Update app.py

app.py

@@ -5,8 +5,7 @@ import requests, validators
 import torch
 import pathlib
 from PIL import Image
-from transformers import AutoFeatureExtractor, DetrForObjectDetection, YolosForObjectDetection
-
+from transformers import AutoFeatureExtractor, DetrForObjectDetection
 import os
 
 # colors for visualization
@@ -52,23 +51,20 @@ def visualize_prediction(pil_img, output_dict, threshold=0.7, id2label=None):
     plt.axis("off")
     return fig2img(plt.gcf())
 
-def detect_objects(model_name,url_input,image_input,threshold):
-    
+def detect_objects(model_name,image_input,threshold):
+    print(type(image_input))
+
     #Extract model and feature extractor
     feature_extractor = AutoFeatureExtractor.from_pretrained(model_name)
     
-    if 'detr' in model_name:
-        
-        model = DetrForObjectDetection.from_pretrained(model_name)
-        
-    elif 'yolos' in model_name:
-    
-        model = YolosForObjectDetection.from_pretrained(model_name)
-    
-    if validators.url(url_input):
-        image = Image.open(requests.get(url_input, stream=True).raw)
+    if 'detr' in model_name:    
+        model = DetrForObjectDetection.from_pretrained(model_name)     
         
-    elif image_input:
+    if image_input:
+      if isinstance(image_input,str):
+        if validators.url(image_input):
+          image = Image.open(requests.get(image_input, stream=True).raw)
+      else:
         image = image_input
     
     #Make prediction
@@ -86,68 +82,50 @@ def set_example_url(example: list) -> dict:
     return gr.Textbox.update(value=example[0])
 
 
-title = """<h1 id="title">Object Detection App with DETR and YOLOS</h1>"""
-
-description = """
-Links to HuggingFace Models:
-- [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50)  
-- [facebook/detr-resnet-101](https://huggingface.co/facebook/detr-resnet-101)  
-- [hustvl/yolos-small](https://huggingface.co/hustvl/yolos-small)
-- [hustvl/yolos-tiny](https://huggingface.co/hustvl/yolos-tiny)
-"""
-
-models = ["facebook/detr-resnet-50","facebook/detr-resnet-101",'hustvl/yolos-small','hustvl/yolos-tiny']
-urls = ["https://c8.alamy.com/comp/J2AB4K/the-new-york-stock-exchange-on-the-wall-street-in-new-york-J2AB4K.jpg"]
-
-twitter_link = """
-[![](https://img.shields.io/twitter/follow/nickmuchi?label=@nickmuchi&style=social)](https://twitter.com/nickmuchi)
-"""
-
+models = ["facebook/detr-resnet-50","facebook/detr-resnet-101"]
+#examples = ['1daaadc1e83fcecc7bfa920ed2773653.jpeg']
 css = '''
 h1#title {
   text-align: center;
 }
 '''
-demo = gr.Blocks(css=css)
+demo = gr.Blocks()
 
 with demo:
-    gr.Markdown(title)
-    gr.Markdown(description)
-    gr.Markdown(twitter_link)
+    #r.Markdown(title)
+    #gr.Markdown(description)
     options = gr.Dropdown(choices=models,label='Select Object Detection Model',show_label=True)
     slider_input = gr.Slider(minimum=0.2,maximum=1,value=0.7,label='Prediction Threshold')
     
     with gr.Tabs():
         with gr.TabItem('Image URL'):
-            with gr.Row():
-                url_input = gr.Textbox(lines=2,label='Enter valid image URL here..')
-                img_output_from_url = gr.Image(shape=(650,650))
-                
-            with gr.Row():
-                example_url = gr.Dataset(components=[url_input],samples=[[str(url)] for url in urls])
+          with gr.Row():
+            url_input = gr.Textbox(lines=2,label='Enter valid image URL here..')
+            img_output_from_url = gr.Image(shape=(650,650))
             
-            url_but = gr.Button('Detect')
-     
+          with gr.Row():
+            example_url = gr.Dataset(components=[url_input],samples=[['https://miro.medium.com/max/960/1*ACc03086R6H_LyLydy8Z4g.jpeg'],['https://www.exposit.com/wp-content/uploads/2021/12/Blog_cover-52-scaled.jpeg']])
+          
+          url_but = gr.Button('Detect')  
+        
         with gr.TabItem('Image Upload'):
-            with gr.Row():
-                img_input = gr.Image(type='pil')
-                img_output_from_upload= gr.Image(shape=(650,650))
+          with gr.Row():
+            img_input = gr.Image(type='pil')
+            img_output_from_upload= gr.Image(shape=(650,650))
                 
-            with gr.Row(): 
-                example_images = gr.Dataset(components=[img_input],
-                                            samples=[[path.as_posix()]
-                                                     for path in sorted(pathlib.Path('images').rglob('*.JPG'))])
+          with gr.Row():
+            example_images = gr.Dataset(components=[img_input],
+                                            samples=[["airport.jpg"],['football-match.jpg']])
                 
-            img_but = gr.Button('Detect')
+          img_but = gr.Button('Detect')
         
-    
-    url_but.click(detect_objects,inputs=[options,url_input,img_input,slider_input],outputs=img_output_from_url,queue=True)
-    img_but.click(detect_objects,inputs=[options,url_input,img_input,slider_input],outputs=img_output_from_upload,queue=True)
+    url_but.click(detect_objects,inputs=[options,url_input,slider_input],outputs=img_output_from_url,queue=True)
+    img_but.click(detect_objects,inputs=[options,img_input,slider_input],outputs=img_output_from_upload,queue=True)
     example_images.click(fn=set_example_image,inputs=[example_images],outputs=[img_input])
     example_url.click(fn=set_example_url,inputs=[example_url],outputs=[url_input])
     
 
-    gr.Markdown("![visitor badge](https://visitor-badge.glitch.me/badge?page_id=nickmuchi-object-detection-with-detr-and-yolos)")
+    #gr.Markdown("![visitor badge](https://visitor-badge.glitch.me/badge?page_id=nickmuchi-object-detection-with-detr-and-yolos)")
 
     
 demo.launch(enable_queue=True)