app1.py

import gradio as gr
import torch
import json
#import yolov7
import yolov7detect.helpers as yolov7d


# 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')
    
model_path =  "kadirnar/yolov7-v0.1" #"kadirnar/yolov7-tiny-v0.1"
image_size = 640
conf_threshold = 0.25
iou_threshold = 0.45

def yolov7_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,
):
    """
    YOLOv7 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
    """

    model = yolov7d.load_model(model_path, device="cpu", hf_model=True, trace=False)
    model.conf = conf_threshold
    model.iou = iou_threshold
    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 = "Yolov7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors"

examples = [['small-vehicles1.jpeg'], ['zidane.jpg']]
demo_app = gr.Interface(
    fn=yolov7_inference,
    inputs=inputs,
    outputs=["text"],
    title=title,
    examples=examples,
    #cache_examples=True,
    #theme='huggingface',
)
demo_app.launch(debug=True, server_name="192.168.0.153", server_port=8081, enable_queue=True)
#demo_app.launch(debug=True, server_port=8083, enable_queue=True)
#demo_app.launch(debug=True, enable_queue=True)