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README.md

@@ -1,12 +1,6 @@
 ---
-title: Gradio
-emoji: 🐨
-colorFrom: blue
-colorTo: purple
+title: gradio
+app_file: ct_seg_gradio.py
 sdk: gradio
-sdk_version: 4.14.0
-app_file: app.py
-pinned: false
+sdk_version: 3.47.1
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

ct_seg_gradio.py

@@ -0,0 +1,117 @@
+import gradio as gr
+import random
+import time
+import os
+from glob import glob
+from PIL import Image
+import torchvision.transforms as transforms
+
+num_rank = 200
+image_prefix = "/deep/u/eprakash/AngioSeg/diffusion/ct_synthetic_60/synth/"
+mask_prefix = "/deep/u/eprakash/ct/orig_masks/"
+image_ids = []
+mask_ids = []
+img_list = "/deep/u/eprakash/ct/train_60.csv"
+with open(img_list) as fp:
+    for line in fp:
+        ex = line.strip().split(",")[0]
+        mask_ids.append(ex.split("_")[0] + "_mask_" + ex.split("_")[1])
+        image_ids.append("('" + ex + "',)")
+image_ids = image_ids[301:501]
+mask_ids = mask_ids[301:501]
+save_path = "ct_seg_ranks" 
+
+def is_int(s):
+    try:
+        int(s)
+        return True
+    except ValueError:
+        return False
+
+def load_img(img_path, size=512):
+    img = Image.open(img_path).convert('RGB')
+    transform_list = [transforms.Resize((size, size))]
+    transform = transforms.Compose(transform_list)
+    img = transform(img)
+    return img
+
+def find_completed_idxs(save_path=save_path):
+    files = os.listdir(save_path)
+    incorrect_files = []
+    if len(files) == 0:
+        return [-1], []
+    else:
+        file_list = []
+        for f in files:
+            f_name = int(f.split(".")[0])
+            with open(save_path + "/" + f) as fp:
+                for line in fp:
+                    items = line.strip().split(",")
+                    if (len(items) != 5 and f_name != -1):
+                        incorrect_files.append(f_name)
+                    else:
+                        if ((not is_int(items[1].strip()) or not is_int(items[2].strip()) or not is_int(items[3].strip()) or not is_int(items[4].strip())) and f_name != -1):
+                            incorrect_files.append(f_name)
+            file_list.append(f_name)
+        file_list = sorted(file_list)
+        incorrect_files = sorted(incorrect_files)
+        return file_list, incorrect_files
+
+def load_next(rank, img_1, mask_1, img_2, mask_2, img_3, mask_3, img_4, mask_4, example, ids=image_ids, image_prefix=image_prefix, save_path=save_path):
+    file_list, incorrect_files = find_completed_idxs()
+    print(str(file_list) + " " + str(incorrect_files))
+    if (int(example) not in file_list or int(example) in incorrect_files):
+        r = str(image_ids[int(example)]).split(",")[0].split("(")[1] + "," + rank
+        r_fp = open(save_path + "/" + str(int(example)) +".txt", "w")
+        r_fp.write(r + "\n")
+        r_fp.close()
+    file_list, incorrect_files = find_completed_idxs()
+    if (len(incorrect_files) != 0):
+        example = incorrect_files[-1]
+    else:
+        example = file_list[-1] + 1
+    if int(example) == num_rank:
+        rank = "DONE!"
+        example = -1
+        mask_1 = gr.Image(label="Mask", value=load_img("/deep/u/eprakash/blank.jpg"), interactive=False)
+        img_1 = gr.Image(label="Sample #1", value=load_img("/deep/u/eprakash/blank.jpg"), interactive=False)
