import gradio as gr
import random
import time

fp = open("/deep2/u/eprakash/MedSegDiff/data/ISIC/ISBI2016_ISIC_Part3B_Training_GroundTruth.csv")
image_ids = []
for line in fp:
    image_ids.append(line.split(",")[0].split("_")[1])
image_ids = image_ids[:700]
rankings = []
def load_next(rank, img_1, img_2, img_3, img_4, img_5, example, ids=image_ids):
    if example == len(image_ids):
        return [None, None, None, None, None, None, None]
    else:
        rankings.append(str(image_ids[int(example)-1]) + "," + rank)
        r_fp = open("ranks/isic_ranks_" + str(int(example) - 1) +".csv", "w")
        for r in rankings:
            r_fp.write(r + "\n")
        r_fp.close()
        example += 1
        rank = ""
        img_1 = gr.Image(label="Sample #1", value="/deep2/u/eprakash/Diffusion-based-Segmentation/isic_synthetic_data/" + str(image_ids[int(example)-1]) + "_synthetic_0.jpg", interactive=False)
        img_2 = gr.Image(label="Sample #2", value="/deep2/u/eprakash/Diffusion-based-Segmentation/isic_synthetic_data/" + str(image_ids[int(example)-1]) + "_synthetic_1.jpg", interactive=False)
        img_3 = gr.Image(label="Sample #3", value="/deep2/u/eprakash/Diffusion-based-Segmentation/isic_synthetic_data/" + str(image_ids[int(example)-1]) + "_synthetic_2.jpg", interactive=False)
        img_4 = gr.Image(label="Sample #4", value="/deep2/u/eprakash/Diffusion-based-Segmentation/isic_synthetic_data/" + str(image_ids[int(example)-1]) + "_synthetic_3.jpg", interactive=False)
        img_5 = gr.Image(label="Sample #5", value="/deep2/u/eprakash/Diffusion-based-Segmentation/isic_synthetic_data/" + str(image_ids[int(example)-1]) + "_synthetic_4.jpg", interactive=False)
        return [rank, img_1, img_2, img_3, img_4, img_5, example]

with gr.Blocks() as demo:
    example = gr.Number(label="Example #", value=1, interactive=False)
    rank = gr.Textbox(label="Rankings (Best to worst, comma-separated, no spaces)")
    with gr.Row():
        img_1 = gr.Image(label="Sample #1", value="/deep2/u/eprakash/Diffusion-based-Segmentation/isic_synthetic_data/" + str(image_ids[0]) + "_synthetic_0.jpg", interactive=False)
        img_2 = gr.Image(label="Sample #2", value="/deep2/u/eprakash/Diffusion-based-Segmentation/isic_synthetic_data/" + str(image_ids[0]) + "_synthetic_1.jpg", interactive=False)
        img_3 = gr.Image(label="Sample #3", value="/deep2/u/eprakash/Diffusion-based-Segmentation/isic_synthetic_data/" + str(image_ids[0]) + "_synthetic_2.jpg", interactive=False)
        img_4 = gr.Image(label="Sample #4", value="/deep2/u/eprakash/Diffusion-based-Segmentation/isic_synthetic_data/" + str(image_ids[0]) + "_synthetic_3.jpg", interactive=False)
        img_5 = gr.Image(label="Sample #5", value="/deep2/u/eprakash/Diffusion-based-Segmentation/isic_synthetic_data/" + str(image_ids[0]) + "_synthetic_4.jpg", interactive=False)
    next_btn = gr.Button(value="Next")
    next_btn.click(fn=load_next, inputs=[rank, img_1, img_2, img_3, img_4, img_5, example], outputs=[rank, img_1, img_2, img_3, img_4, img_5, example], queue=False)
    demo.queue()
    demo.launch(share=True)

fp.close()