app: add initial gradio version

app.py

@@ -0,0 +1,63 @@
+try:
+    import detectron2
+except:
+    import os 
+    os.system('pip install git+https://github.com/facebookresearch/detectron2.git')
+
+from matplotlib.pyplot import axis
+import gradio as gr
+import requests
+import numpy as np
+from torch import nn
+import requests
+
+import torch
+
+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
+
+
+model_path = "https://huggingface.co/dbmdz/detectron2-model/resolve/main/model_final.pth"
+
+cfg = get_cfg()
+cfg.merge_from_file("./configs/detectron2/faster_rcnn_R_50_FPN_3x.yaml")
+cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.8
+cfg.MODEL.ROI_HEADS.NUM_CLASSES = 2
+cfg.MODEL.WEIGHTS = model_path
+
+my_metadata = MetadataCatalog.get("dbmdz_coco_all")
+my_metadata.thing_classes = ["Illumination", "Illustration"]
+
+if not torch.cuda.is_available():
+    cfg.MODEL.DEVICE='cpu'
+
+predictor = DefaultPredictor(cfg)
+
+def inference(image):
+    print(image.height)
+
+    height = image.height
+
+    img = np.array(image.resize((500, height)))
+    outputs = predictor(img)
+
+    v = Visualizer(img, my_metadata, scale=1.2)
+    out = v.draw_instance_predictions(outputs["instances"].to("cpu"))
+    
+    return out.get_image()
+
+title = "DBMDZ Detectron2 Model Demo"
+description = "This demo introduces an interactive playground for our trained Detectron2 model. <br>The model was trained on image from digitized books to detect Illustration or Illumination segments on a given page. Classification threshold is set to 0.8."
+article = '<p>Detectron model is available from our repository <a href="">here</a> on the Hugging Face Model Hub.</p>'
+
+gr.Interface(
+    inference, 
+    [gr.inputs.Image(type="pil", label="Input")], 
+    gr.outputs.Image(type="numpy", label="Output"),
+    title=title,
+    description=description,
+    article=article,
+    examples=[]).launch()