Create app.py

app.py

@@ -0,0 +1,70 @@
+try:
+    import detectron2
+except:
+    import os 
+    os.system('pip install git+https://github.com/facebookresearch/detectron2.git')
+from matplotlib.pyplot import axis
+import requests
+import numpy as np
+from torch import nn
+
+import requests
+
+
+import torch
+import detectron2
+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
+import streamlit as st
+from detectron2.utils.visualizer import ColorMode
+import os
+import cv2
+from PIL import Image, ImageOps
+import numpy as np
+
+model_path = "model_final.pth"
+
+cfg = get_cfg()
+cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.6
+cfg.MODEL.ROI_HEADS.NUM_CLASSES = 4
+cfg.MODEL.WEIGHTS = model_path
+st.write("""
+         # Car Damage Detection
+         """
+         )
+file = st.file_uploader("Please upload an image file(JPG/PNG/JPEG format)", type=["jpg", "png","jpeg"])
+
+st.set_option('deprecation.showfileUploaderEncoding', False)
+
+car_metadata = MetadataCatalog.get("test1")
+car_metadata.thing_classes = ['Damage-car','Damage','Others','Undamage']
+
+if not torch.cuda.is_available():
+    cfg.MODEL.DEVICE='cpu'
+    
+predictor = DefaultPredictor(cfg)
+def inference(image):
+   
+    img = np.array(image)
+    outputs = predictor(img) 
+    v = Visualizer(img[:, :, ::-1],
+                   metadata=car_metadata, 
+                   scale=0.5, 
+                   instance_mode=ColorMode.IMAGE_BW  
+    )
+    out = v.draw_instance_predictions(outputs["instances"].to("cpu"))
+    return out.get_image()
+if file is None:
+    st.text("Please upload an image file")
+else:
+    image = Image.open(file).convert('RGB')
+    st.image(image,use_column_width=True)
+    st.write("""
+              # Output!!
+            """
+             )
+    predictions = inference(image)
+    st.image(predictions,use_column_width=True)