Add application file

app.py

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+import gradio as gr
+
+models = {
+    # "object-detection": "facebook/detr-resnet-50",
+    "image-classification": "microsoft/resnet-50",
+    "text-to-image": "cagliostrolab/animagine-xl-3.0",
+    "image-to-text": "Salesforce/blip-image-captioning-large",
+    "audio-classification": "speechbrain/emotion-recognition-wav2vec2-IEMOCAP",
+    "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()
+