+        mask_2 = gr.Image(label="Mask", value=load_img("/deep/u/eprakash/blank.jpg"), interactive=False)
+        img_2 = gr.Image(label="Sample #2", value=load_img("/deep/u/eprakash/blank.jpg"), interactive=False)
+        mask_3 = gr.Image(label="Mask", value=load_img("/deep/u/eprakash/blank.jpg"), interactive=False)
+        img_3 = gr.Image(label="Sample #3", value=load_img("/deep/u/eprakash/blank.jpg"), interactive=False)
+        mask_4 = gr.Image(label="Mask", value=load_img("/deep/u/eprakash/blank.jpg"), interactive=False)
+        img_4 = gr.Image(label="Sample #4", value=load_img("/deep/u/eprakash/blank.jpg"), interactive=False)
+    else:
+        rank = ""
+        img_1 = gr.Image(label="Sample #1", value=load_img(image_prefix + str(image_ids[int(example)]) + "_synthetic_0.png"), interactive=False)
+        mask_1 = gr.Image(label="Mask", value=load_img(mask_prefix + str(mask_ids[int(example)]) + ".jpg"), interactive=False)
+        img_2 = gr.Image(label="Sample #2", value=load_img(image_prefix+ str(image_ids[int(example)]) + "_synthetic_1.png"), interactive=False)
+        mask_2 = gr.Image(label="Mask", value=load_img(mask_prefix + str(mask_ids[int(example)]) + ".jpg"), interactive=False)
+        img_3 = gr.Image(label="Sample #3", value=load_img(image_prefix + str(image_ids[int(example)]) + "_synthetic_2.png"), interactive=False)
+        mask_3 = gr.Image(label="Mask", value=load_img(mask_prefix + str(mask_ids[int(example)]) + ".jpg"), interactive=False)
+        img_4 = gr.Image(label="Sample #4", value=load_img(image_prefix + str(image_ids[int(example)]) + "_synthetic_3.png"), interactive=False)
+        mask_4 = gr.Image(label="Mask", value=load_img(mask_prefix + str(mask_ids[int(example)]) + ".jpg"), interactive=False)
+    return [rank, img_1, mask_1, img_2, mask_2, img_3, mask_3, img_4, mask_4, example]
+
+with gr.Blocks() as demo:
+    last_idx = -1
+    example = gr.Number(label="Example #. Click next for #-1 (blank starting page).", value=last_idx, interactive=False)
+    rank = gr.Textbox(label="Rankings (Best to worst, comma-separated, no spaces).")
+    with gr.Column(scale=1):
+        with gr.Row():
+            mask_1 = gr.Image(label="Mask", value=load_img("/deep/u/eprakash/blank.jpg"), interactive=False)
+            img_1 = gr.Image(label="Sample #1", value=load_img("/deep/u/eprakash/blank.jpg"), interactive=False)
+        with gr.Row():
+            mask_2 = gr.Image(label="Mask", value=load_img("/deep/u/eprakash/blank.jpg"), interactive=False)
+            img_2 = gr.Image(label="Sample #2", value=load_img("/deep/u/eprakash/blank.jpg"), interactive=False)
+        with gr.Row():
+            mask_3 = gr.Image(label="Mask", value=load_img("/deep/u/eprakash/blank.jpg"), interactive=False)
+            img_3 = gr.Image(label="Sample #3", value=load_img("/deep/u/eprakash/blank.jpg"), interactive=False)
+        with gr.Row():
+            mask_4 = gr.Image(label="Mask", value=load_img("/deep/u/eprakash/blank.jpg"), interactive=False)
+            img_4 = gr.Image(label="Sample #4", value=load_img("/deep/u/eprakash/blank.jpg"), interactive=False)
+    next_btn = gr.Button(value="Next")
+    next_btn.click(fn=load_next, inputs=[rank, img_1, mask_1, img_2, mask_2, img_3, mask_3, img_4, mask_4, example], outputs=[rank, img_1, mask_1, img_2, mask_2, img_3, mask_3, img_4, mask_4, example], queue=False)
+    demo.queue()
+    demo.launch(share=True)
+

