Update app.py

app.py

@@ -1,42 +1,9 @@
-# check pytorch installation: 
-import torch, torchvision
-try:
-    import detectron2
-except:
-    import os 
-    os.system('pip install git+https://github.com/facebookresearch/detectron2.git')
 import gradio as gr
-# Some basic setup:
-# Setup detectron2 logger
-import detectron2
-from detectron2.utils.logger import setup_logger
-
-# import some common libraries
-import numpy as np
-import os, json, cv2, random
-
-# import some common detectron2 utilities
-from detectron2 import model_zoo
-from detectron2.engine import DefaultPredictor
-from detectron2.config import get_cfg
-from detectron2.utils.visualizer import Visualizer
-from detectron2.data import MetadataCatalog, DatasetCatalog
-from PIL import Image
-
-cfg = get_cfg()
-cfg.MODEL.DEVICE='cpu'
-cfg.merge_from_file(model_zoo.get_config_file("COCO-Detection/faster_rcnn_R_101_FPN_3x.yaml"))
-cfg.MODEL.WEIGHTS = "model_final.pth"
-cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.5  # set threshold for this model
-predictor = DefaultPredictor(cfg)
+from Inference import *
 def inference(img):
-    im = cv2.imread(img.name)
-    outputs = predictor(im)
-    v = Visualizer(im[:, :, ::-1], MetadataCatalog.get(cfg.DATASETS.TRAIN[0]), scale=1.2)
-    out = v.draw_instance_predictions(outputs["instances"].to("cpu"))
-    return Image.fromarray(np.uint8(out.get_image())).convert('RGB')
-
-examples = [['input.jpg']]
-
-gr.Interface(inference, inputs=gr.inputs.Image(type="file"), outputs=gr.outputs.Image(type="pil"),enable_queue=True,
-    examples=examples).launch()
+    img = cv2.imread(img.name)
+    boxes, scores, cls_ids = prediction(img)
+    return vis(img, boxes, scores, cls_ids)
+examples = [['input.jpg'], ['1.png']]
+gr.Interface(inference, inputs=gr.inputs.Image(type="file"), outputs=gr.outputs.Image(type="pil"),
+    examples=examples).launch()