import gradio as gr
import cv2
import os

from ultralytics import YOLO

# Remove the URLs containing 'mp4'
file_urls = [
    'Cyst.jpg',
    'Stone.jpg'
]

# Do not download files from URLs, use local files directly

model = YOLO('best.pt')
path = [['Cyst.jpg'], ['Stone.jpg'],['Normal.jpg']]


def detect_objects_on_image(image_path):
    image = cv2.imread(image_path)
    model = YOLO("best.pt")
    results = model.predict(image_path)
    result = results[0]
    output = []

    for box in result.boxes:
        x1, y1, x2, y2 = [round(x) for x in box.xyxy[0].tolist()]
        class_id = box.cls[0].item()
        prob = round(box.conf[0].item(), 2)
        output.append([x1, y1, x2, y2, result.names[class_id], prob])

        cv2.rectangle(
            image,
            (x1, y1),
            (x2, y2),
            color=(0, 0, 255),
            thickness=2,
            lineType=cv2.LINE_AA
        )

        # Change font and size
        font = cv2.FONT_HERSHEY_SIMPLEX
        font_size = 0.5  # Adjust the font size as needed
        cv2.putText(image, result.names[class_id], (x1, y1), font, font_size, (36, 255, 12), 1)

    return cv2.cvtColor(image, cv2.COLOR_BGR2RGB)


inputs_image = [
    gr.components.Image(type="filepath", label="Input Image"),
]
outputs_image = [
    gr.components.Image(type="numpy", label="Output Image"),
]
interface_image = gr.Interface(
    fn=detect_objects_on_image,
    inputs=inputs_image,
    outputs=outputs_image,
    title="Kidney Stone and Cyst detection",
    examples=path,
    cache_examples=False,
)
'''
def show_preds_video(video_path):
    cap = cv2.VideoCapture(video_path)
    while(cap.isOpened()):
        ret, frame = cap.read()
        if ret:
            frame_copy = frame.copy()
            outputs = model.predict(source=frame)
            results = outputs[0].cpu().numpy()
            for i, det in enumerate(results.boxes.xyxy):
                cv2.rectangle(
                    frame_copy,
                    (int(det[0]), int(det[1])),
                    (int(det[2]), int(det[3])),
                    color=(0, 0, 255),
                    thickness=2,
                    lineType=cv2.LINE_AA
                )
            yield cv2.cvtColor(frame_copy, cv2.COLOR_BGR2RGB)
'''

gr.TabbedInterface(
    [interface_image],
    tab_names=['Shamim MD Jony']
).launch(share=True)