import gradio as gr
import matplotlib.pyplot as plt
from PIL import Image
from ultralyticsplus import YOLO, render_result
import cv2
import numpy as np

model = YOLO('best (1).pt')

 # for i, r in enumerate(results):
      
 #    # Plot results image
 #      im_bgr = r.plot()  
 #      im_rgb = im_bgr[..., ::-1]  # Convert BGR to RGB

def response(image):
  print(image)
  results = model(image)
  text=""
  for r in results:
    conf = np.array(r.boxes.conf)
    cls = np.array(r.boxes.cls)
    cls = cls.astype(int)
    xywh = np.array(r.boxes.xywh)
    xywh = xywh.astype(int)  
      
    for con, cl, xy in zip(conf, cls, xywh):
        cone = con.astype(float)
        conef = round(cone,3)
        conef = conef * 100
        text += (f"Detected {name[cl]} with confidence {round(conef,1)}% at ({xy[0]},{xy[1]})\n")
        
        
    # im_rgb = Image.fromarray(im_rgb)
    
      
    return text

name = ['grenade','knife','pistol','rifle']

def response2(image: gr.Image = None,image_size: gr.Slider = 640, conf_threshold: gr.Slider = 0.3, iou_threshold: gr.Slider = 0.6):

    results = model.predict(image, conf=conf_threshold, iou=iou_threshold, imgsz=image_size)

    box = results[0].boxes

    render = render_result(model=model, image=image, result=results[0], rect_th = 1, text_th = 1)
   
   
    text = response(image)
   
    
    # xywh = int(results.boxes.xywh)
    # x = xywh[0]
    # y = xywh[1]
        
   
            
    return render,text


inputs = [
    gr.Image(type="filepath",  label="Input Image"),
    gr.Slider(minimum=320, maximum=1280, value=640,
                     step=32, label="Image Size"),
    gr.Slider(minimum=0.0, maximum=1.0, value=0.3,
                     step=0.05, label="Confidence Threshold"),
    gr.Slider(minimum=0.0, maximum=1.0, value=0.6,
                     step=0.05, label="IOU Threshold"),
]


outputs = [gr.Image( type="filepath", label="Output Image"),
           gr.Textbox(label="Result")
          ]

title = "YOLOv8 Custom Object Detection by Uyen Nguyen"


examples = [['th (11).jpg', 900, 0.5, 0.8],
            ['th (8).jpg', 1152, 0.05, 0.05],
            ['th (3).jpg', 1024, 0.25, 0.25],
            # ['four.jpg', 832, 0.3, 0.3]]
           ]

# yolo_app = gr.Interface(
#     fn=yoloV8_func,
#     inputs=inputs,
#     outputs=outputs,
#     title=title,
#     # examples=examples,
#     # cache_examples=True,
# )

# Launch the Gradio interface in debug mode with queue enabled
# yolo_app.launch()

iface = gr.Interface(fn=response2, inputs=inputs, outputs=outputs)
iface.launch(debug=True)