Update app.py

app.py

@@ -3,6 +3,7 @@ from transformers import pipeline
 from PIL import Image, ImageDraw, ImageFont
 import gradio as gr
 
+
 # Draw bounding box definition
 def draw_bounding_box(im, score, label, xmin, ymin, xmax, ymax, index, num_boxes):
   """ Draw a bounding box. """
@@ -21,6 +22,8 @@ def object_classify(img):
   object_detector = pipeline("object-detection", model = model, feature_extractor = feature_extractor)
   bboxes = object_detector(img)
 
+  price_total = 0
+  total_items_in_cart = 0
   
   # Iteration elements
   num_boxes = len(bboxes)
@@ -28,6 +31,13 @@ def object_classify(img):
 
   # Draw bounding box for each result
   for i in bboxes:
+    if i['label'] == 'apple':
+      price_total += 25 #pesos? dunno
+    elif i['label'] == 'bottle':
+      price_total += 15
+    elif i['label'] == 'broccoli':
+      price_total += 100
+    
     box = i['box']
     #Draw the bounding box
     output_image = draw_bounding_box(img, i['score'],i['label'],
@@ -35,12 +45,13 @@ def object_classify(img):
                                   box['xmax'], box['ymax'],
                                   index, num_boxes)
     index += 1
-  return output_image
+    total_items_in_cart += 1
+  return output_image, str(price_total), str(total_items_in_cart)
 
 TITLE = 'Object Detection for Effective Self-Checkout in Grocery Shopping [Work In Progress]'
-DESCRIPTION = 'A deep learning application to reimagine self-checkout stores.'
+DESCRIPTION = 'A deep learning application using DETR model to reimagine self-checkout stores.'
 EXAMPLES = ['ex1.jpg']
 interface=gr.Interface(object_classify,
-                       gr.inputs.Image(type = 'pil'),gr.outputs.Image(),
+                       gr.inputs.Image(type = 'pil'),outputs = [gr.outputs.Image(), gr.outputs.Textbox(label='Total Price: ')],
                        examples = EXAMPLES,title = TITLE, description=DESCRIPTION, allow_flagging="never")
 interface.launch()