first check commit

README.md

@@ -1,6 +1,6 @@
 ---
-title: AutoGradeCheckSystem
-emoji: 😻
+title: GradeSystem
+emoji: 🐢
 colorFrom: indigo
 colorTo: indigo
 sdk: gradio

app.py

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+import os, sys
+import numpy as np
+import pandas as pd
+import gradio as gr
+
+from libs import *
+
+import logging
+
+logging.basicConfig(level=logging.DEBUG)
+
+
+with gr.Blocks(title="수업 성적 확인") as demo:
+    gr.Markdown("수업 성적 확인을 위해, 수업과 이름 학번을 입력하세요.")
+
+    with gr.Row():
+        with gr.Column():
+            class_select = gr.Dropdown(choices=CLASS_LST, multiselect=False, label="수업 선택")
+            name_input = gr.Textbox(label="이름 입력")
+            number_input = gr.Textbox(label="학번 입력")
+            input_buttton = gr.Button("확인")
+
+        with gr.Column():
+            image_output = gr.Image(label="성적 분포 그림")
+            data_output = gr.Dataframe(label="성적", interactive=True)
+    
+    input_buttton.click(check_grade,inputs=[name_input, number_input, class_select], outputs = [image_output, data_output])
+    
+
+demo.launch(debug=True)

data/datalist.txt

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+지능형시스템(가).csv
+지능형시스템(나).csv

libs/__init__.py

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+
+from .utils import *

libs/utils.py

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+import os
+import numpy as np
+import pandas as pd
+import gradio as gr
+import matplotlib as mpl
+import matplotlib.pyplot as plt
+from collections import Counter 
+from urllib.parse import quote
+from huggingface_hub import  hf_hub_download
+from datasets import load_dataset
+from datasets import Dataset
+from huggingface_hub import HfApi, HfFolder
+import logging
+
+logging.basicConfig(level=logging.DEBUG)
+
+
+space_name = 'Grade_Dataset'
+access_token = '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'
+
+DATA_PATH = "https://huggingface.co/datasets/IgnoreLee/Grade_Dataset/tree/main"
+IMG_PATH  = os.path.join(DATA_PATH, "img")
+CLASS_LST = ["지능형시스템(가)", "지능형시스템(나)"]
+
+def _get_csv_file(name,repo_id = "IgnoreLee/Grade_Dataset"):
+    return hf_hub_download(repo_id=repo_id, 
+    filename=f"{name}.csv", 
+    repo_type="dataset", 
+    token="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")
+
+def encoder(strings):
+    original = ["(", ")", "_", "-", ".", ",", "~", "/", ";", "\\", " "] + list(range(0, 9))
+    en_str = ""
+    for str in strings:
+        if str in original:
+            en_str += str
+        else:
+            en_str += quote(str)
+    return en_str
+
+def draw_figure(count_dict:dict, mean, median) -> np.array:
+    # font_path = os.path.join(".","font","NanumGothic.ttf")
+    # font = mpl.font_manager.FontProperties(fname=font_path).get_name()
+    # mpl.rc('font', family=font)
+
+    fig = plt.figure()
+    plt.title("Overall score distribution")
+    plt.bar(count_dict.keys(), count_dict.values(), width=0.4, label = f"Total number of people {np.sum(list(count_dict.values()))}")
+    plt.axvline(mean.sum(), label=f"Mean : ({mean.sum():.2f})", color="r", linestyle=":")
+    plt.legend()
+    plt.xlabel = "Grade"
+    plt.ylabel = "Number of Person"
+    fig.canvas.draw()
+    return np.array(fig.canvas.renderer._renderer)
+
+def csv_analysis(data:pd.DataFrame):
+    datas = data.values
+    mean, std, = datas.mean(0), datas.std(0)
+    ranking = np.sort(np.unique(datas.sum(1)))[::-1]
+    count_dict = Counter(datas.sum(1))
+    return (mean, std, ranking), count_dict
+
+def check_grade(name, number, course:str):
+    assert course in CLASS_LST, gr.Error("수업 이름을 확인해주세요.")
+
+    # Preprocessing
+    number = int(number)
+    # hf_folder = HfApi(space_name, f'{course}.csv')
+    # dataset = Dataset.from_config(hf_folder, data_files={"access_token": access_token})
+    dataset = load_dataset(f"IgnoreLee/Grade_Dataset", data_files = f"{course}.csv", use_auth_token=access_token)
+    logging.debug("Debug message: {}".format(dataset))
+    data = dataset['train'].to_pandas()
+    logging.debug("Debug message: {}".format(data))
+    data.set_index(list(data.columns[:2]), inplace=True)
+    logging.debug("Debug message: {}".format(data))
+    data.fillna(0, inplace=True)
+    cols = list(data.columns)
+
+    # Data Analysis
+    (mean, std, ranking), count_dict = csv_analysis(data)
+    logging.debug("Debug message: {}".format(count_dict))
+    finded_student = data.loc[number, name, :]
+    finded_student.loc[:,"합계"] =  finded_student.loc[:,cols].values.sum(1)
+    median = np.median(finded_student[cols].values.sum(1))
+
+    # Draw Figure
+    img = draw_figure(count_dict, mean, median)
+    
+    return img, finded_student