cxr_gradio.py

@@ -0,0 +1,77 @@
+import gradio as gr
+import random
+import time
+import os
+from glob import glob
+from PIL import Image
+import torchvision.transforms as transforms
+
+image_prefix = "/deep/u/eprakash/AngioSeg/diffusion/cxr_synthetic_data_25_no_transform/synth/"
+image_ids = glob(os.path.join(image_prefix, '*' + '.png'))
+image_ids = list(set([os.path.splitext(os.path.basename(p))[0].split("_")[0] for p in image_ids]))
+save_path = "cxr_ranks"
+
+def load_img(img_path, size=1024):
+    img = Image.open(img_path).convert('RGB')
+    transform_list = [transforms.Resize((size, size))]
+    transform = transforms.Compose(transform_list)
+    img = transform(img)
+    return img
+
+def find_completed_idxs(save_path=save_path):
+    files = os.listdir(save_path)
+    if len(files) == 0:
+        return [-1]
+    else:
+        file_list = []
+        for f in files:
+            f = int(f.split(".")[0])
+            file_list.append(f)
+        file_list = sorted(file_list)
+        return file_list
+
+def load_next(rank, img_1, mask_1, img_2, mask_2, img_3, mask_3, img_4, mask_4, example, ids=image_ids, image_prefix=image_prefix, save_path=save_path):
+    if int(example) == len(image_ids) - 1:
+        return [None, None, None, None, None, None, None]
+    else:
+        file_list = find_completed_idxs()
+        if (int(example) not in file_list):
+            r = str(image_ids[int(example)]) + "," + rank
+            r_fp = open(save_path + "/" + str(int(example)) +".txt", "w")
+            r_fp.write(r + "\n")
+            r_fp.close()
+        file_list = find_completed_idxs()
+        example = file_list[-1] + 1
+        rank = ""
+        img_1 = gr.Image(label="Sample #1", value=load_img(image_prefix + str(image_ids[int(example)]) + "_synthetic_0.png"), interactive=False)
+        mask_1 = gr.Image(label="Mask", value=load_img(image_prefix + str(image_ids[int(example)]) + "_synthetic_mask_1.png"), interactive=False)
+        img_2 = gr.Image(label="Sample #2", value=load_img(image_prefix+ str(image_ids[int(example)]) + "_synthetic_1.png"), interactive=False)
+        mask_2 = gr.Image(label="Mask", value=load_img(image_prefix + str(image_ids[int(example)]) + "_synthetic_mask_2.png"), interactive=False)
+        img_3 = gr.Image(label="Sample #3", value=load_img(image_prefix + str(image_ids[int(example)]) + "_synthetic_2.png"), interactive=False)
+        mask_3 = gr.Image(label="Mask", value=load_img(image_prefix + str(image_ids[int(example)]) + "_synthetic_mask_3.png"), interactive=False)
+        img_4 = gr.Image(label="Sample #4", value=load_img(image_prefix + str(image_ids[int(example)]) + "_synthetic_3.png"), interactive=False)
+        mask_4 = gr.Image(label="Mask", value=load_img(image_prefix + str(image_ids[int(example)]) + "_synthetic_mask_4.png"), interactive=False)
+        return [rank, img_1, mask_1, img_2, mask_2, img_3, mask_3, img_4, mask_4, example]
+
+with gr.Blocks() as demo:
+    last_idx = -1
+    example = gr.Number(label="Example #. Click next for #-1 (blank starting page).", value=last_idx, interactive=False)
+    rank = gr.Textbox(label="Rankings (Best to worst, comma-separated, no spaces).")
+    with gr.Column(scale=1):
+        with gr.Row():
+            mask_1 = gr.Image(label="Mask", value=load_img("/deep/u/eprakash/blank.jpg"), interactive=False)
+            img_1 = gr.Image(label="Sample #1", value=load_img("/deep/u/eprakash/blank.jpg"), interactive=False)
+        with gr.Row():
+            mask_2 = gr.Image(label="Mask", value=load_img("/deep/u/eprakash/blank.jpg"), interactive=False)
+            img_2 = gr.Image(label="Sample #2", value=load_img("/deep/u/eprakash/blank.jpg"), interactive=False)
+        with gr.Row():
+            mask_3 = gr.Image(label="Mask", value=load_img("/deep/u/eprakash/blank.jpg"), interactive=False)
+            img_3 = gr.Image(label="Sample #3", value=load_img("/deep/u/eprakash/blank.jpg"), interactive=False)
+        with gr.Row():
+            mask_4 = gr.Image(label="Mask", value=load_img("/deep/u/eprakash/blank.jpg"), interactive=False)
+            img_4 = gr.Image(label="Sample #4", value=load_img("/deep/u/eprakash/blank.jpg"), interactive=False)
+    next_btn = gr.Button(value="Next")
+    next_btn.click(fn=load_next, inputs=[rank, img_1, mask_1, img_2, mask_2, img_3, mask_3, img_4, mask_4, example], outputs=[rank, img_1, mask_1, img_2, mask_2, img_3, mask_3, img_4, mask_4, example], queue=False)
+    demo.queue()
+    demo.launch(share=True)
+

