Create app.py

app.py

@@ -0,0 +1,56 @@
+import gdown
+
+def download_file_from_google_drive(file_id, output_file):
+    """
+    Download a file from Google Drive.
+
+    :param file_id: The Google Drive file ID.
+    :param output_file: The name of the file to save.
+    """
+    url = f"https://drive.google.com/uc?id={file_id}"
+    gdown.download(url, output_file, quiet=False)
+
+# Example usage:
+file_id = "1Wgh9dWT6SbakJhvuNkSaIa1ydFtkfUW6"
+out = "average_model.pth"
+download_file_from_google_drive(file_id,out)
+
+from super_gradients.training import models
+import torch
+import supervision as sv
+import gradio as gr 
+
+DEVICE = 'cuda' if torch.cuda.is_available() else "cpu"
+MODEL_ARCH = 'yolo_nas_l'
+clasess = ["Airplane"]
+checkpoint_path= "average_model.pth"
+
+
+def run(image , CONFIDENCE_TRESHOLD) : 
+  best_model = models.get(
+      MODEL_ARCH,
+      num_classes=len(clasess),
+      checkpoint_path= checkpoint_path
+  ).to(DEVICE)
+  result = list(best_model.predict(image, conf=CONFIDENCE_TRESHOLD))[0]
+  detections = sv.Detections(
+          xyxy=result.prediction.bboxes_xyxy,
+          confidence=result.prediction.confidence,
+          class_id=result.prediction.labels.astype(int)
+      )
+  box_annotator = sv.BoxAnnotator()
+  annotated_frame = box_annotator.annotate(
+      scene=image.copy(),
+      detections=detections,
+      labels=clasess
+  )
+  return annotated_frame
+iface = gr.Interface(
+    fn=run,
+    inputs=[gr.Image(label="Input image", type="numpy") , gr.Slider(0, 1, value=0.5, label="Select your CONFIDENCE_TRESHOLD")],
+    outputs=gr.Image(label="The Prediction Output :", type="numpy"),
+    title="Aerial Airport YOLO Nas object detection",
+    allow_flagging=False , 
+    description="I conducted fine-tuning on the YOLO-NAS (YOLO Neural Architecture Search) model, a cutting-edge object detection architecture developed by Deci-AI. My objective was to enhance its ability to detect airplanes in the 'Aerial Airport' dataset",
+)
+iface.launch(debug=True)