Create app.py

app.py

@@ -0,0 +1,55 @@
+import gradio as gr
+from ultralytics import YOLO
+import cv2
+import numpy as np
+import os 
+from PIL import Image
+
+
+def perform(image):
+    """Perform the task of detection"""
+  # Predict with the model
+  results = model(image, save=True)  # predict on an image
+  # get the saved prediction
+  prediction_image = Image.open(os.path.join(results[0].save_dir, os.listdir(results[0].save_dir)[0]))
+  # initialize the output list to save the bbox
+  outputs = []
+  # Access the results
+  for result in results[0]:
+      xywh = result.boxes.xyxy[0].numpy().tolist()  # top-left-x, top-left-y, bottom-right-x, bottom-right-y
+      name = result.names[result.boxes.cls.item()] # classes
+      confs = float(result.boxes.conf)  # confidence score of each box
+      outputs.append({"xywh":xywh ,"conf":confs, "name":name})
+  # return the image and the bbox
+  return prediction_image, outputs
+
+def load_model(model_name:str = "yolo11n.pt"):
+  """Model loading"""
+  try:
+    print(f"Loading the model {model_name}")
+    model = YOLO(model_name)
+  except Exception as e:
+    print(f"Error loading the model {e}")
+  return model
+
+# loading the model
+model = load_model()
+
+# Create the Gradio interface
+demo = gr.Interface(
+    fn=perform,
+    inputs=[gr.Image(type="pil")],  # PIL Image format
+    outputs=[gr.Image(type="pil"), gr.JSON()],
+    title="Object Detection using YOLO",
+    description="Upload an image and get image with bbox in it",
+    examples=None  # You can add example images here if needed
+)
+
+# Launch the app
+if __name__ == "__main__":
+  username = os.environ.get('username')
+  password = os.environ.get('password')
+  print(username, " ",password)
+  demo.launch(share=True, 
+                auth=(username, password)
+                )