Simplifica script

app.py

@@ -1,48 +1,27 @@
 import gradio as gr
 import os
-#os.system("pip -qq install yoloxdetect==0.0.7")
-#os.system("pip -qq install yoloxdetect")
 import torch
 import json
 import yoloxdetect2.helpers as yoloxdetect
-#from yoloxdetect import YoloxDetector
-
-
-# Images
-torch.hub.download_url_to_file('https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg', 'zidane.jpg')
-torch.hub.download_url_to_file('https://raw.githubusercontent.com/obss/sahi/main/tests/data/small-vehicles1.jpeg', 'small-vehicles1.jpeg')
-torch.hub.download_url_to_file('https://raw.githubusercontent.com/Megvii-BaseDetection/YOLOX/main/assets/dog.jpg', 'dog.jpg')
 
 model = yoloxdetect.YoloxDetector2('kadirnar/yolox_s-v0.1.1', 'configs.yolox_s', device="cpu", hf_model=True)
+image_size = 640
 
 def yolox_inference(
     image_path: gr.inputs.Image = None,
-    model_path: gr.inputs.Dropdown = 'kadirnar/yolox_s-v0.1.1',
-    config_path: gr.inputs.Textbox = 'configs.yolox_s',
-    image_size: gr.inputs.Slider = 640
 ):
     """
     YOLOX inference function
     Args:
         image: Input image
-        model_path: Path to the model
-        config_path: Path to the config file
-        image_size: Image size
     Returns:
         Rendered image
     """
 
-    #model = YoloxDetector(model_path, config_path=config_path, device="cpu", hf_model=True)
-    #pred = model.predict(image_path=image_path, image_size=image_size)
     pred2 = []
     if model :
         model.torchyolo = True
         pred2 = model.predict(image_path=image_path, image_size=image_size)
-        #text = "Ola"
-        #print (vars(model))
-        #print (pred2[0])
-        #print (pred2[1])
-        #print (pred2[2])
 
     
     tensor = {
@@ -51,7 +30,6 @@ def yolox_inference(
     }
 
     if pred2 is not None:
-        #print (pred2[3])
         for i, element in enumerate(pred2[0]):
             object = {}
             itemclass = round(pred2[2][i].item())
@@ -64,7 +42,6 @@ def yolox_inference(
             object["h"] = element[3].item()
             tensor["tensorflow"].append(object)
   
-    #print(tensor)
 
     text = json.dumps(tensor)
     return text
@@ -72,18 +49,15 @@ def yolox_inference(
 
 inputs = [
     gr.inputs.Image(type="filepath", label="Input Image"),
-    gr.inputs.Textbox(lines=1, label="Model Path", default="kadirnar/yolox_s-v0.1.1"),
-    gr.inputs.Textbox(lines=1, label="Config Path", default="configs.yolox_s"),
-    gr.inputs.Slider(minimum=320, maximum=1280, default=640, step=32, label="Image Size"),
 ]
 
 outputs = gr.outputs.Image(type="filepath", label="Output Image")
 title = "SIMULADOR PARA RECONHECIMENTO DE IMAGEM"
 
 examples = [
-    ["small-vehicles1.jpeg", "kadirnar/yolox_m-v0.1.1", "configs.yolox_m", 640],
-    ["zidane.jpg", "kadirnar/yolox_s-v0.1.1", "configs.yolox_s", 640],
-    ["dog.jpg", "kadirnar/yolox_tiny-v0.1.1", "configs.yolox_tiny", 640],
+    ["small-vehicles1.jpeg"],
+    ["zidane.jpg"],
+    ["dog.jpg"],
 ]
 
 demo_app = gr.Interface(
@@ -94,7 +68,6 @@ demo_app = gr.Interface(
     examples=examples,
     cache_examples=True,
     live=True,
-    theme='huggingface',
 )
 demo_app.launch(debug=True, server_name="192.168.0.153", server_port=8080, enable_queue=True)
 #demo_app.launch(debug=True, server_port=8083, enable_queue=True)