LLM Responses ---\n")
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print(llm_output)

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import requests
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import time
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API_KEY = "YOUR_API_KEY"

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audio_file_path = "./meeting.mp3"
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# ------------------------------------------
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# Step 1: Upload the audio file
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# ------------------------------------------
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def upload_file(filename):
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    with open(filename, "rb") as f:
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        upload_url = "https://api.assemblyai.com/v2/upload"
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        headers = {"authorization": API_KEY}
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        response = requests.post(upload_url, headers=headers, data=f)
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        response.raise_for_status()
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        return response.json()["upload_url"]
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audio_url = upload_file(audio_file_path)
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print(f"Uploaded audio file. URL: {audio_url}")
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# ------------------------------------------
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# Step 2: Request transcription
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# ------------------------------------------
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transcript_request = requests.post(
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    "https://api.assemblyai.com/v2/transcript",
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    headers={"authorization": API_KEY, "content-type": "application/json"},
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    json={"audio_url": audio_url, "speech_models": ["universal-3-pro"]},
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)
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transcript_id = transcript_request.json()["id"]
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# Poll until completed
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while True:
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    polling_response = requests.get(
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        f"https://api.assemblyai.com/v2/transcript/{transcript_id}",
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        headers={"authorization": API_KEY},
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    )
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    status = polling_response.json()["status"]
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    if status == "completed":
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        transcript_text = polling_response.json()["text"]
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        break
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    elif status == "error":
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        raise RuntimeError(f"Transcription failed: {polling_response.json()['error']}")
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    else:
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        print(f"Transcription status: {status}")
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        time.sleep(3)
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print("\nTranscription complete.\n")

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agent_context = "The agent is trying to get the customer to go through with the update to their car."
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customer_context = "The customer is calling to check how much it would cost to update the map in his car."

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answer_format = "<answer in one sentence> <reason in one sentence>"
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questions = [
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    {
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        "question": "What was the overall sentiment of the call?",
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        "context": customer_context,
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        "answer_format": answer_format,
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    },
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    {
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        "question": "What was the sentiment of the agent in this call?",
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        "context": agent_context,
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        "answer_format": answer_format,
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    },
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    {
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        "question": "What was the sentiment of the customer in this call?",
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        "context": customer_context,
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        "answer_format": answer_format,
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    },
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    {
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        "question": "What quote best demonstrates the customer's level of interest?",
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        "context": customer_context,
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        "answer_format": answer_format,
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    },
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    {
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        "question": "Provide a quote from the agent that demonstrates their level of enthusiasm.",
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        "context": agent_context,
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        "answer_format": answer_format,
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    },
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]

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# ------------------------------------------
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# Step 4: Build prompt for the LLM
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# ------------------------------------------
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question_strs = []
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for q in questions:
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    q_str = f"Question: {q['question']}"
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    if q.get("context"):
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        q_str += f"\nContext: {q['context']}"
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    if q.get("answer_format"):
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        q_str += f"\nAnswer Format: {q['answer_format']}"
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    question_strs.append(q_str)
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questions_prompt = "\n\n".join(question_strs)
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prompt = f"""
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You are an expert at analyzing call transcripts.
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Given the series of questions below, answer them accurately and concisely.
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When context or answer format is provided, use it to guide your answers.
17
Transcript:
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{transcript_text}
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Questions:
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{questions_prompt}
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"""

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# ------------------------------------------
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# Step 5: Query the LLM Gateway
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# ------------------------------------------
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headers = {"authorization": API_KEY}
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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={
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        "model": "claude-sonnet-4-5-20250929",
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        "messages": [{"role": "user", "content": prompt}],
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        "max_tokens": 2000,
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    },
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)
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response_json = response.json()
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llm_output = response_json["choices"][0]["message"]["content"]
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# ------------------------------------------
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# Step 6: Parse and display the results
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# ------------------------------------------
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print("\n--- LLM Responses ---\n")
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print(llm_output)