add app

app.py

@@ -0,0 +1,59 @@
+import numpy as np
+from keras_cv_attention_models.yolox import * # import all yolox model
+from keras_cv_attention_models.coco import data
+import matplotlib.pyplot as plt
+import gradio as gr
+ 
+
+choices = ["YOLOXNano", "YOLOXS"]
+
+def main(input_img, models):
+    
+    #
+    fig, ax = plt.subplots() 
+
+    # YOLOXNano models
+    if models == "YOLOXNano": 
+        model = YOLOXNano(pretrained="coco") 
+
+    # YOLOXS models   
+    elif models == "YOLOXS":       
+        model = YOLOXS(pretrained="coco") 
+        
+    # pass    
+    else:  
+        pass
+
+    # image pre processing yolox
+    preds = model(model.preprocess_input(input_img))
+    bboxs, lables, confidences = model.decode_predictions(preds)[0]
+    data.show_image_with_bboxes(input_img, bboxs, lables, confidences, num_classes=100,label_font_size=17, ax=ax)
+
+    return fig
+
+# define params
+
+input = [gr.inputs.Image(shape=(2000, 1500),label = "Input Image"),
+         gr.inputs.Dropdown(choices= choices, type="value", default='YOLOXS', label="Model")]
+
+output = gr.outputs.Image(type="plot", label="Output Image")
+
+title = "Demo"
+
+example = [["image1.jpeg ","YOLOXNano"],["image2.jpeg","YOLOXS"]]
+
+description = "Demo for YOLOX(Object Detection). Models are YOLOXNano - YOLOXX"           
+
+article = "<a href='https://github.com/Megvii-BaseDetection/YOLOX' target='_blank'><u>YOLOX </u></a>is an anchor-free version of YOLO, with a simpler design but better performance!<br><br><p style='text-align: center'>Untuk penjelasan lihat di <a href='https://github.com/sultanbst123/YOLOX-Demo' target='_blank'><u>repo ku </u>😁</a></p>"
+
+# deploy
+iface = gr.Interface(main, 
+                    inputs = input,
+                    outputs = output, 
+                    title = title,
+                    article = article,
+                    description = description, 
+                    examples = example, 
+                    theme = "dark") 
+                    
+iface.launch(debug = True)