app.py

MODEL_INFO = ["Model"]
AVGACC = "Overall Acc."
TASK_INFO = [AVGACC, "Dynamic Perception","State Transitions Perception","Comparison Reasoning","Reasoning with External Knowledge","Explanatory Reasoning","Predictive Reasoning","Description","Counterfactual Reasoning","Camera Movement Perception"]

DATA_TITILE_TYPE = ["markdown", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]
CSV_DIR = "./file/result.csv"

COLUMN_NAMES = MODEL_INFO + TASK_INFO
GT_PATH = "./file/AUTO-EVAL-VIDEO.json"
JSON_DATASET_PATH = "./file/userdata.json"
LEADERBORAD_INTRODUCTION = """# AutoEval-Video Leaderboard
    
    Welcome to the leaderboard of AutoEval-Video!
    AutoEval-Video comprises 327 complex open-ended video-question instances that span across nine skill dimensions, which address video-specific perception, comprehension, and generation skills. Please refer to our [paper]() for more details.
    """

SUBMIT_INTRODUCTION = """# Submit Introduction
    For example, if you want to upload GPT-4V's result in the leaderboard, you need to:
    1. Fill in 'GPT-4V' in 'Model Name' if it is your first time to submit your result. Alternatively, if you wish to modify the outcomes of your model, please add a version suffix after the model's name like 'GPT-4V_v2'.
    2. Upload results.json.
    3. Click the 'Evaluate' button.
    4. Click 'Refresh' to obtain the uploaded leaderboard.
    5. The evaluation results of your model will be given in the "Overall Acc." box. For results specific to each evaluation dimension, please refer back to the leaderboard.
"""

TABLE_INTRODUCTION = """The table below shows the performance of various models on different evaluation dimensions on AutoEval-Video.
        We use accuracy(%) as the primary evaluation metric for each dimension. 
    """

CITATION_BUTTON_LABEL = "If you find AutoEval-Video useful for your research and applications, please copy the following snippet to cite these results: "
CITATION_BUTTON_TEXT = """@article{chen2023autoevalvideo,
      title={AutoEval-Video: An Automatic Benchmark for Assessing Large Vision Language Models in Open-Ended Video Question Answering}, 
      author={Xiuyuan Chen and Yuan Lin and Yuchen Zhang and Weiran Huang},
      year={2023},
      journal={arXiv preprint arXiv:2311.14906}
}"""
style = """<style>
    .dataframe-container {
        overflow-x: auto;
    }
</style>"""
import gradio as gr
import pandas as pd
import json
from tqdm import tqdm
import time
import random
from huggingface_hub import CommitScheduler, login
import os
from openai import OpenAI
from tool import *

global data_component
login(token=os.environ.get("HF_TOKEN"), write_permission=True)


def get_result_df():
    df = pd.read_csv(CSV_DIR)[COLUMN_NAMES]
    df = df.sort_values(by=AVGACC, ascending=False)
    return df
def prediction_analyse(prediction_content,questiontype_list):
    predictions = prediction_content.split("\n")

    ground_truth_data = []
    with open("./file/AUTO-EVAL-VIDEO.json", "r") as f:
        for line in f :
            data = json.loads(line.strip())
            ground_truth_data.append(data)

    id2item = {str(item["ID"]): item for item in ground_truth_data}

    results = {i: {"correct": 0, "total": 0} for i in questiontype_list}

    for prediction in tqdm(predictions):
        # pdb.set_trace()
        prediction = prediction.strip()
        if not prediction:
            continue
        try:
            prediction = json.loads(prediction)
        except json.JSONDecodeError:
            print(f"Warning: Skipping invalid JSON data in line: {prediction}")
            continue
        question_id = str(prediction["ID"])
        print("Evaluating ID: " + question_id)
        item_gt = id2item[question_id]
        rule = item_gt['Rule']
        question_type = item_gt["Dimension"]

        pre_output = prediction["prediction"]
        if "judge" in list(prediction.keys()):
            judge_result_bit = prediction["judge"]
        else:
            _, judge_result_bit = alternate_judge(rule, pre_output, os.environ.get("yuan_api"))
        assert judge_result_bit in ["0", "1"], "Invalid judge result bit!"
        if judge_result_bit == "1":
            results[question_type]["correct"] += 1

        results[question_type]["total"] += 1
    return results



scheduler = CommitScheduler(
    repo_id="AUTOEVAL-Video-Backup",
    private=True,
    repo_type="dataset",
    folder_path="./file",
    path_in_repo="data",
    every=5,
)

def save_json(modelname, user_dict_list):
    with open(JSON_DATASET_PATH, "a") as f:
        json.dump({modelname:user_dict_list}, f)
        f.write('\n')

