Python quickstart

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import os
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from hume import HumeClient
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client = HumeClient(api_key=os.getenv("HUME_API_KEY"))

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from hume.expression_measurement.batch.types import Models, Prosody
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job_id = client.expression_measurement.batch.start_inference_job(
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    urls=["https://hume-tutorials.s3.amazonaws.com/faces.zip"],
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    models=Models(
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        prosody=Prosody(granularity="utterance"),
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    ),
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)
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print(f"Job ID: {job_id}")

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import time
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while True:
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    job_details = client.expression_measurement.batch.get_job_details(id=job_id)
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    status = job_details.state.status
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    if status == "COMPLETED":
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        print("Job completed.")
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        break
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    elif status == "FAILED":
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        print("Job failed.")
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        break
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    print(f"Status: {status}")
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    time.sleep(3)

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predictions = client.expression_measurement.batch.get_job_predictions(id=job_id)
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for result in predictions:
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    source = result.source
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    print(f"\nSource: {source.url or source.filename}")
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    for file_prediction in result.results.predictions:
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        for group in file_prediction.models.prosody.grouped_predictions:
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            for prediction in group.predictions:
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                print(f"\n  Text: {prediction.text}")
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                top_emotions = sorted(prediction.emotions, key=lambda e: e.score, reverse=True)[:3]
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                for emotion in top_emotions:
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                    print(f"    {emotion.name}: {emotion.score:.3f}")

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import asyncio
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from hume import AsyncHumeClient
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from hume.expression_measurement.stream import Config, StreamLanguage
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async def stream_text():
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    client = AsyncHumeClient(api_key=os.getenv("HUME_API_KEY"))
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    async with client.expression_measurement.stream.connect() as socket:
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        result = await socket.send_text(
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            text="I am so excited to try this out!",
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            config=Config(
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                language=StreamLanguage(granularity="sentence"),
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            ),
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        )
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        language_predictions = result.language.predictions
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        for prediction in language_predictions:
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            print(f"Text: {prediction.text}")
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            top_emotions = sorted(prediction.emotions, key=lambda e: e.score, reverse=True)[:3]
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            for emotion in top_emotions:
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                print(f"  {emotion.name}: {emotion.score:.3f}")
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asyncio.run(stream_text())

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import asyncio
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from hume import AsyncHumeClient
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from hume.expression_measurement.stream import Config
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async def stream_file():
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    client = AsyncHumeClient(api_key=os.getenv("HUME_API_KEY"))
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    async with client.expression_measurement.stream.connect() as socket:
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        result = await socket.send_file(
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            "sample.mp3",
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            config=Config(prosody={}),
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        )
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        prosody_predictions = result.prosody.predictions
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        for prediction in prosody_predictions:
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            top_emotions = sorted(prediction.emotions, key=lambda e: e.score, reverse=True)[:3]
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            for emotion in top_emotions:
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                print(f"  {emotion.name}: {emotion.score:.3f}")
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asyncio.run(stream_file())