Update app.py

app.py

@@ -1,24 +1,33 @@
 import gradio as gr
-from transformers import pipeline
-from PIL import Image
-from helper import load_image_from_url, render_results_in_image
 
+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",
+}
 
-def get_pipeline_prediction(pil_image):
-    
-    pipeline_output = od_pipe(pil_image)
-    
-    processed_image = render_results_in_image(pil_image,
-                                            pipeline_output)
-    return processed_image
-
-
-demo = gr.Interface(
-  fn=get_pipeline_prediction,
-  inputs=gr.Image(label="Input image", 
-                  type="pil"),
-  outputs=gr.Image(label="Output image with predicted instances",
-                   type="pil")
-)
-
-demo.launch()
+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()