Create app.py

app.py

@@ -0,0 +1,52 @@
+from transformers import SegformerFeatureExtractor, SegformerForSemanticSegmentation
+from datasets import load_dataset
+from transformers import pipeline
+from PIL import Image, ImageDraw, ImageFont
+import gradio as gr
+
+ds = load_dataset("hf-internal-testing/fixtures_ade20k", split="test")
+
+def object_classify(img1,img2):
+
+  feature_extractor = SegformerFeatureExtractor.from_pretrained("nvidia/segformer-b2-finetuned-ade-512-512")
+  model = SegformerForSemanticSegmentation.from_pretrained("nvidia/segformer-b2-finetuned-ade-512-512")
+  
+  object_detector = pipeline(task="image-segmentation", model = model, feature_extractor = feature_extractor)
+  
+  #list of dictionaries
+  dict_obj1 = object_detector(img1) 
+  dict_obj2 = object_detector(img2)
+  
+  #list of object labels present in the image
+  objects_1=[]
+  objects_2=[]
+  
+  #gets the label from each dictionary
+  for i in dict_obj1:
+    objects_1.append(i['label'])
+  
+  for j in dict_obj2:
+    objects_2.append(j['label'])
+   
+  #gets the uncommon elements from the 2 lists
+  missing_objects=list(set(objects_1)-set(objects_2))
+  
+  return missing_objects
+ 
+  
+TITLE = 'Missing Items'
+DESCRIPTION = 'Input two indoor pictures. First image being the original and second is one with the missing item/s'
+EXAMPLES = [['Bedroom_1.jpg'],['Bedroom_2.jpg']]
+
+INPUTS=[gr.inputs.Image(type = 'pil'),gr.inputs.Image(type = 'pil')]
+OUTPUTS=gr.outputs.Textbox()
+
+interface=gr.Interface(object_classify,
+                       INPUTS,
+                       OUTPUTS,
+                       examples = EXAMPLES,
+                       title = TITLE, 
+                       description=DESCRIPTION, allow_flagging="never")
+
+
+interface.launch()