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