Update app.py

app.py

@@ -5,7 +5,8 @@ import requests, validators
 import torch
 import pathlib
 from PIL import Image
-from transformers import AutoFeatureExtractor, DetrForObjectDetection
+from transformers import AutoFeatureExtractor, DetrForObjectDetection, YolosForObjectDetection
+
 import os
 
 # colors for visualization
@@ -51,15 +52,23 @@ def visualize_prediction(pil_img, output_dict, threshold=0.7, id2label=None):
     plt.axis("off")
     return fig2img(plt.gcf())
 
-def detect_objects(model_name,image_input,threshold):
+def detect_objects(model_name,url_input,image_input,threshold):
     
     #Extract model and feature extractor
     feature_extractor = AutoFeatureExtractor.from_pretrained(model_name)
     
-    if 'detr' in model_name:    
-        model = DetrForObjectDetection.from_pretrained(model_name)     
+    if 'detr' in model_name:
+        
+        model = DetrForObjectDetection.from_pretrained(model_name)
         
-    if image_input:
+    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)
+        
+    elif image_input:
         image = image_input
     
     #Make prediction
@@ -73,18 +82,27 @@ def detect_objects(model_name,image_input,threshold):
 def set_example_image(example: list) -> dict:
     return gr.Image.update(value=example[0])
 
+def set_example_url(example: list) -> dict:
+    return gr.Textbox.update(value=example[0])
 
 
-title = """<h1 id="title">Detection for Drone</h1>"""
+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;
@@ -95,18 +113,21 @@ demo = gr.Blocks(css=css)
 with demo:
     gr.Markdown(title)
     gr.Markdown(description)
+    gr.Markdown(twitter_link)
     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))
+                
+            with gr.Row():
+                example_url = gr.Dataset(components=[url_input],samples=[[str(url)] for url in urls])
             
+            url_but = gr.Button('Detect')
+     
         with gr.TabItem('Image Upload'):
             with gr.Row():
                 img_input = gr.Image(type='pil')
@@ -115,17 +136,18 @@ with demo:
             with gr.Row(): 
                 example_images = gr.Dataset(components=[img_input],
                                             samples=[[path.as_posix()]
-                                                     for path in sorted(pathlib.Path('images').rglob('*.jpeg'))])
+                                                     for path in sorted(pathlib.Path('images').rglob('*.JPG'))])
                 
             img_but = gr.Button('Detect')
         
-    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)
+    
+    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)
     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)