Update app.py

app.py

@@ -1,278 +1,278 @@
-   import gradio as gr
-   import polars as pl
-   import os
-   import shutil
-   import numpy as np
-   from PyQt6.QtWidgets import QApplication
-   from PyQt6 import QtCore
-   import pyqtgraph as pg
-   from pyqtgraph.exporters import ImageExporter
+import gradio as gr
+import polars as pl
+import os
+import shutil
+import numpy as np
+from PyQt6.QtWidgets import QApplication
+from PyQt6 import QtCore
+import pyqtgraph as pg
+from pyqtgraph.exporters import ImageExporter
 
-   class DataProcessor:
-      def __init__(self, bodypart_names, x_max, y_max):
-         # 余分な空白を除去してリスト化
-         self.bodypart_names = [name.strip() for name in bodypart_names.split(',')]
-         self.x_max = x_max
-         self.y_max = y_max
-         self.output_folder = 'output_plots'
-         if not os.path.exists(self.output_folder):
-               os.makedirs(self.output_folder)
+class DataProcessor:
+    def __init__(self, bodypart_names, x_max, y_max):
+        # 余分な空白を除去してリスト化
+        self.bodypart_names = [name.strip() for name in bodypart_names.split(',')]
+        self.x_max = x_max
+        self.y_max = y_max
+        self.output_folder = 'output_plots'
+        if not os.path.exists(self.output_folder):
+            os.makedirs(self.output_folder)
 
-      def process_csv(self, file_path):
-         # CSVをpolarsで読み込み（ヘッダーはなし）
-         df_raw = pl.read_csv(file_path, has_header=False)
-         # 2行分のヘッダー（インデックス1,2）を取得し、最初の列は除外する
-         header1 = df_raw.row(1)[1:]
-         header2 = df_raw.row(2)[1:]
-         new_columns = [f"{h1}|{h2}" for h1, h2 in zip(header1, header2)]
-         # データ部分はインデックス3以降（0-indexed）とし、先頭列を削除
-         df_data = df_raw.slice(3)
-         first_col = df_data.columns[0]
-         df_data = df_data.drop(first_col)
-         df_data.columns = new_columns
+    def process_csv(self, file_path):
+        # CSVをpolarsで読み込み（ヘッダーはなし）
+        df_raw = pl.read_csv(file_path, has_header=False)
+        # 2行分のヘッダー（インデックス1,2）を取得し、最初の列は除外する
+        header1 = df_raw.row(1)[1:]
+        header2 = df_raw.row(2)[1:]
+        new_columns = [f"{h1}|{h2}" for h1, h2 in zip(header1, header2)]
+        # データ部分はインデックス3以降（0-indexed）とし、先頭列を削除
+        df_data = df_raw.slice(3)
+        first_col = df_data.columns[0]
+        df_data = df_data.drop(first_col)
+        df_data.columns = new_columns
 
-         # likelihood列のみ抽出
-         df_likelihood = self.extract_likelihood(df_data)
-         # likelihood列を除去したデータ
-         df_no_likelihood = self.remove_likelihood(df_data)
-         # 付属肢名の置換（左側の名前を mapping で変更）
-         df_renamed = self.rename_bodyparts(df_no_likelihood)
-         return df_renamed, df_likelihood
+        # likelihood列のみ抽出
+        df_likelihood = self.extract_likelihood(df_data)
+        # likelihood列を除去したデータ
+        df_no_likelihood = self.remove_likelihood(df_data)
+        # 付属肢名の置換（左側の名前を mapping で変更）
+        df_renamed = self.rename_bodyparts(df_no_likelihood)
+        return df_renamed, df_likelihood
 
-      def remove_likelihood(self, df):
-         # 列名が "bodypart|likelihood" となっている列を除外
-         new_cols = [col for col in df.columns if col.split("|")[1] != "likelihood"]
-         return df.select(new_cols)
+    def remove_likelihood(self, df):
+        # 列名が "bodypart|likelihood" となっている列を除外
+        new_cols = [col for col in df.columns if col.split("|")[1] != "likelihood"]
+        return df.select(new_cols)
 