def add_new_eval(
    input_file,
    model_name_textbox: str,
):
    if len(model_name_textbox) == 0:
        return "Error! Empty model name!", get_result_df()
        
    if input_file is None:
        return "Error! Empty file!", get_result_df()
    else:
        csv_data = pd.read_csv(CSV_DIR, dtype={'Model': str})
        model_name_list = list(csv_data['Model'])
        if model_name_textbox in model_name_list:
            return "In the leaderboard, there already exists a model with the same name, and duplicate submissions of it are not allowed.", get_result_df()
        
        questiontype = COLUMN_NAMES[-9:]
        id2questiontype = dict(zip(range(1, 10),questiontype))
        content = input_file.decode("utf-8").strip()
        userdata = content.split('\n')
        if len(userdata) != count_lines(GT_PATH):
            return f"Error! The number of lines in the submit file ({len(userdata)}) does not match the number of lines in the AUTO-EVAL-VIDEO.json file ({count_lines(GT_PATH)}).", get_result_df()
        
        prediction = prediction_analyse(content,questiontype)

        each_task_accuracy = {i: round(prediction[i]["correct"] / max(1, prediction[i]["total"]) * 100, 1) for i in questiontype}

        total_correct_video = sum(prediction[i]["correct"] for i in questiontype)

        total_video = sum(prediction[i]["total"] for i in questiontype)

        
        average_accuracy_video = round(total_correct_video / max(1, total_video) * 100, 1)
        

        col = csv_data.shape[0]
        new_data = [
            model_name_textbox, 
            average_accuracy_video,
            each_task_accuracy[id2questiontype[1]],
            each_task_accuracy[id2questiontype[2]],
            each_task_accuracy[id2questiontype[3]],
            each_task_accuracy[id2questiontype[4]],
            each_task_accuracy[id2questiontype[5]],
            each_task_accuracy[id2questiontype[6]],
            each_task_accuracy[id2questiontype[7]],
            each_task_accuracy[id2questiontype[8]],
            each_task_accuracy[id2questiontype[9]],
            ]
        csv_data.loc[col] = new_data
        with scheduler.lock:
            csv_data = csv_data.to_csv(CSV_DIR, index=False)
            save_json(model_name_textbox, userdata)
    return str(average_accuracy_video) + "%", get_result_df()


block = gr.Blocks()


with block:
    gr.Markdown(
        LEADERBORAD_INTRODUCTION
    )
    with gr.Tabs(elem_classes="tab-buttons") as tabs:
        with gr.TabItem(" 🏆  AutoEval-Video Benchmark", elem_id="AutoEval-Video-tab-table", id=0):
            with gr.Row():
                
                with gr.Accordion("Citation", open=False):
                    # citation_button = gr.interface.inputs.Textbox(
                    #                     value=CITATION_BUTTON_TEXT,
                    #                     label=CITATION_BUTTON_LABEL,
                    #                     interactive=False,
                    #                     show_copy_button=True,
                    #                     elem_id="citation-button",
                    #                 )
                    citation_button = gr.Textbox(
                        value=CITATION_BUTTON_TEXT,
                        label=CITATION_BUTTON_LABEL,
                        interactive=False,
                        elem_id="citation-button",
                        show_copy_button=True
                    )
                    # citation_button = gr.Textbox(
                    #     value=CITATION_BUTTON_TEXT,
                    #     label=CITATION_BUTTON_LABEL,
                    #     interactive=False,
                    #     elem_id="citation-button",
                    # ).style(show_copy_button=True)
    
            gr.Markdown(
                TABLE_INTRODUCTION
            )

            data_component = gr.components.Dataframe(
                value=get_result_df, 
                headers=COLUMN_NAMES,
                type="pandas", 
                datatype=DATA_TITILE_TYPE,
                interactive=False,
                visible=True,
                # css=style,
                )
            with gr.Row():
                data_run = gr.Button("Refresh")
                data_run.click(
                    get_result_df, outputs=data_component
                )
        
        with gr.TabItem("✨ Submit your model result here!", elem_id="AutoEval-Video-tab-table",id=1):
            with gr.Row():
                gr.Markdown(SUBMIT_INTRODUCTION, elem_classes="markdown-text")


            with gr.Column():
                model_name_textbox = gr.Textbox(
                    label="Model name"
                    )

            with gr.Column():

                input_file = gr.inputs.File(label = "Click to Upload a json File", file_count="single", type='binary')
                submit_button = gr.Button("Evaluate")
                overall_acc = gr.Textbox(label="Overall Acc.")
    
                submit_button.click(
                    add_new_eval,
                    inputs = [
                        input_file,
                        model_name_textbox,
                    ],
                    outputs = [overall_acc, data_component],
                )
block.launch()