cxr_seg_gradio.py

@@ -0,0 +1,113 @@
+import gradio as gr
+import random
+import time
+import os
+from glob import glob
+from PIL import Image
+import torchvision.transforms as transforms
+
+save_path = "cxr_seg_ranks"
+num_rank = 200
+image_prefix = "/deep/u/eprakash/AngioSeg/diffusion/cxr_synthetic_60/synth/"
+mask_prefix = "/deep/u/eprakash/AngioSeg/diffusion/cxr_synthetic_60/orig/"
+image_ids = []
+img_list = "/deep/u/eprakash/cxr/train_non_25.csv"
+with open(img_list) as fp:
+    for line in fp:
+        image_ids.append("('" + line.strip().split(",")[0] + "',)")
+image_ids = image_ids[301:501]
+
+def is_int(s):
+    try:
+        int(s)
+        return True
+    except ValueError:
+        return False
+
+def load_img(img_path, size=512):
+    img = Image.open(img_path).convert('RGB')
+    transform_list = [transforms.Resize((size, size))]
+    transform = transforms.Compose(transform_list)
+    img = transform(img)
+    return img
+
+def find_completed_idxs(save_path=save_path):
+    files = os.listdir(save_path)
+    incorrect_files = []
+    if len(files) == 0:
+        return [-1], []
+    else:
+        file_list = []
+        for f in files:
+            f_name = int(f.split(".")[0])
+            with open(save_path + "/" + f) as fp:
+                for line in fp:
+                    items = line.strip().split(",")
+                    if (len(items) != 5 and f_name != -1):
+                        incorrect_files.append(f_name)
+                    else:
+                        if ((not is_int(items[1].strip()) or not is_int(items[2].strip()) or not is_int(items[3].strip()) or not is_int(items[4].strip())) and f_name != -1):
+                            incorrect_files.append(f_name)
+            file_list.append(f_name)
+        file_list = sorted(file_list)
+        incorrect_files = sorted(incorrect_files)
+        return file_list, incorrect_files
+
+def load_next(rank, img_1, mask_1, img_2, mask_2, img_3, mask_3, img_4, mask_4, example, ids=image_ids, image_prefix=image_prefix, save_path=save_path):
+    file_list, incorrect_files = find_completed_idxs()
+    print(str(file_list) + " " + str(incorrect_files))
+    if (int(example) not in file_list or int(example) in incorrect_files):
+        r = str(image_ids[int(example)]).split(",")[0].split("(")[1] + "," + rank
+        r_fp = open(save_path + "/" + str(int(example)) +".txt", "w")
+        r_fp.write(r + "\n")
+        r_fp.close()
+    file_list, incorrect_files = find_completed_idxs()
+    if (len(incorrect_files) != 0):
+        example = incorrect_files[-1]
+    else:
+        example = file_list[-1] + 1
+    if int(example) == num_rank:
+        rank = "DONE!"
+        example = -1
+        mask_1 = gr.Image(label="Mask", value=load_img("/deep/u/eprakash/blank.jpg"), interactive=False)
+        img_1 = gr.Image(label="Sample #1", value=load_img("/deep/u/eprakash/blank.jpg"), interactive=False)
+        mask_2 = gr.Image(label="Mask", value=load_img("/deep/u/eprakash/blank.jpg"), interactive=False)
+        img_2 = gr.Image(label="Sample #2", value=load_img("/deep/u/eprakash/blank.jpg"), interactive=False)
+        mask_3 = gr.Image(label="Mask", value=load_img("/deep/u/eprakash/blank.jpg"), interactive=False)
