Create app.py

app.py

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+import gradio as gr
+import cv2
+
+from ultralyticsplus import YOLO, render_result
+
+# Model Heading and Description
+model_heading = "ProductDetect-o-Matic: Shelf Wizard for Retail Magic"
+description = """ 🛒 Prepare to be amazed by ProductDetect-o-Matic! 🪄 Unveil the secrets of your shelves and keep products in check. From the mystical 'Empty Shelf' to the enigmatic 'Magical Product', our model is here to bring retail magic to life. Created by Foduu AI, your ultimate sidekick for retail success. 💼🎩✨
+🛍️ Ready to experience the retail revolution? Reach out at [email protected] and let's make your shelves enchanting! Giving a thumbs up won't make you a retail wizard, but it's a step closer to making your shelves smile! 🚀👍🪄
+📧 Contact us: [email protected]
+👍 Like | Join the Retail Adventure!"""
+
+image_path = [
+    ['test/test1.jpg', 'foduucom/product-detection-in-shelf-yolov8', 640, 0.25, 0.45],
+    ['test/test2.jpg', 'foduucom/product-detection-in-shelf-yolov8', 640, 0.25, 0.45]
+]
+
+# Load YOLO model
+model = YOLO('foduucom/product-detection-in-shelf-yolov8')
+
+############################################################# Image Inference ############################################################
+def yolov8_img_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 = 0.45,
+):
+    model = YOLO(model_path)
+    model.overrides['conf'] = conf_threshold
+    model.overrides['iou'] = iou_threshold
+    model.overrides['agnostic_nms'] = False
+    model.overrides['max_det'] = 1000
+    results = model.predict(image)
+    render = render_result(model=model, image=image, result=results[0])
+
+    return render
+
+
+inputs_image = [
+    gr.inputs.Image(type="filepath", label="Input Image"),
+    gr.inputs.Dropdown(["foduucom/product-detection-in-shelf-yolov8"], default="foduucom/product-detection-in-shelf-yolov8", 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=0.45, step=0.05, label="IOU Threshold"),
+]
+
+outputs_image = gr.outputs.Image(type="filepath", label="Output Image")
+interface_image = gr.Interface(
+    fn=yolov8_img_inference,
+    inputs=inputs_image,
+    outputs=outputs_image,
+    title=model_heading,
+    description=description,
+    examples=image_path,
+    cache_examples=False,
+    theme='huggingface'
+)
+
+################################################## Video Inference ############################################################
+def show_preds_video(
+    video_path: str = None,
+    model_path: str = None,
+    image_size: int = 640,
+    conf_threshold: float = 0.25,
+    iou_threshold: float = 0.45,
+):
+    cap = cv2.VideoCapture(video_path)
+
+    while cap.isOpened():
+        success, frame = cap.read()
+
+        if success:
+            model = YOLO(model_path)
+            model.overrides['conf'] = conf_threshold
+            model.overrides['iou'] = iou_threshold
+            model.overrides['agnostic_nms'] = False
+            model.overrides['max_det'] = 1000
+            results = model.predict(frame)
+            annotated_frame = results[0].plot()
+
+            if cv2.waitKey(1) & 0xFF == ord("q"):
+                break
+        else:
+            break
+
+    cap.release()
+    cv2.destroyAllWindows()
+
+
+inputs_video = [
+    gr.components.Video(type="filepath", label="Input Video"),
+    gr.inputs.Dropdown(["foduucom/product-detection-in-shelf-yolov8"], default="foduucom/product-detection-in-shelf-yolov8", 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=0.45, step=0.05, label="IOU Threshold"),
+]
+
+outputs_video = gr.outputs.Image(type="filepath", label="Output Video")
+video_path = [['test/testvideo.mp4', 'foduucom/product-detection-in-shelf-yolov8', 640, 0.25, 0.45]]
+interface_video = gr.Interface(
+    fn=show_preds_video,
+    inputs=inputs_video,
+    outputs=outputs_video,
+    title=model_heading,
+    description=description,
+    examples=video_path,
+    cache_examples=False,
+    theme='huggingface'
+)
+
+gr.TabbedInterface(
+    [interface_image, interface_video],
+    tab_names=['Image inference', 'Video inference']
+).queue().launch()