Update app.py

Added comments

app.py

@@ -8,27 +8,23 @@ import numpy as np
 from ultralytics import YOLO
 import yolov5
 
-file_urls = [
-    'https://c8.alamy.com/zooms/9/382c1e254fe14207998df9ec56354291/wce5tj.jpg',
-    'https://c8.alamy.com/comp/W20YYR/freight-container-on-the-back-of-a-truck-stuck-in-traffic-on-the-interstate-in-georgia-usa-W20YYR.jpg',
-    'https://www.shutterstock.com/shutterstock/photos/318604739/display_1500/stock-photo-highway-and-container-truck-at-china-318604739.jpg'
+# Image download
+# file_urls = [
+# ]
 
-]
-
-def download_file(url, save_name):
-    url = url
-    if not os.path.exists(save_name):
-        file = requests.get(url)
-        open(save_name, 'wb').write(file.content)
-
-for i, url in enumerate(file_urls):    
-    download_file(
-        file_urls[i],
-        f"image_{i}.jpg"
-    )
+# def download_file(url, save_name):
+#     url = url
+#     if not os.path.exists(save_name):
+#         file = requests.get(url)
+#         open(save_name, 'wb').write(file.content)
 
-# model_path = 'Container_YOLOV5'
+# for i, url in enumerate(file_urls):    
+#     download_file(
+#         file_urls[i],
+#         f"image_{i}.jpg"
+#     )
 
+# Function for inference
 def yolov5_inference(
     image: gr.inputs.Image = None,
     model_path: gr.inputs.Dropdown = None,
@@ -36,18 +32,24 @@ def yolov5_inference(
     conf_threshold: gr.inputs.Slider = 0.25,
     iou_threshold: gr.inputs.Slider = 0.45 ):
 
-
+    # Loading Yolo V5 model
     model = yolov5.load(model_path, device="cpu")
+
+    # Setting model configuration 
     model.conf = conf_threshold
     model.iou = iou_threshold
+
+    # Inference
     results = model([image], size=image_size)
+
+    # Cropping the predictions    
     crops = results.crop(save=False)
     img_crops = []
     for i in range(len(crops)):
         img_crops.append(crops[i]["im"][..., ::-1])
     return results.render()[0], img_crops
         
-
+# gradio Input
 inputs = [
     gr.inputs.Image(type="pil", label="Input Image"),
     gr.inputs.Dropdown(["Crime_Y5.pt","yolov5s.pt", "yolov5m.pt", "yolov5l.pt", "yolov5x.pt"], label="Model", default = 'Crime_Y5.pt'),
@@ -56,15 +58,19 @@ inputs = [
     gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.45, step=0.05, label="IOU Threshold"),
 ]
 
+# gradio Output
 outputs = gr.outputs.Image(type="filepath", label="Output Image")
 outputs_crops = gr.Gallery(label="Object crop")
-title = "Container code detection - YOLO V5"
+
+title = "Crime detection using custom trained Yolo model"
 description = "YOLOv5 is a family of object detection models pretrained on COCO dataset. This model is a pip implementation of the original YOLOv5 model."
 
+# gradio examples: "Image", "Model", "Image Size", "Confidence Threshold", "IOU Threshold"
 examples = [['1.jpg', 'Crime_Y5.pt', 640, 0.35, 0.45]
           ,['2.jpg', 'Crime_Y5.pt', 640, 0.35, 0.45]
           ,['4.jpg', 'Crime_Y5.pt', 640, 0.35, 0.45]]
 
+# gradio app launch
 demo_app = gr.Interface(
     fn=yolov5_inference,
     inputs=inputs,