-      def rename_bodyparts(self, df):
-         cols = df.columns
-         current_names = []
-         for col in cols:
-               bp = col.split("|")[0]
-               if bp not in current_names:
-                  current_names.append(bp)
-         if len(self.bodypart_names) != len(current_names):
-               raise ValueError("The length of bodypart_names must be equal to the number of bodyparts.")
-         mapping = dict(zip(current_names, self.bodypart_names))
-         new_cols = {col: f"{mapping[col.split('|')[0]]}|{col.split('|')[1]}" for col in cols}
-         return df.rename(new_cols)
+    def rename_bodyparts(self, df):
+        cols = df.columns
+        current_names = []
+        for col in cols:
+            bp = col.split("|")[0]
+            if bp not in current_names:
+                current_names.append(bp)
+        if len(self.bodypart_names) != len(current_names):
+            raise ValueError("The length of bodypart_names must be equal to the number of bodyparts.")
+        mapping = dict(zip(current_names, self.bodypart_names))
+        new_cols = {col: f"{mapping[col.split('|')[0]]}|{col.split('|')[1]}" for col in cols}
+        return df.rename(new_cols)
 
-      def extract_likelihood(self, df):
-         # likelihood列のみを抽出
-         likelihood_cols = [col for col in df.columns if col.split("|")[1] == "likelihood"]
-         df_likelihood = df.select(likelihood_cols)
-         current_names = []
-         for col in likelihood_cols:
-               bp = col.split("|")[0]
-               if bp not in current_names:
-                  current_names.append(bp)
-         if len(self.bodypart_names) != len(current_names):
-               raise ValueError("The length of bodypart_names must be equal to the number of bodyparts.")
-         mapping = dict(zip(current_names, self.bodypart_names))
-         new_cols = {col: f"{mapping[col.split('|')[0]]}|{col.split('|')[1]}" for col in likelihood_cols}
-         return df_likelihood.rename(new_cols)
+    def extract_likelihood(self, df):
+        # likelihood列のみを抽出
+        likelihood_cols = [col for col in df.columns if col.split("|")[1] == "likelihood"]
+        df_likelihood = df.select(likelihood_cols)
+        current_names = []
+        for col in likelihood_cols:
+            bp = col.split("|")[0]
+            if bp not in current_names:
+                current_names.append(bp)
+        if len(self.bodypart_names) != len(current_names):
+            raise ValueError("The length of bodypart_names must be equal to the number of bodyparts.")
+        mapping = dict(zip(current_names, self.bodypart_names))
+        new_cols = {col: f"{mapping[col.split('|')[0]]}|{col.split('|')[1]}" for col in likelihood_cols}
+        return df_likelihood.rename(new_cols)
 
-      def get_bodyparts(self, df):
-         bodyparts = []
-         for col in df.columns:
-               bp = col.split("|")[0]
-               if bp not in bodyparts:
-                  bodyparts.append(bp)
-         return bodyparts
+    def get_bodyparts(self, df):
+        bodyparts = []
+        for col in df.columns:
+            bp = col.split("|")[0]
+            if bp not in bodyparts:
+                bodyparts.append(bp)
+        return bodyparts
 
-      def plot_scatter(self, df):
-         image_paths = []
-         bodyparts = self.get_bodyparts(df)
-         app = QApplication.instance()
-         if app is None:
-               app = QApplication([])
+    def plot_scatter(self, df):
+        image_paths = []
+        bodyparts = self.get_bodyparts(df)
+        app = QApplication.instance()
+        if app is None:
+            app = QApplication([])
 
