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')


# def response(image):
#   print(image)
#   results = model(image)
#   for i, r in enumerate(results):
      
#     # Plot results image
#       im_bgr = r.plot()  
#       im_rgb = im_bgr[..., ::-1]  # Convert BGR to RGB
    
#     # im_rgb = Image.fromarray(im_rgb)
      
#       return im_rgb

def response2(image: gr.Image = None,image_size: gr.Slider = 640, conf_threshold: gr.Slider = 0.5, iou_threshold: gr.Slider = 0.6):
                    
    model = YOLO('best (1).pt')
    
    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
   for i, r in enumerate(results):
      
    # Plot results image
      im_bgr = r.plot()  
      im_rgb = im_bgr[..., ::-1]  # Convert BGR to RGB
    
    # im_rgb = Image.fromarray(im_rgb)
    return im_rgb


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=0.25,
                     step=0.05, label="Confidence Threshold"),
    gr.Slider(minimum=0.0, maximum=1.0, value=0.45,
                     step=0.05, label="IOU Threshold"),
]


outputs = gr.Image( type="filepath", label="Output Image")

title = "YOLOv8 Custom Object Detection by Uyen Nguyen"


# examples = [['one.jpg', 900, 0.5, 0.8],
#             ['two.jpg', 1152, 0.05, 0.05],
#             ['three.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()