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import json | |
import os | |
import os.path as osp | |
import gradio as gr | |
import numpy as np | |
import gradio as gr | |
def load_json(load_dir_path, json_file_name): | |
load_path = os.path.join(load_dir_path, json_file_name) | |
if not os.path.exists(load_path): | |
return None | |
with open(load_path, 'r', encoding='utf-8') as f: | |
obj_serializable = json.load(f) | |
return obj_serializable | |
def load_results_recaption(save_path, model="gpt-3.5-turbo-0125"): | |
result_list = load_json(save_path, f'final_results-{model}.json') | |
if result_list is not None: | |
result_list = result_list['result_list'] | |
if result_list is None: | |
result_list = load_json(save_path, 'inference_results.json') | |
return result_list | |
plava_theme = gr.themes.Monochrome( | |
text_size="sm", | |
spacing_size="sm", | |
primary_hue=gr.themes.Color(c100="#f5f5f5", c200="#e5e5e5", c300="#d4d4d4", c400="#a3a3a3", c50="#fafafa", c500="#737373", c600="#525252", c700="#404040", c800="#262626", c900="#171717", c950="#000000"), | |
secondary_hue=gr.themes.Color(c100="#f5f5f5", c200="#e5e5e5", c300="#d4d4d4", c400="#a3a3a3", c50="#fafafa", c500="#737373", c600="#525252", c700="#404040", c800="#262626", c900="#171717", c950="#000000"), | |
neutral_hue=gr.themes.Color(c100="#f5f5f5", c200="#e5e5e5", c300="#d4d4d4", c400="#a3a3a3", c50="#fafafa", c500="#737373", c600="#525252", c700="#404040", c800="#262626", c900="#171717", c950="#000000"), | |
).set( | |
background_fill_primary_dark='*primary_950', | |
background_fill_secondary_dark='*neutral_950' | |
) | |
load_results_funcs = [ | |
load_results_recaption, | |
] | |
recaption_root_dir = "recaption_results" | |
local_video_root_dir = "DATAS/Recaption/Inter4K/60fps/UHD" | |
remote_video_root_dir ="https://huggingface.co/datasets/ermu2001/Inter4kPlavaRecaption/resolve/main/DATAS/Recaption/Inter4K/60fps/UHD" | |
def show(result_list_first, result_list_second, result_index): | |
sample2index_second = {} | |
for i, result in enumerate(result_list_second): | |
if 'video_path' not in result: | |
continue | |
question = result['question'] if 'question' in result else '' | |
video_path = result['video_path'] | |
samplehash = question + '--' +video_path | |
sample2index_second[samplehash] = i | |
info = result_list_first[result_index] | |
info_str_first = json.dumps(info, indent=4, ensure_ascii=False) | |
video_path = info['video_path'] | |
question = info['question'] if 'question' in info else '' | |
samplehash = question + '--' +video_path | |
if samplehash in sample2index_second: | |
info = result_list_second[sample2index_second[samplehash]] | |
info_str_second = json.dumps(info, indent=4, ensure_ascii=False) | |
else: | |
info_str_second = f"NO {video_path} IN THE SECOND RESULT DIR" | |
video_path = video_path.replace(local_video_root_dir, remote_video_root_dir) | |
return video_path, info_str_first, info_str_second | |
def reload_results_dirs(): | |
result_dirs = [] | |
# load result dir paths | |
for dirpath, dirnames, filenames in os.walk(recaption_root_dir): | |
if len(dirnames) == 0 and len(filenames) != 0: | |
result_dirs.append(dirpath) | |
return gr.Dropdown(result_dirs, value=result_dirs[0]) | |
def reload_results(result_dir): | |
# if isinstance(result_dir, list): | |
# result_dir = result_dir[0] | |
if result_dir is None or not osp.exists(result_dir): | |
return None | |
for fn in load_results_funcs: | |
result_list = fn(result_dir) | |
if result_list is not None: | |
np.random.shuffle(result_list) | |
break | |
result_index = gr.Slider(0, len(result_list), step=1) | |
return result_list, result_index | |
with gr.Blocks(title="PLAVA RESULTS", theme=plava_theme) as demo: | |
result_list_first = gr.State() | |
result_list_second = gr.State() | |
with gr.Row(): | |
with gr.Column(): | |
gr.Markdown("# Showing off Model's Outputs.") | |
gr.Markdown( | |
"You can find all our results, including:\n" | |
"1. results of Captioned Inter4k\n" | |
"2. results of Different Benchmark inference outputs.\n" | |
"Choose a directory to see the different output variant.\n" | |
"You can also choose secondary directory (as long as they are from the same dataset.) to compare on the results.\n" | |
) | |
with gr.Row(): | |
with gr.Column(): | |
show_video = gr.Video(interactive=False) | |
with gr.Column(): | |
button_reload = gr.Button(value='Reload From The Evaluation/Inference Root Directory') | |
result_index = gr.Slider(0, 0, step=1, label="Index") | |
result_dir_first = gr.Dropdown(label='Test Result Path') | |
info_first = gr.Text(interactive=False, label='Detailed Output Information') | |
result_dir_second = gr.Dropdown(label='Test Result Path') | |
info_second = gr.Text(interactive=False, label='Detailed Output Information') | |
button_reload.click(reload_results_dirs, [], [result_dir_first]) | |
button_reload.click(reload_results_dirs, [], [result_dir_second]) | |
result_dir_first.change(reload_results, [result_dir_first], [result_list_first, result_index]) | |
result_dir_second.change(reload_results, [result_dir_second], [result_list_second, result_index]) | |
result_index.change(show, [result_list_first, result_list_second, result_index], [show_video, info_first, info_second]) | |
demo.load(reload_results_dirs, [], [result_dir_first]) | |
demo.load(reload_results_dirs, [], [result_dir_second]) | |
demo.launch() |