-         # 個別の散布図を作成
-         for i, bodypart in enumerate(bodyparts):
-               try:
-                  x = np.array(df[f"{bodypart}|x"].to_list(), dtype=float)
-                  y = np.array(df[f"{bodypart}|y"].to_list(), dtype=float)
-               except Exception as e:
-                  continue
-               pw = pg.PlotWidget(title=f'トラッキングの座標({bodypart})')
-               pw.setLabel('bottom', 'X Coordinate(pixel)')
-               pw.setLabel('left', 'Y Coordinate(pixel)')
-               pw.setXRange(0, self.x_max)
-               pw.setYRange(0, self.y_max)
-               pw.invertY(True)
-               color = pg.intColor(i, len(bodyparts))
-               # 散布図アイテムの追加
-               scatter = pg.ScatterPlotItem(x=x, y=y, pen=pg.mkPen(color=color), symbol='o', brush=color)
-               pw.addItem(scatter)
-               # 始点を黒丸でハイライトし、"Start"テキストを追加
-               if len(x) > 0:
-                  scatter_start = pg.ScatterPlotItem(x=[x[0]], y=[y[0]], pen=pg.mkPen(color='k'), symbol='o', size=10, brush='k')
-                  pw.addItem(scatter_start)
-                  text = pg.TextItem("Start", color='k')
-                  text.setPos(x[0], y[0])
-                  pw.addItem(text)
-               # PNGにエクスポート
-               exporter = ImageExporter(pw.plotItem)
-               filename = os.path.join(self.output_folder, f"{bodypart}.png")
-               exporter.export(filename)
-               image_paths.append(filename)
+        # 個別の散布図を作成
+        for i, bodypart in enumerate(bodyparts):
+            try:
+                x = np.array(df[f"{bodypart}|x"].to_list(), dtype=float)
+                y = np.array(df[f"{bodypart}|y"].to_list(), dtype=float)
+            except Exception as e:
+                continue
+            pw = pg.PlotWidget(title=f'トラッキングの座標({bodypart})')
+            pw.setLabel('bottom', 'X Coordinate(pixel)')
+            pw.setLabel('left', 'Y Coordinate(pixel)')
+            pw.setXRange(0, self.x_max)
+            pw.setYRange(0, self.y_max)
+            pw.invertY(True)
+            color = pg.intColor(i, len(bodyparts))
+            # 散布図アイテムの追加
+            scatter = pg.ScatterPlotItem(x=x, y=y, pen=pg.mkPen(color=color), symbol='o', brush=color)
+            pw.addItem(scatter)
+            # 始点を黒丸でハイライトし、"Start"テキストを追加
+            if len(x) > 0:
+                scatter_start = pg.ScatterPlotItem(x=[x[0]], y=[y[0]], pen=pg.mkPen(color='k'), symbol='o', size=10, brush='k')
+                pw.addItem(scatter_start)
+                text = pg.TextItem("Start", color='k')
+                text.setPos(x[0], y[0])
+                pw.addItem(text)
+            # PNGにエクスポート
+            exporter = ImageExporter(pw.plotItem)
+            filename = os.path.join(self.output_folder, f"{bodypart}.png")
+            exporter.export(filename)
+            image_paths.append(filename)
 
-         # 全付属肢の散布図を作成
-         pw_all = pg.PlotWidget(title='トラッキングの座標(全付属肢)')
-         pw_all.setLabel('bottom', 'X Coordinate(pixel)')
-         pw_all.setLabel('left', 'Y Coordinate(pixel)')
-         pw_all.setXRange(0, self.x_max)
-         pw_all.setYRange(0, self.y_max)
-         pw_all.invertY(True)
-         for i, bodypart in enumerate(bodyparts):
-               try:
-                  x = np.array(df[f"{bodypart}|x"].to_list(), dtype=float)
-                  y = np.array(df[f"{bodypart}|y"].to_list(), dtype=float)
-               except Exception as e:
-                  continue
-               color = pg.intColor(i, len(bodyparts))
-               scatter = pg.ScatterPlotItem(x=x, y=y, pen=pg.mkPen(color=color), symbol='o', brush=color)
-               pw_all.addItem(scatter)
-         exporter_all = ImageExporter(pw_all.plotItem)
-         filename_all = os.path.join(self.output_folder, "all_plot.png")
-         exporter_all.export(filename_all)
-         image_paths.append(filename_all)
-         return image_paths
+        # 全付属肢の散布図を作成
+        pw_all = pg.PlotWidget(title='トラッキングの座標(全付属肢)')
+        pw_all.setLabel('bottom', 'X Coordinate(pixel)')
+        pw_all.setLabel('left', 'Y Coordinate(pixel)')
+        pw_all.setXRange(0, self.x_max)
+        pw_all.setYRange(0, self.y_max)
+        pw_all.invertY(True)
+        for i, bodypart in enumerate(bodyparts):
+            try:
+                x = np.array(df[f"{bodypart}|x"].to_list(), dtype=float)
+                y = np.array(df[f"{bodypart}|y"].to_list(), dtype=float)
+            except Exception as e:
+                continue
+            color = pg.intColor(i, len(bodyparts))
+            scatter = pg.ScatterPlotItem(x=x, y=y, pen=pg.mkPen(color=color), symbol='o', brush=color)
+            pw_all.addItem(scatter)
+        exporter_all = ImageExporter(pw_all.plotItem)
+        filename_all = os.path.join(self.output_folder, "all_plot.png")
+        exporter_all.export(filename_all)
+        image_paths.append(filename_all)
+        return image_paths
 
