updates

app.py

@@ -1,7 +1,44 @@
 import gradio as gr
+# check pytorch installation: 
+import torch, torchvision
+# Some basic setup:
+# Setup detectron2 logger
+import detectron2
+from detectron2.utils.logger import setup_logger
 
-def greet(name):
-    return "Hello " + name + "!!"
+# import some common libraries
+import numpy as np
+import os, json, cv2, random
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+# 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)
+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')
+    
+
+title = "Detectron 2"
+description = "Gradio demo for Detectron 2: A PyTorch-based modular object detection library. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below."
+article = "<p style='text-align: center'><a href='https://ai.facebook.com/blog/-detectron2-a-pytorch-based-modular-object-detection-library-/' target='_blank'>Detectron2: A PyTorch-based modular object detection library</a> | <a href='https://github.com/facebookresearch/detectron2' target='_blank'>Github Repo</a></p>"
+
+examples = [['input.jpg']]
+
+gr.Interface(inference, inputs=gr.inputs.Image(type="file"), outputs=gr.outputs.Image(type="pil"),enable_queue=True, title=title,
+    description=description,
+    article=article,
+    examples=examples).launch()