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Remove HfFolder and InferenceAPI classes (#3344)
* Remove HfFolder * Remove InferenceAPI * more recent gradio * bump pytest * fix python 3.9? * install gradio only on python 3.10+ * fix tests * fix tests * fix
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‎docs/source/de/guides/inference.md‎

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@@ -8,7 +8,6 @@ Inferenz ist der Prozess, bei dem ein trainiertes Modell verwendet wird, um Vorh
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- [Inferenz API](https://huggingface.co/docs/api-inference/index): ein Service, der Ihnen ermöglicht, beschleunigte Inferenz auf der Infrastruktur von Hugging Face kostenlos auszuführen. Dieser Service ist eine schnelle Möglichkeit, um anzufangen, verschiedene Modelle zu testen und AI-Produkte zu prototypisieren.
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- [Inferenz Endpunkte](https://huggingface.co/inference-endpoints/index): ein Produkt zur einfachen Bereitstellung von Modellen im Produktivbetrieb. Die Inferenz wird von Hugging Face in einer dedizierten, vollständig verwalteten Infrastruktur auf einem Cloud-Anbieter Ihrer Wahl durchgeführt.
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Diese Dienste können mit dem [`InferenceClient`] Objekt aufgerufen werden. Dieser fungiert als Ersatz für den älteren [`InferenceApi`] Client und fügt spezielle Unterstützung für Aufgaben und das Ausführen von Inferenz hinzu, sowohl auf [Inferenz API](https://huggingface.co/docs/api-inference/index) als auch auf [Inferenz Endpunkten](https://huggingface.co/docs/inference-endpoints/index). Im Abschnitt [Legacy InferenceAPI client](#legacy-inferenceapi-client) erfahren Sie, wie Sie zum neuen Client migrieren können.
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> [!TIP]
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> [`InferenceClient`] ist ein Python-Client, der HTTP-Anfragen an unsere APIs stellt. Wenn Sie die HTTP-Anfragen direkt mit Ihrem bevorzugten Tool (curl, postman,...) durchführen möchten, lesen Sie bitte die Dokumentationsseiten der [Inferenz API](https://huggingface.co/docs/api-inference/index) oder der [Inferenz Endpunkte](https://huggingface.co/docs/inference-endpoints/index).
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Das Ziel von [`InferenceClient`] ist es, die einfachste Schnittstelle zum Ausführen von Inferenzen auf Hugging Face-Modellen bereitzustellen. Es verfügt über eine einfache API, die die gebräuchlichsten Aufgaben unterstützt. Hier ist eine Liste der derzeit unterstützten Aufgaben:
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| Domäne | Aufgabe | Unterstützt | Dokumentation |
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|--------|--------------------------------|--------------|------------------------------------|
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| Audio | [Audio Classification](https://huggingface.co/tasks/audio-classification) | ✅ | [`~InferenceClient.audio_classification`] |
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| | [Automatic Speech Recognition](https://huggingface.co/tasks/automatic-speech-recognition) | ✅ | [`~InferenceClient.automatic_speech_recognition`] |
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| | [Text-to-Speech](https://huggingface.co/tasks/text-to-speech) | ✅ | [`~InferenceClient.text_to_speech`] |
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| Computer Vision | [Image Classification](https://huggingface.co/tasks/image-classification) | ✅ | [`~InferenceClient.image_classification`] |
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| | [Image Segmentation](https://huggingface.co/tasks/image-segmentation) | ✅ | [`~InferenceClient.image_segmentation`] |
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| | [Image-to-Image](https://huggingface.co/tasks/image-to-image) | ✅ | [`~InferenceClient.image_to_image`] |
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| | [Image-to-Text](https://huggingface.co/tasks/image-to-text) | ✅ | [`~InferenceClient.image_to_text`] |
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| | [Object Detection](https://huggingface.co/tasks/object-detection) | ✅ | [`~InferenceClient.object_detection`] |
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| | [Text-to-Image](https://huggingface.co/tasks/text-to-image) | ✅ | [`~InferenceClient.text_to_image`] |
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| | [Zero-Shot-Image-Classification](https://huggingface.co/tasks/zero-shot-image-classification) | ✅ | [`~InferenceClient.zero_shot_image_classification`] |
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| Multimodal | [Documentation Question Answering](https://huggingface.co/tasks/document-question-answering) | ✅ | [`~InferenceClient.document_question_answering`] |
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| | [Visual Question Answering](https://huggingface.co/tasks/visual-question-answering) | ✅ | [`~InferenceClient.visual_question_answering`] |
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| NLP | [Conversational](https://huggingface.co/tasks/conversational) | ✅ | [`~InferenceClient.conversational`] |