-      def plot_trajectories(self, df):
-         image_paths = []
-         bodyparts = self.get_bodyparts(df)
-         app = QApplication.instance()
-         if app is None:
-               app = QApplication([])
+    def plot_trajectories(self, df):
+        image_paths = []
+        bodyparts = self.get_bodyparts(df)
+        app = QApplication.instance()
+        if app is None:
+            app = QApplication([])
 
-         # 個別の軌跡図を作成
-         for i, bodypart in enumerate(bodyparts):
-               try:
-                  x = np.array(df[f"{bodypart}|x"].to_list(), dtype=float)
-                  y = np.array(df[f"{bodypart}|y"].to_list(), dtype=float)
-               except Exception as e:
-                  continue
-               pw = pg.PlotWidget(title=f'トラッキングの座標({bodypart})')
-               pw.setLabel('bottom', 'Frames')
-               pw.setLabel('left', 'Coordinate(pixel)')
-               pen_x = pg.mkPen(color=pg.intColor(i, len(bodyparts)), style=QtCore.Qt.PenStyle.DashLine)
-               pen_y = pg.mkPen(color=pg.intColor(i, len(bodyparts)))
-               pw.plot(x, pen=pen_x, name=f"{bodypart}(x座標)")
-               pw.plot(y, pen=pen_y, name=f"{bodypart}(y座標)")
-               exporter = ImageExporter(pw.plotItem)
-               filename = os.path.join(self.output_folder, f"{bodypart}_trajectories.png")
-               exporter.export(filename)
-               image_paths.append(filename)
+        # 個別の軌跡図を作成
+        for i, bodypart in enumerate(bodyparts):
+            try:
+                x = np.array(df[f"{bodypart}|x"].to_list(), dtype=float)
+                y = np.array(df[f"{bodypart}|y"].to_list(), dtype=float)
+            except Exception as e:
+                continue
+            pw = pg.PlotWidget(title=f'トラッキングの座標({bodypart})')
+            pw.setLabel('bottom', 'Frames')
+            pw.setLabel('left', 'Coordinate(pixel)')
+            pen_x = pg.mkPen(color=pg.intColor(i, len(bodyparts)), style=QtCore.Qt.PenStyle.DashLine)
+            pen_y = pg.mkPen(color=pg.intColor(i, len(bodyparts)))
+            pw.plot(x, pen=pen_x, name=f"{bodypart}(x座標)")
+            pw.plot(y, pen=pen_y, name=f"{bodypart}(y座標)")
+            exporter = ImageExporter(pw.plotItem)
+            filename = os.path.join(self.output_folder, f"{bodypart}_trajectories.png")
+            exporter.export(filename)
+            image_paths.append(filename)
 
