# import gradio as gr
# import cv2
# from ultralytics import YOLO

# model0 = YOLO('yolov8.pt')
# model1 = YOLO('yolov8.pt')
# model2 = YOLO('yolov8.pt')
# model3 = YOLO('yolov8.pt')
# model4 = YOLO('yolov8.pt')
# model5 = YOLO('yolov8.pt')

# models = [model0, model1, model2, model3, model4, model5]
# model_names = ["Model 0", "Model 1", "Model 2", "Model 3", "Model 4", "Model 5"]

# def show_preds_image(image, model_selection=0):
#     img = image.read()
#     outputs = models[model_selection].predict(source=img)
#     results = outputs[0].cpu().numpy()
#     for i, det in enumerate(results.boxes.xyxy):
#         cv2.rectangle(
#             img,
#             (int(det[0]), int(det[1])),
#             (int(det[2]), int(det[3])),
#             color=(0, 0, 255),
#             thickness=2,
#             lineType=cv2.LINE_AA
#         )
#     return cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

# interface_image = gr.Interface(
#     fn=show_preds_image,
#     inputs=[
#         gr.inputs.Image(type="file", label="Input Image"),
#         gr.inputs.Dropdown(
#             choices=[(name, idx) for idx, name in enumerate(model_names)],
#             label="Select Model",
#             default=0
#         )
#     ],
#     outputs=gr.outputs.Image(type="numpy", label="Output Image"),
#     title="Panicle detector app",
# )

# interface_image.launch()






import gradio as gr
import cv2
import requests
import os

from ultralytics import YOLO



model0 = YOLO('yolov8.pt')
model1 = YOLO('yolov8.pt')
model2 = YOLO('yolov8.pt')
model3 = YOLO('yolov8.pt')
model4 = YOLO('yolov8.pt')
model5 = YOLO('yolov8.pt')

models = []
models.append(model0)
models.append(model1)
models.append(model2)
models.append(model3)
models.append(model4)
models.append(model5)


path  = [['flowering.png']]




def show_preds_image(image_path, selection):
    image = cv2.imread(image_path)
    outputs = models[selection].predict(source=image_path)
    results = outputs[0].cpu().numpy()
    for i, det in enumerate(results.boxes.xyxy):
        cv2.rectangle(
            image,
            (int(det[0]), int(det[1])),
            (int(det[2]), int(det[3])),
            color=(0, 0, 255),
            thickness=2,
            lineType=cv2.LINE_AA
        )
    return cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

inputs = [
    gr.components.Image(type="filepath", label="Input Image"),
    gr.components.Dropdown(choices=[str(i) for i in range(len(models))], label="Select Model", type="index"),
]
outputs_image = [
    gr.components.Image(type="numpy", label="Output Image"),
]
model_select = []
interface_image = gr.Interface(
    fn=show_preds_image,
    inputs=inputs,
    outputs=outputs_image,
    title="Panicle detector app",
    examples=path,
    cache_examples=False,
)



gr.TabbedInterface(
    [interface_image],
    tab_names=['Image inference']
).queue().launch()