from huggingface_hub import InferenceClient

client = InferenceClient(
    provider="hf-inference",
    api_key="hf_xxxxxxxxxxxxxxxxxxxxxxxx",
)

output = client.audio_classification("sample1.flac", model="ehcalabres/wav2vec2-lg-xlsr-en-speech-emotion-recognition")

from huggingface_hub import InferenceClient

client = InferenceClient(
    provider="hf-inference",
    api_key="hf_xxxxxxxxxxxxxxxxxxxxxxxx",
)

output = client.automatic_speech_recognition("sample1.flac", model="openai/whisper-large-v3-turbo")

from huggingface_hub import InferenceClient

client = InferenceClient(
    provider="hf-inference",
    api_key="hf_xxxxxxxxxxxxxxxxxxxxxxxx",
)

completion = client.chat.completions.create(
    model="Qwen/QwQ-32B",
    messages=[
        {
            "role": "user",
            "content": "What is the capital of France?"
        }
    ],
    max_tokens=500,
)

print(completion.choices[0].message)

from huggingface_hub import InferenceClient

client = InferenceClient(
    provider="hf-inference",
    api_key="hf_xxxxxxxxxxxxxxxxxxxxxxxx",
)

completion = client.chat.completions.create(
    model="google/gemma-3-27b-it",
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "type": "text",
                    "text": "Describe this image in one sentence."
                },
                {
                    "type": "image_url",
                    "image_url": {
                        "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
                    }
                }
            ]
        }
    ],
    max_tokens=500,
)

print(completion.choices[0].message)

from huggingface_hub import InferenceClient

client = InferenceClient(
    provider="hf-inference",
    api_key="hf_xxxxxxxxxxxxxxxxxxxxxxxx",
)

result = client.feature_extraction(
    inputs="Today is a sunny day and I will get some ice cream.",
    model="intfloat/multilingual-e5-large-instruct",
)

from huggingface_hub import InferenceClient

client = InferenceClient(
    provider="hf-inference",
    api_key="hf_xxxxxxxxxxxxxxxxxxxxxxxx",
)

result = client.fill_mask(
    inputs="The answer to the universe is undefined.",
    model="google-bert/bert-base-uncased",
)

from huggingface_hub import InferenceClient

client = InferenceClient(
    provider="hf-inference",
    api_key="hf_xxxxxxxxxxxxxxxxxxxxxxxx",
)

output = client.image_classification("cats.jpg", model="Falconsai/nsfw_image_detection")

from huggingface_hub import InferenceClient

client = InferenceClient(
    provider="hf-inference",
    api_key="hf_xxxxxxxxxxxxxxxxxxxxxxxx",
)

# output is a PIL.Image object
image = client.image_to_image(
    "cat.png",
    prompt="Turn the cat into a tiger.",
    model="enhanceaiteam/Flux-Uncensored-V2",
)

from huggingface_hub import InferenceClient

client = InferenceClient(
    provider="hf-inference",
    api_key="hf_xxxxxxxxxxxxxxxxxxxxxxxx",
)

output = client.object_detection("cats.jpg", model="facebook/detr-resnet-50")

from huggingface_hub import InferenceClient

client = InferenceClient(
    provider="hf-inference",
    api_key="hf_xxxxxxxxxxxxxxxxxxxxxxxx",
)

result = client.question_answering(
    inputs={
    "question": "What is my name?",
    "context": "My name is Clara and I live in Berkeley."
},
    model="deepset/gelectra-large-germanquad",
)

from huggingface_hub import InferenceClient

client = InferenceClient(
    provider="hf-inference",
    api_key="hf_xxxxxxxxxxxxxxxxxxxxxxxx",
)

result = client.summarization(
    inputs="The tower is 324 metres (1,063 ft) tall, about the same height as an 81-storey building, and the tallest structure in Paris. Its base is square, measuring 125 metres (410 ft) on each side. During its construction, the Eiffel Tower surpassed the Washington Monument to become the tallest man-made structure in the world, a title it held for 41 years until the Chrysler Building in New York City was finished in 1930. It was the first structure to reach a height of 300 metres. Due to the addition of a broadcasting aerial at the top of the tower in 1957, it is now taller than the Chrysler Building by 5.2 metres (17 ft). Excluding transmitters, the Eiffel Tower is the second tallest free-standing structure in France after the Millau Viaduct.",
    model="facebook/bart-large-cnn",
)

from huggingface_hub import InferenceClient

client = InferenceClient(
    provider="hf-inference",
    api_key="hf_xxxxxxxxxxxxxxxxxxxxxxxx",
)

result = client.text_classification(
    inputs="I like you. I love you",
    model="ProsusAI/finbert",
)

from huggingface_hub import InferenceClient

client = InferenceClient(
    provider="hf-inference",
    api_key="hf_xxxxxxxxxxxxxxxxxxxxxxxx",
)

completion = client.chat.completions.create(
    model="Qwen/QwQ-32B",
    messages="\"Can you please let us know more details about your \"",
    max_tokens=500,
)

print(completion.choices[0].message)

from huggingface_hub import InferenceClient

client = InferenceClient(
    provider="hf-inference",
    api_key="hf_xxxxxxxxxxxxxxxxxxxxxxxx",
)

# output is a PIL.Image object
image = client.text_to_image(
    "Astronaut riding a horse",
    model="black-forest-labs/FLUX.1-dev",
)

from huggingface_hub import InferenceClient

client = InferenceClient(
    provider="hf-inference",
    api_key="hf_xxxxxxxxxxxxxxxxxxxxxxxx",
)

video = client.text_to_video(
    "A young man walking on the street",
    model="AdamLucek/Wan2.1-T2V-14B-OldBookIllustrations",
)

from huggingface_hub import InferenceClient

client = InferenceClient(
    provider="hf-inference",
    api_key="hf_xxxxxxxxxxxxxxxxxxxxxxxx",
)

result = client.token_classification(
    inputs="My name is Sarah Jessica Parker but you can call me Jessica",
    model="dbmdz/bert-large-cased-finetuned-conll03-english",
)

from huggingface_hub import InferenceClient

client = InferenceClient(
    provider="hf-inference",
    api_key="hf_xxxxxxxxxxxxxxxxxxxxxxxx",
)

result = client.translation(
    inputs="Меня зовут Вольфганг и я живу в Берлине",
    model="facebook/nllb-200-distilled-600M",
)

import requests

API_URL = "https://router.huggingface.co/hf-inference/models/facebook/bart-large-mnli"
headers = {"Authorization": "Bearer hf_xxxxxxxxxxxxxxxxxxxxxxxx"}

def query(payload):
    response = requests.post(API_URL, headers=headers, json=payload)
    return response.json()

output = query({
    "inputs": "Hi, I recently bought a device from your company but it is not working as advertised and I would like to get reimbursed!",
    "parameters": {"candidate_labels": ["refund", "legal", "faq"]},
})