Add application file

app.py

@@ -6,17 +6,15 @@ import json
 import cv2
 from PIL import Image
 from timeit import default_timer as timer
-import pathlib
-import platform
 import numpy as np
+from transformers import AutoModel
+
 model = torch.hub.load('ultralytics/yolov5','yolov5s', pretrained=True)
+#model1 = AutoModel.from_pretrained(model)
 cnt = 0
-
 def LCR(bbox,x_img, y_img):
     x1 = bbox[0]/x_img
     x2 = bbox[2]/x_img
-    
-    
     if x1 < 0.2 and x2 < 0.2 :
         location = "Left"
     elif x1 > 0.8 and x2 > 0.8:
@@ -96,14 +94,6 @@ def turn_img_into_fileJSON(frame):
 title = "Object-detection"
 description = "An EfficientNetB2 feature extractor computer vision model to classify images of object."
 article = "Created by Ryan"
-
-# json_str1, pred_time1 = turn_img_into_fileJSON("C:/Users/ACER/Pictures/mydestiny/273536337_788402492117531_8798195010554693138_n.jpg")
-# print(json_str1, pred_time1)
-
-# json_str, pred_time = turn_img_into_fileJSON("D:/cuoc_thi/object-detection/download.jpg")
-# print(json_str, pred_time)
-
-
 # Create the Gradio demo
 demo = gr.Interface(fn=turn_img_into_fileJSON, # mapping function from input to output
                     inputs="pil", # what are the inputs?
@@ -116,5 +106,5 @@ demo = gr.Interface(fn=turn_img_into_fileJSON, # mapping function from input to
                     description=description,
                     article=article,
                     live = True)
-#demo.launch()
-demo.launch(share=True)
+demo.launch()
+#demo.launch(share=True)