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import gradio as gr |
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models = { |
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"image-classification": "microsoft/resnet-50", |
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"text-to-image": "runwayml/stable-diffusion-v1-5", |
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"image-to-text": "nlpconnect/vit-gpt2-image-captioning", |
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"audio-classification": "mtg-upf/discogs-maest-30s-pw-73e-ts", |
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"audio-to-audio": "speechbrain/mtl-mimic-voicebank", |
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"automatic-speech-recognition": "jonatasgrosman/wav2vec2-large-xlsr-53-english", |
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"conversational": "microsoft/DialoGPT-medium", |
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"feature-extraction": "cambridgeltl/SapBERT-from-PubMedBERT-fulltext", |
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"fill-mask": "bert-base-uncased", |
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"question-answering": "deepset/roberta-base-squad2", |
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"summarization": "facebook/bart-large-cnn", |
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"text-classification": "cardiffnlp/twitter-roberta-base-sentiment-latest", |
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"text-generation": "gpt2", |
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"text2text-generation": "vennify/t5-base-grammar-correction", |
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"translation": "Helsinki-NLP/opus-mt-fr-en", |
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"zero-shot-classification": "facebook/bart-large-mnli", |
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"sentence-similarity": "sentence-transformers/all-mpnet-base-v2", |
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"text-to-speech": "facebook/mms-tts-eng", |
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"token-classification": "benjamin/wtp-canine-s-1l", |
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"document-question-answering": "fxmarty/tiny-doc-qa-vision-encoder-decoder", |
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"visual-question-answering": "Salesforce/blip-vqa-capfilt-large", |
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} |
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with gr.Blocks() as demo: |
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gr.Markdown("## Gradio Pipelines Tasks") |
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for k, v in models.items(): |
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with gr.Tab(k): |
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gr.load(v, src="models") |
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demo.launch() |
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