adiciona first

app.py

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+import gradio as gr
+import torch
+import json
+import yolov5
+
+# 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/WongKinYiu/yolov7/main/inference/images/image3.jpg', 'image3.jpg')
+torch.hub.download_url_to_file('https://github.com/ultralytics/yolov5/releases/download/v5.0/yolov5s.pt','yolov5s.pt')
+
+model_path = "yolov5s.pt" #"yolov5m.pt", "yolov5l.pt", "yolov5x.pt",
+image_size = 640,
+conf_threshold = 0.25,
+iou_threshold = 0.45,
+model = yolov5.load(model_path, device="cpu")
+
+def yolov5_inference(
+    image: gr.inputs.Image = None,
+   
+):
+    """
+    YOLOv5 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
+    """
+    
+    results = model([image], size=image_size)
+    tensor = {
+      "tensorflow": [ 
+      ]
+    }
+
+    if results.pred is not None:
+        for i, element in enumerate(results.pred[0]):
+            object = {}
+            #print (element[0])
+            itemclass = round(element[5].item())
+            object["classe"] = itemclass
+            object["nome"] = results.names[itemclass]
+            object["score"] = element[4].item()
+            object["x"] = element[0].item()
+            object["y"] = element[1].item()
+            object["w"] = element[2].item()
+            object["h"] = element[3].item()
+            tensor["tensorflow"].append(object)
+          
+  
+
+    text = json.dumps(tensor)
+    #print (text)
+    return text #results.render()[0]
+        
+
+inputs = [
+    gr.inputs.Image(type="pil", label="Input Image"),
+]
+
+outputs = gr.outputs.Image(type="filepath", label="Output Image")
+title = "YOLOv5"
+description = "YOLOv5 is a family of object detection models pretrained on COCO dataset. This model is a pip implementation of the original YOLOv5 model."
+
+examples = [['zidane.jpg'], ['image3.jpg']]
+demo_app = gr.Interface(
+    fn=yolov5_inference,
+    inputs=inputs,
+    outputs=["text"],
+    title=title,
+    examples=examples,
+    #cache_examples=True,
+    #live=True,
+    #theme='huggingface',
+)
+demo_app.launch(debug=True, enable_queue=True)

requirements.txt

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+torch
+yolov5