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app.py

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+import os
+import tempfile
+import shutil
+
+import gradio as gr
+import matplotlib.cm as cm
+import pandas as pd
+import japanize_matplotlib
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+def all_likelihood_plot(csv_file_name, tmpdir):
+   df = pd.read_csv(csv_file_name, header=[1, 2])
+   df = df.drop(df.columns[[0]], axis=1)
+   columns = df.columns.droplevel(1)
+
+   # 重複を削除
+   columns = columns.drop_duplicates()
+   likelihood = [df[x]["likelihood"] for x in columns]
+   a = pd.DataFrame(likelihood, index=columns).T
+
+   #平均値を求める
+   point_average = a.mean()
+   parts = ["指節1", "指節2", "指節3", "指節4", "指節5", "指節6", "指節7", "指節8", "指節9",
+            "指節10", "指節11", "指節12", "指節13", "指節14", "触角(右)", "触角(左)", "頭部", "腹尾節"]
+
+   # カラーマップの設定
+   a.columns = parts
+   cmap = plt.get_cmap('rainbow')
+   # バイオリン図のプロット
+   sns.set(style="whitegrid",font="IPAexGothic")
+   fig, ax = plt.subplots()
+
+   # データをバイオリンプロットで描画
+   sns.violinplot(data=a, palette=[cmap(i)
+                  for i in np.linspace(0, 1, len(columns))], ax=ax,inner=None)
+
+   # 横軸のラベルを重ならないように
+   plt.xticks(rotation=65)
+
+   ax.set_title('付属肢別の尤度')
+   ax.set_xlabel('付属肢')
+   ax.set_ylabel('尤度')
+
+   #それぞれの要素の平均値をプロット
+   plt.scatter(x=parts, y=point_average, color='black', marker='x')
+
+   # 最大値を1に
+   plt.ylim(0, 1)
+
+   #ラベルがはみ出ないように
+   plt.tight_layout()
+
+   # グラフを表示
+   plt.savefig(f"likelihood.png", dpi=300)
+
+
+def main(csv_file):
+   with tempfile.TemporaryDirectory(dir=".") as tmpdir:
+      all_likelihood_plot(csv_file, tmpdir)
+      return f"likelihood.png"
+
+
+iface = gr.Interface(fn=main, inputs="file", outputs="image", title="尤度のグラフを作成します。")
+iface.launch()

requirements.txt

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+pandas
+matplotlib
+numpy
+japanize_matplotlib
+seaborn