Create app.py

app.py

@@ -0,0 +1,56 @@
+import gradio as gr
+import os
+from pathlib import Path
+from ultralytics import YOLO
+import cv2
+import logging
+import numpy as np
+
+
+def setup_logging():
+    logging.basicConfig(level=logging.INFO,
+                        format='%(asctime)s - %(levelname)s - %(message)s')
+
+
+def process_image(model_path, image):
+    try:
+        # Wczytanie modelu
+        model = YOLO(model_path)
+        logging.info(f'Loaded model from: {model_path}')
+
+        # Przetwarzanie obrazu
+        logging.info(f'Processing file')
+        # Wykrywanie obiektów na obrazie
+        results = model(image)
+
+        for result in results:
+            # Pobierz obraz wynikowy z zaznaczonymi wykryciami
+            result_img = result.plot()
+
+        logging.info("Image processing completed.")
+        return result_img
+    except Exception as e:
+        logging.error(f'Error occurred: {e}')
+        return None
+
+
+def yolo_detection(image):
+    model_path = 'model.pt'  # Podaj tutaj ścieżkę do swojego modelu YOLO
+    result_image = process_image(model_path, image)
+    if result_image is not None:
+        return cv2.cvtColor(result_image, cv2.COLOR_BGR2RGB)
+    else:
+        return None
+
+
+with gr.Blocks() as demo:
+    gr.Markdown("# 👁️ tiny YOLOv8 Open/closed eye detection\nUpload an image and see the detection results.")
+
+    image_input = gr.Image(label="Input Image")
+    image_output = gr.Image(label="Detected Objects")
+
+    detect_button = gr.Button("Detect Objects")
+
+    detect_button.click(fn=yolo_detection, inputs=image_input, outputs=image_output)
+
+demo.launch(share=True)