start

app.py

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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)
+    video_path = "https://huggingface.co/datasets/nielsr/video-demo/resolve/main/eating_spaghetti.mp4"
+    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()

requirements.txt

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+gradio
+numpy