+        img_3 = gr.Image(label="Sample #3", value=load_img("/deep/u/eprakash/blank.jpg"), interactive=False)
+        mask_4 = gr.Image(label="Mask", value=load_img("/deep/u/eprakash/blank.jpg"), interactive=False)
+        img_4 = gr.Image(label="Sample #4", value=load_img("/deep/u/eprakash/blank.jpg"), interactive=False)
+    else:
+        rank = ""
+        img_1 = gr.Image(label="Sample #1", value=load_img(image_prefix + str(image_ids[int(example)]) + "_synthetic_0.png"), interactive=False)
+        mask_1 = gr.Image(label="Mask", value=load_img(mask_prefix + str(image_ids[int(example)]) + "_mask.png"), interactive=False)
+        img_2 = gr.Image(label="Sample #2", value=load_img(image_prefix+ str(image_ids[int(example)]) + "_synthetic_1.png"), interactive=False)
+        mask_2 = gr.Image(label="Mask", value=load_img(mask_prefix + str(image_ids[int(example)]) + "_mask.png"), interactive=False)
+        img_3 = gr.Image(label="Sample #3", value=load_img(image_prefix + str(image_ids[int(example)]) + "_synthetic_2.png"), interactive=False)
+        mask_3 = gr.Image(label="Mask", value=load_img(mask_prefix + str(image_ids[int(example)]) + "_mask.png"), interactive=False)
+        img_4 = gr.Image(label="Sample #4", value=load_img(image_prefix + str(image_ids[int(example)]) + "_synthetic_3.png"), interactive=False)
+        mask_4 = gr.Image(label="Mask", value=load_img(mask_prefix + str(image_ids[int(example)]) + "_mask.png"), interactive=False)
+    return [rank, img_1, mask_1, img_2, mask_2, img_3, mask_3, img_4, mask_4, example]
+
+with gr.Blocks() as demo:
+    last_idx = -1
+    example = gr.Number(label="Example #. Click next for #-1 (blank starting page).", value=last_idx, interactive=False)
+    rank = gr.Textbox(label="Rankings (Best to worst, comma-separated, no spaces).")
+    with gr.Column(scale=1):
+        with gr.Row():
+            mask_1 = gr.Image(label="Mask", value=load_img("/deep/u/eprakash/blank.jpg"), interactive=False)
+            img_1 = gr.Image(label="Sample #1", value=load_img("/deep/u/eprakash/blank.jpg"), interactive=False)
+        with gr.Row():
+            mask_2 = gr.Image(label="Mask", value=load_img("/deep/u/eprakash/blank.jpg"), interactive=False)
+            img_2 = gr.Image(label="Sample #2", value=load_img("/deep/u/eprakash/blank.jpg"), interactive=False)
+        with gr.Row():
+            mask_3 = gr.Image(label="Mask", value=load_img("/deep/u/eprakash/blank.jpg"), interactive=False)
+            img_3 = gr.Image(label="Sample #3", value=load_img("/deep/u/eprakash/blank.jpg"), interactive=False)
+        with gr.Row():
+            mask_4 = gr.Image(label="Mask", value=load_img("/deep/u/eprakash/blank.jpg"), interactive=False)
+            img_4 = gr.Image(label="Sample #4", value=load_img("/deep/u/eprakash/blank.jpg"), interactive=False)
+    next_btn = gr.Button(value="Next")
+    next_btn.click(fn=load_next, inputs=[rank, img_1, mask_1, img_2, mask_2, img_3, mask_3, img_4, mask_4, example], outputs=[rank, img_1, mask_1, img_2, mask_2, img_3, mask_3, img_4, mask_4, example], queue=False)
+    demo.queue()
+    demo.launch(share=True)
+

