Create app.py

app.py

@@ -0,0 +1,53 @@
+import gradio as gr
+import cv2
+import time
+import numpy as np
+from sahi import AutoDetectionModel
+from sahi.predict import get_sliced_prediction
+from pathlib import Path
+
+detection_model = AutoDetectionModel.from_pretrained(
+    model_type='ultralytics',
+    model_path="/workspace/runs/detect/train2/weights/last.pt",  # Replace with your model path
+    confidence_threshold=0.01,
+    device="cpu"  # Change to 'cuda:0' if you have a GPU
+)
+
+OUTPUT_PATH = "/workspace/pred_image.jpg"
+TEMP_PNG_PATH = "/workspace/pred_image.png"
+
+def run_inference(image):
+    input_path = "/workspace/input_image.jpg"
+    cv2.imwrite(input_path, cv2.cvtColor(image, cv2.COLOR_RGB2BGR))
+    
+    result = get_sliced_prediction(
+        input_path,
+        detection_model,
+        slice_height=256,
+        slice_width=256,
+        overlap_height_ratio=0.2,
+        overlap_width_ratio=0.2
+    )
+    
+    result.export_visuals(export_dir=Path(TEMP_PNG_PATH).parent, file_name=Path(TEMP_PNG_PATH).name)
+    
+    time.sleep(2)
+    
+    if not Path(TEMP_PNG_PATH).exists():
+        raise FileNotFoundError(f"SAHI did not save the PNG file at {TEMP_PNG_PATH}")
+    
+    processed_image = cv2.imread(TEMP_PNG_PATH)
+    cv2.imwrite(OUTPUT_PATH, processed_image)
+    Path(TEMP_PNG_PATH).unlink()
+    
+    return OUTPUT_PATH
+
+demo = gr.Interface(
+    fn=run_inference,
+    inputs=gr.Image(type="numpy"),
+    outputs=gr.Image(type="file"),
+    title="YOLO11 Object Detection",
+    description="Upload a DDR image to run inference using YOLO11"
+)
+
+demo.launch()