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| | [Feature Extraction](https://huggingface.co/tasks/feature-extraction) | ✅ | [`~InferenceClient.feature_extraction`] |
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| | [Fill Mask](https://huggingface.co/tasks/fill-mask) | ✅ | [`~InferenceClient.fill_mask`] |
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| | [Question Answering](https://huggingface.co/tasks/question-answering) | ✅ | [`~InferenceClient.question_answering`] |
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| | [Sentence Similarity](https://huggingface.co/tasks/sentence-similarity) | ✅ | [`~InferenceClient.sentence_similarity`] |
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| | [Summarization](https://huggingface.co/tasks/summarization) | ✅ | [`~InferenceClient.summarization`] |
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| | [Table Question Answering](https://huggingface.co/tasks/table-question-answering) | ✅ | [`~InferenceClient.table_question_answering`] |
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| | [Text Classification](https://huggingface.co/tasks/text-classification) | ✅ | [`~InferenceClient.text_classification`] |
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| | [Text Generation](https://huggingface.co/tasks/text-generation) | ✅ | [`~InferenceClient.text_generation`] |
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| | [Token Classification](https://huggingface.co/tasks/token-classification) | ✅ | [`~InferenceClient.token_classification`] |
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| | [Translation](https://huggingface.co/tasks/translation) | ✅ | [`~InferenceClient.translation`] |
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| | [Zero Shot Classification](https://huggingface.co/tasks/zero-shot-classification) | ✅ | [`~InferenceClient.zero_shot_classification`] |
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| Tabular | [Tabular Classification](https://huggingface.co/tasks/tabular-classification) | ✅ | [`~InferenceClient.tabular_classification`] |
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| | [Tabular Regression](https://huggingface.co/tasks/tabular-regression) | ✅ | [`~InferenceClient.tabular_regression`] |
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| Domäne | Aufgabe | Unterstützt | Dokumentation |
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| --------------- | --------------------------------------------------------------------------------------------- | ----------- | --------------------------------------------------- |
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| Audio | [Audio Classification](https://huggingface.co/tasks/audio-classification) | ✅ | [`~InferenceClient.audio_classification`] |
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| | [Automatic Speech Recognition](https://huggingface.co/tasks/automatic-speech-recognition) | ✅ | [`~InferenceClient.automatic_speech_recognition`] |
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| | [Text-to-Speech](https://huggingface.co/tasks/text-to-speech) | ✅ | [`~InferenceClient.text_to_speech`] |
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| Computer Vision | [Image Classification](https://huggingface.co/tasks/image-classification) | ✅ | [`~InferenceClient.image_classification`] |
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| | [Image Segmentation](https://huggingface.co/tasks/image-segmentation) | ✅ | [`~InferenceClient.image_segmentation`] |
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| | [Image-to-Image](https://huggingface.co/tasks/image-to-image) | ✅ | [`~InferenceClient.image_to_image`] |
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| | [Image-to-Text](https://huggingface.co/tasks/image-to-text) | ✅ | [`~InferenceClient.image_to_text`] |
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| | [Object Detection](https://huggingface.co/tasks/object-detection) | ✅ | [`~InferenceClient.object_detection`] |
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| | [Text-to-Image](https://huggingface.co/tasks/text-to-image) | ✅ | [`~InferenceClient.text_to_image`] |
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| | [Zero-Shot-Image-Classification](https://huggingface.co/tasks/zero-shot-image-classification) | ✅ | [`~InferenceClient.zero_shot_image_classification`] |
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| Multimodal | [Documentation Question Answering](https://huggingface.co/tasks/document-question-answering) | ✅ | [`~InferenceClient.document_question_answering`] |
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| | [Visual Question Answering](https://huggingface.co/tasks/visual-question-answering) | ✅ | [`~InferenceClient.visual_question_answering`] |
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| NLP | [Conversational](https://huggingface.co/tasks/conversational) | ✅ | [`~InferenceClient.conversational`] |
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| | [Feature Extraction](https://huggingface.co/tasks/feature-extraction) | ✅ | [`~InferenceClient.feature_extraction`] |
