Create app.py

app.py

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+import gradio as gr
+import torch
+from sahi.prediction import ObjectPrediction
+from sahi.utils.cv import visualize_object_predictions, read_image
+from ultralyticsplus import YOLO
+
+
+def yolov8_inference(
+    image: gr.inputs.Image = None,
+    model_path: gr.inputs.Dropdown = None,
+    image_size: gr.inputs.Slider = 640,
+    conf_threshold: gr.inputs.Slider = 0.25,
+    iou_threshold: gr.inputs.Slider = 0.25,
+):
+    """
+    YOLOv8 inference function
+    Args:
+        image: Input image
+        model_path: Path to the model
+        image_size: Image size
+        conf_threshold: Confidence threshold
+        iou_threshold: IOU threshold
+    Returns:
+        Rendered image
+    """
+    model = YOLO(model_path)
+    model.conf = conf_threshold
+    model.iou = iou_threshold
+    results = model.predict(image, imgsz=image_size, return_outputs=True)
+    object_prediction_list = []
+    for _, image_results in enumerate(results):
+        image_predictions_in_xyxy_format = image_results['det']
+        for pred in image_predictions_in_xyxy_format:
+            x1, y1, x2, y2 = (
+                int(pred[0]),
+                int(pred[1]),
+                int(pred[2]),
+                int(pred[3]),
+            )
+            bbox = [x1, y1, x2, y2]
+            score = pred[4]
+            category_name = model.model.names[int(pred[5])]
+            category_id = pred[5]
+            object_prediction = ObjectPrediction(
+                bbox=bbox,
+                category_id=int(category_id),
+                score=score,
+                category_name=category_name,
+            )
+            object_prediction_list.append(object_prediction)
+
+    image = read_image(image)
+    output_image = visualize_object_predictions(image=image, object_prediction_list=object_prediction_list)
+    return output_image['image']
+        
+
+inputs = [
+    gr.inputs.Image(type="filepath", label="Input Image"),
+    gr.inputs.Dropdown(["Asma/GreenHawk_test"], 
+                       default="Asma/GreenHawk_test", label="Model"),
+    gr.inputs.Slider(minimum=320, maximum=1280, default=640, step=32, label="Image Size"),
+    gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.25, step=0.05, label="Confidence Threshold"),
+    gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.25, step=0.05, label="IOU Threshold"),
+]
+
+outputs = gr.outputs.Image(type="filepath", label="Output Image")
+title = "GreenHawk - Visual Pollution Detection"
+
+demo_app = gr.Interface(
+    fn=yolov8_inference,
+    inputs=inputs,
+    outputs=outputs,
+    title=title,
+    # examples=examples,
+    cache_examples=True,
+    theme='huggingface',
+)
+demo_app.launch(enable_queue=True,share=True)