lung_seg_gradio.py

@@ -0,0 +1,113 @@
+import gradio as gr
+import random
+import time
+import os
+from glob import glob
+from PIL import Image
+import torchvision.transforms as transforms
+
+num_rank = 200
+image_prefix = "/deep/u/eprakash/AngioSeg/diffusion/lung_seg_synthetic_60/synth/"
+mask_prefix = "/deep/u/eprakash/AngioSeg/diffusion/lung_seg_synthetic_60/orig/"
+image_ids = []
+img_list = "/deep/u/eprakash/lung_seg/train_60.csv"
+with open(img_list) as fp:
+    for line in fp:
+        image_ids.append("('" + line.strip().split(",")[0] + "',)")
+image_ids = image_ids[301:501]
+save_path = "lung_seg_ranks"
+
+def is_int(s):
+    try:
+        int(s)
+        return True
+    except ValueError:
+        return False
+
+def load_img(img_path, size=512):
+    img = Image.open(img_path).convert('RGB')
+    transform_list = [transforms.Resize((size, size))]
+    transform = transforms.Compose(transform_list)
+    img = transform(img)
+    return img
+
+def find_completed_idxs(save_path=save_path):
+    files = os.listdir(save_path)
+    incorrect_files = []
+    if len(files) == 0:
+        return [-1], []
+    else:
+        file_list = []
+        for f in files:
+            f_name = int(f.split(".")[0])
+            with open(save_path + "/" + f) as fp:
+                for line in fp:
+                    items = line.strip().split(",")
+                    if (len(items) != 5 and f_name != -1):
+                        incorrect_files.append(f_name)
+                    else:
+                        if ((not is_int(items[1].strip()) or not is_int(items[2].strip()) or not is_int(items[3].strip()) or not is_int(items[4].strip())) and f_name != -1):
+                            incorrect_files.append(f_name)
+            file_list.append(f_name)
+        file_list = sorted(file_list)
+        incorrect_files = sorted(incorrect_files)
+        return file_list, incorrect_files
+
+def load_next(rank, img_1, mask_1, img_2, mask_2, img_3, mask_3, img_4, mask_4, example, ids=image_ids, image_prefix=image_prefix, save_path=save_path):
+    file_list, incorrect_files = find_completed_idxs()
+    print(str(file_list) + " " + str(incorrect_files))
+    if (int(example) not in file_list or int(example) in incorrect_files):
+        r = str(image_ids[int(example)]).split(",")[0].split("(")[1] + "," + rank
+        r_fp = open(save_path + "/" + str(int(example)) +".txt", "w")
+        r_fp.write(r + "\n")
+        r_fp.close()
+    file_list, incorrect_files = find_completed_idxs()
+    if (len(incorrect_files) != 0):
+        example = incorrect_files[-1]
+    else:
+        example = file_list[-1] + 1
+    if int(example) == num_rank:
+        rank = "DONE!"
+        example = -1
+        mask_1 = gr.Image(label="Mask", value=load_img("/deep/u/eprakash/blank.jpg"), interactive=False)
+        img_1 = gr.Image(label="Sample #1", value=load_img("/deep/u/eprakash/blank.jpg"), interactive=False)
+        mask_2 = gr.Image(label="Mask", value=load_img("/deep/u/eprakash/blank.jpg"), interactive=False)
+        img_2 = gr.Image(label="Sample #2", value=load_img("/deep/u/eprakash/blank.jpg"), interactive=False)
+        mask_3 = gr.Image(label="Mask", value=load_img("/deep/u/eprakash/blank.jpg"), interactive=False)
+        img_3 = gr.Image(label="Sample #3", value=load_img("/deep/u/eprakash/blank.jpg"), interactive=False)
