Upload app.py

app.py

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+
+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
+ 
+# semua yolox model
+choices = ["YOLOXNano", "YOLOXTiny", "YOLOXS", "YOLOXM", "YOLOXL", "YOLOXX"]
+
+def main(input_img, models):
+    
+    #
+    fig, ax = plt.subplots() # pakai ini,jika tidak akan muncul error
+
+    # YOLOXNano models
+    if models == "YOLOXNano": 
+        model = YOLOXNano(pretrained="coco") 
+
+    # YOLOXTiny models
+    elif models == "YOLOXTiny": 
+        model = YOLOXTiny(pretrained="coco") 
+
+    # YOLOXS models   
+    elif models == "YOLOXS":       
+        model = YOLOXS(pretrained="coco") 
+
+    # YOLOXM models    
+    elif models == "YOLOXM": 
+        model = YOLOXM(pretrained="coco") 
+
+    # YOLOXL models    
+    elif models == "YOLOXL":
+        model = YOLOXL(pretrained="coco") 
+
+    # YOLOXX models   
+    elif models == "YOLOXX":      
+        model = YOLOXX(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=(512, 512),label = "Input Image"),
+         gr.inputs.Dropdown(choices= choices, type="value", default='YOLOXS', label="Model")]
+
+output = gr.outputs.Image(type="plot", label="Output Image")
+
+title = "YOLOX Demo(FT)"
+
+example = [[" "]]
+
+description = "Demo for YOLOX(Object Detection). Models are YOLOXNano - YOLOXX"           
+
+article = "<p style='text-align: center'>Untuk penjelasan lihat di <a href='https://github.com/sultanbst123/Text_summarization-id2id' target='_blank'><u>repo ku </u>😁</a></p>"
+
+# deploy
+iface = gr.Interface(main, 
+                    inputs = input,
+                    outputs = output, 
+                    title = title,
+                    article = article,
+                    description = description, 
+                    theme = "dark") 
+                    
+iface.launch(debug = True)