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| | [Fill Mask](https://huggingface.co/tasks/fill-mask) | ✅ | [`~InferenceClient.fill_mask`] |
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| | [Question Answering](https://huggingface.co/tasks/question-answering) | ✅ | [`~InferenceClient.question_answering`] |
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| | [Sentence Similarity](https://huggingface.co/tasks/sentence-similarity) | ✅ | [`~InferenceClient.sentence_similarity`] |
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| | [Summarization](https://huggingface.co/tasks/summarization) | ✅ | [`~InferenceClient.summarization`] |
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| | [Table Question Answering](https://huggingface.co/tasks/table-question-answering) | ✅ | [`~InferenceClient.table_question_answering`] |
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| | [Text Classification](https://huggingface.co/tasks/text-classification) | ✅ | [`~InferenceClient.text_classification`] |
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| | [Text Generation](https://huggingface.co/tasks/text-generation) | ✅ | [`~InferenceClient.text_generation`] |
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| | [Token Classification](https://huggingface.co/tasks/token-classification) | ✅ | [`~InferenceClient.token_classification`] |
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| | [Translation](https://huggingface.co/tasks/translation) | ✅ | [`~InferenceClient.translation`] |
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| | [Zero Shot Classification](https://huggingface.co/tasks/zero-shot-classification) | ✅ | [`~InferenceClient.zero_shot_classification`] |
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| Tabular | [Tabular Classification](https://huggingface.co/tasks/tabular-classification) | ✅ | [`~InferenceClient.tabular_classification`] |
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| | [Tabular Regression](https://huggingface.co/tasks/tabular-regression) | ✅ | [`~InferenceClient.tabular_regression`] |
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> [!TIP]
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[{'score': 0.9779096841812134, 'label': 'Blenheim spaniel'}, ...]
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```
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## Legacy InferenceAPI client
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Der [`InferenceClient`] dient als Ersatz für den veralteten [`InferenceApi`]-Client. Er bietet spezifische Unterstützung für Aufgaben und behandelt Inferenz sowohl auf der [Inferenz API](https://huggingface.co/docs/api-inference/index) als auch auf den [Inferenz Endpunkten](https://huggingface.co/docs/inference-endpoints/index).
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Hier finden Sie eine kurze Anleitung, die Ihnen hilft, von [`InferenceApi`] zu [`InferenceClient`] zu migrieren.
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### Initialisierung
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Ändern Sie von
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```python
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>>> from huggingface_hub import InferenceApi
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>>> inference = InferenceApi(repo_id="bert-base-uncased", token=API_TOKEN)
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```
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zu
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>>> from huggingface_hub import InferenceClient
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>>> inference = InferenceClient(model="bert-base-uncased", token=API_TOKEN)
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```
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### Ausführen einer bestimmten Aufgabe
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Ändern Sie von
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```python
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>>> from huggingface_hub import InferenceApi
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>>> inference = InferenceApi(repo_id="paraphrase-xlm-r-multilingual-v1", task="feature-extraction")
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>>> inference(...)
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```
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zu
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```python
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>>> from huggingface_hub import InferenceClient
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>>> inference = InferenceClient()
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>>> inference.feature_extraction(..., model="paraphrase-xlm-r-multilingual-v1")
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```
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> [!TIP]
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> Dies ist der empfohlene Weg, um Ihren Code an [`InferenceClient`] anzupassen. Dadurch können Sie von den aufgabenspezifischen Methoden wie `feature_extraction` profitieren.