+        mask_4 = gr.Image(label="Mask", value=load_img("/deep/u/eprakash/blank.jpg"), interactive=False)
+        img_4 = gr.Image(label="Sample #4", value=load_img("/deep/u/eprakash/blank.jpg"), interactive=False)
+    else:
+        rank = ""
+        img_1 = gr.Image(label="Sample #1", value=load_img(image_prefix + str(image_ids[int(example)]) + "_synthetic_0.png"), interactive=False)
+        mask_1 = gr.Image(label="Mask", value=load_img(mask_prefix + str(image_ids[int(example)]) + "_mask.png"), interactive=False)
+        img_2 = gr.Image(label="Sample #2", value=load_img(image_prefix+ str(image_ids[int(example)]) + "_synthetic_1.png"), interactive=False)
+        mask_2 = gr.Image(label="Mask", value=load_img(mask_prefix + str(image_ids[int(example)]) + "_mask.png"), interactive=False)
+        img_3 = gr.Image(label="Sample #3", value=load_img(image_prefix + str(image_ids[int(example)]) + "_synthetic_2.png"), interactive=False)
+        mask_3 = gr.Image(label="Mask", value=load_img(mask_prefix + str(image_ids[int(example)]) + "_mask.png"), interactive=False)
+        img_4 = gr.Image(label="Sample #4", value=load_img(image_prefix + str(image_ids[int(example)]) + "_synthetic_3.png"), interactive=False)
+        mask_4 = gr.Image(label="Mask", value=load_img(mask_prefix + str(image_ids[int(example)]) + "_mask.png"), interactive=False)
+    return [rank, img_1, mask_1, img_2, mask_2, img_3, mask_3, img_4, mask_4, example]
+
+with gr.Blocks() as demo:
+    last_idx = -1
+    example = gr.Number(label="Example #. Click next for #-1 (blank starting page).", value=last_idx, interactive=False)
+    rank = gr.Textbox(label="Rankings (Best to worst, comma-separated, no spaces).")
+    with gr.Column(scale=1):
+        with gr.Row():
+            mask_1 = gr.Image(label="Mask", value=load_img("/deep/u/eprakash/blank.jpg"), interactive=False)
+            img_1 = gr.Image(label="Sample #1", value=load_img("/deep/u/eprakash/blank.jpg"), interactive=False)
+        with gr.Row():
+            mask_2 = gr.Image(label="Mask", value=load_img("/deep/u/eprakash/blank.jpg"), interactive=False)
+            img_2 = gr.Image(label="Sample #2", value=load_img("/deep/u/eprakash/blank.jpg"), interactive=False)
+        with gr.Row():
+            mask_3 = gr.Image(label="Mask", value=load_img("/deep/u/eprakash/blank.jpg"), interactive=False)
+            img_3 = gr.Image(label="Sample #3", value=load_img("/deep/u/eprakash/blank.jpg"), interactive=False)
+        with gr.Row():
+            mask_4 = gr.Image(label="Mask", value=load_img("/deep/u/eprakash/blank.jpg"), interactive=False)
+            img_4 = gr.Image(label="Sample #4", value=load_img("/deep/u/eprakash/blank.jpg"), interactive=False)
+    next_btn = gr.Button(value="Next")
+    next_btn.click(fn=load_next, inputs=[rank, img_1, mask_1, img_2, mask_2, img_3, mask_3, img_4, mask_4, example], outputs=[rank, img_1, mask_1, img_2, mask_2, img_3, mask_3, img_4, mask_4, example], queue=False)
+    demo.queue()
+    demo.launch(share=True)
+

lung_seg_ranks/0.txt

@@ -0,0 +1 @@
+'4.5.04.955547.25.3.0.2.364123573012382.7444502808851.4',

test_gradio.py

@@ -0,0 +1,43 @@
+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()

test_gradio_2.py

@@ -0,0 +1,43 @@
+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[750:]
+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_3/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()