Update app.py

app.py

@@ -3,6 +3,7 @@ import gradio as gr
 import matplotlib.pyplot as plt
 import requests, validators
 import torch
+import pathlib
 from PIL import Image
 from transformers import AutoFeatureExtractor, DetrForObjectDetection, YolosForObjectDetection
 
@@ -51,7 +52,7 @@ def visualize_prediction(pil_img, output_dict, threshold=0.7, id2label=None):
     plt.axis("off")
     return fig2img(plt.gcf())
 
-def detect_objects_from_url(model_name,url,threshold):
+def detect_objects(model_name,url_input,image_input,threshold):
     
     #Extract model and feature extractor
     feature_extractor = AutoFeatureExtractor.from_pretrained(model_name)
@@ -66,29 +67,8 @@ def detect_objects_from_url(model_name,url,threshold):
     
     if url and validators.url(url):
         image = Image.open(requests.get(url, stream=True).raw)
-    
-    #Make prediction
-    processed_outputs = make_prediction(image, feature_extractor, model)
-    
-    #Visualize prediction
-    viz_img = visualize_prediction(image, processed_outputs, threshold, model.config.id2label)
-    
-    return viz_img   
-    
-def detect_objects_from_upload(model_name,image_upload,threshold):
-    
-    #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 image_upload:
+    elif image_upload:
         image = image_upload
     
     #Make prediction
@@ -97,13 +77,13 @@ def detect_objects_from_upload(model_name,image_upload,threshold):
     #Visualize prediction
     viz_img = visualize_prediction(image, processed_outputs, threshold, model.config.id2label)
     
-    return viz_img
-    
+    return viz_img   
+        
 #examples=[['facebook/detr-resnet-50','https://media-cldnry.s-nbcnews.com/image/upload/t_fit-1500w,f_auto,q_auto:best/newscms/2020_14/3290756/200331-wall-street-ew-#343p.jpg',,0.7]
 
 
 
-title = 'Object Detection App with DETR and YOLOS'
+title = """<h1 id="title">Object Detection App with DETR and YOLOS</h1>"""
 
 description = """
 Links to HuggingFace Models:
@@ -131,6 +111,12 @@ with demo:
             with gr.Row():
                 url_input = gr.Textbox(lines=1,label='Enter valid image URL here..')
                 img_output_from_url = gr.Image(shape=(450,450))
+                
+            with gr.Row():
+                urls = ["https://media-cldnry.s-nbcnews.com/image/upload/t_fit-1500w,f_auto,q_auto:best/newscms/2020_14/3290756/200331-wall-street-ew-#343p.jpg"]
+                example_url = gr.Dataset(components=[url_input],
+                                            samples=[[url.as_posix()]
+                                                     for url in urls])
             
             url_but = gr.Button('Detect')
      
@@ -139,11 +125,17 @@ with demo:
                 img_input = gr.Image(type='pil')
                 img_output_from_upload= gr.Image(shape=(450,450))
                 
+            with gr.Row():
+                paths = sorted(pathlib.Path('images').rglob('*.JPG')
+                example_images = gr.Dataset(components=[img_input],
+                                            samples=[[path.as_posix()]
+                                                     for path in paths])
+                
             img_but = gr.Button('Detect')
         
     
-    url_but.click(detect_objects_from_url,inputs=[options,url_input,slider_input],outputs=img_output_from_url,queue=True)
-    img_but.click(detect_objects_from_upload,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)
     
     
 demo.launch(enable_queue=True)