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### Eigene Anfragen ausführen
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Ändern Sie von
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```python
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>>> from huggingface_hub import InferenceApi
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>>> inference = InferenceApi(repo_id="bert-base-uncased")
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>>> inference(inputs="The goal of life is [MASK].")
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[{'sequence': 'the goal of life is life.', 'score': 0.10933292657136917, 'token': 2166, 'token_str': 'life'}]
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```
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zu
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```python
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>>> client = InferenceClient()
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>>> response = client.post(json={"inputs": "The goal of life is [MASK]."}, model="bert-base-uncased")
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>>> response.json()
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[{'sequence': 'the goal of life is life.', 'score': 0.10933292657136917, 'token': 2166, 'token_str': 'life'}]
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```
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### Mit Parametern ausführen
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Ändern Sie von
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>>> from huggingface_hub import InferenceApi
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>>> inference = InferenceApi(repo_id="typeform/distilbert-base-uncased-mnli")
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>>> inputs = "Hi, I recently bought a device from your company but it is not working as advertised and I would like to get reimbursed!"
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>>> params = {"candidate_labels":["refund", "legal", "faq"]}
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>>> inference(inputs, params)
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{'sequence': 'Hi, I recently bought a device from your company but it is not working as advertised and I would like to get reimbursed!', 'labels': ['refund', 'faq', 'legal'], 'scores': [0.9378499388694763, 0.04914155602455139, 0.013008488342165947]}
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```
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```python
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>>> client = InferenceClient()
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>>> inputs = "Hi, I recently bought a device from your company but it is not working as advertised and I would like to get reimbursed!"
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>>> params = {"candidate_labels":["refund", "legal", "faq"]}
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>>> response = client.post(json={"inputs": inputs, "parameters": params}, model="typeform/distilbert-base-uncased-mnli")
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>>> response.json()
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{'sequence': 'Hi, I recently bought a device from your company but it is not working as advertised and I would like to get reimbursed!', 'labels': ['refund', 'faq', 'legal'], 'scores': [0.9378499388694763, 0.04914155602455139, 0.013008488342165947]}
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```

‎docs/source/en/guides/inference.md‎

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2. [Inference Endpoints](https://huggingface.co/docs/inference-endpoints/index): a product to easily deploy models to production. Inference is run by Hugging Face in a dedicated, fully managed infrastructure on a cloud provider of your choice.
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3. Local endpoints: you can also run inference with local inference servers like [llama.cpp](https://github.com/ggerganov/llama.cpp), [Ollama](https://ollama.com/), [vLLM](https://github.com/vllm-project/vllm), [LiteLLM](https://docs.litellm.ai/docs/simple_proxy), or [Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) by connecting the client to these local endpoints.
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These services can all be called from the [`InferenceClient`] object. It acts as a replacement for the legacy
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[`InferenceApi`] client, adding specific support for tasks and third-party providers.
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Learn how to migrate to the new client in the [Legacy InferenceAPI client](#legacy-inferenceapi-client) section.
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> [!TIP]
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> [`InferenceClient`] is a Python client making HTTP calls to our APIs. If you want to make the HTTP calls directly using
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> your preferred tool (curl, postman,...), please refer to the [Inference Providers](https://huggingface.co/docs/inference-providers/index) documentation

‎docs/source/en/package_reference/inference_client.md‎

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## InferenceTimeoutError
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[[autodoc]] InferenceTimeoutError
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## InferenceAPI
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[`InferenceAPI`] is the legacy way to call the Inference API. The interface is more simplistic and requires knowing
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the input parameters and output format for each task. It also lacks the ability to connect to other services like
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Inference Endpoints or AWS SageMaker. [`InferenceAPI`] will soon be deprecated so we recommend using [`InferenceClient`]
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whenever possible. Check out [this guide](../guides/inference#legacy-inferenceapi-client) to learn how to switch from
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[`InferenceAPI`] to [`InferenceClient`] in your scripts.
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[[autodoc]] InferenceApi
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- __init__
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- __call__
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- all

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