-         # 全付属肢の軌跡図を作成
-         pw_all = pg.PlotWidget(title='トラッキングの座標(全付属肢)')
-         pw_all.setLabel('bottom', 'Frames')
-         pw_all.setLabel('left', 'Coordinate(pixel)')
-         for i, bodypart in enumerate(bodyparts):
-               try:
-                  x = np.array(df[f"{bodypart}|x"].to_list(), dtype=float)
-                  y = np.array(df[f"{bodypart}|y"].to_list(), dtype=float)
-               except Exception as e:
-                  continue
-               pen_x = pg.mkPen(color=pg.intColor(i, len(bodyparts)), style=QtCore.Qt.PenStyle.DashLine)
-               pen_y = pg.mkPen(color=pg.intColor(i, len(bodyparts)))
-               pw_all.plot(x, pen=pen_x, name=f"{bodypart}(x座標)")
-               pw_all.plot(y, pen=pen_y, name=f"{bodypart}(y座標)")
-         exporter_all = ImageExporter(pw_all.plotItem)
-         filename_all = os.path.join(self.output_folder, "all_trajectories.png")
-         exporter_all.export(filename_all)
-         image_paths.append(filename_all)
-         return image_paths
+        # 全付属肢の軌跡図を作成
+        pw_all = pg.PlotWidget(title='トラッキングの座標(全付属肢)')
+        pw_all.setLabel('bottom', 'Frames')
+        pw_all.setLabel('left', 'Coordinate(pixel)')
+        for i, bodypart in enumerate(bodyparts):
+            try:
+                x = np.array(df[f"{bodypart}|x"].to_list(), dtype=float)
+                y = np.array(df[f"{bodypart}|y"].to_list(), dtype=float)
+            except Exception as e:
+                continue
+            pen_x = pg.mkPen(color=pg.intColor(i, len(bodyparts)), style=QtCore.Qt.PenStyle.DashLine)
+            pen_y = pg.mkPen(color=pg.intColor(i, len(bodyparts)))
+            pw_all.plot(x, pen=pen_x, name=f"{bodypart}(x座標)")
+            pw_all.plot(y, pen=pen_y, name=f"{bodypart}(y座標)")
+        exporter_all = ImageExporter(pw_all.plotItem)
+        filename_all = os.path.join(self.output_folder, "all_trajectories.png")
+        exporter_all.export(filename_all)
+        image_paths.append(filename_all)
+        return image_paths
 
-      def plot_likelihood(self, df_likelihood):
-         image_paths = []
-         bodyparts = self.get_bodyparts(df_likelihood)
-         app = QApplication.instance()
-         if app is None:
-               app = QApplication([])
+    def plot_likelihood(self, df_likelihood):
+        image_paths = []
+        bodyparts = self.get_bodyparts(df_likelihood)
+        app = QApplication.instance()
+        if app is None:
+            app = QApplication([])
 
-         # 付属肢ごとの尤度グラフを作成
-         for i, bodypart in enumerate(bodyparts):
-               try:
-                  likelihood = np.array(df_likelihood[f"{bodypart}|likelihood"].to_list(), dtype=float)
-               except Exception as e:
-                  continue
-               pw = pg.PlotWidget(title=f'フレーム別の尤度 ({bodypart})')
-               pw.setLabel('bottom', 'Frames')
-               pw.setLabel('left', '尤度')
-               pw.setYRange(0, 1.0)
-               color = pg.intColor(i, len(bodyparts))
-               pw.plot(likelihood, pen=pg.mkPen(color=color), name=bodypart)
-               exporter = ImageExporter(pw.plotItem)
-               filename = os.path.join(self.output_folder, f"{bodypart}_likelihood.png")
-               exporter.export(filename)
-               image_paths.append(filename)
+        # 付属肢ごとの尤度グラフを作成
+        for i, bodypart in enumerate(bodyparts):
+            try:
+                likelihood = np.array(df_likelihood[f"{bodypart}|likelihood"].to_list(), dtype=float)
+            except Exception as e:
+                continue
+            pw = pg.PlotWidget(title=f'フレーム別の尤度 ({bodypart})')
+            pw.setLabel('bottom', 'Frames')
+            pw.setLabel('left', '尤度')
+            pw.setYRange(0, 1.0)
+            color = pg.intColor(i, len(bodyparts))
+            pw.plot(likelihood, pen=pg.mkPen(color=color), name=bodypart)
+            exporter = ImageExporter(pw.plotItem)
+            filename = os.path.join(self.output_folder, f"{bodypart}_likelihood.png")
+            exporter.export(filename)
+            image_paths.append(filename)
 
-         # 全付属肢の尤度グラフを作成
-         pw_all = pg.PlotWidget(title='フレーム別の尤度 (全付属肢)')
-         pw_all.setLabel('bottom', 'Frames')
-         pw_all.setLabel('left', '尤度')
-         pw_all.setYRange(0, 1.0)
-         for i, bodypart in enumerate(bodyparts):
-               try:
-                  likelihood = np.array(df_likelihood[f"{bodypart}|likelihood"].to_list(), dtype=float)
-               except Exception as e:
-                  continue
-               color = pg.intColor(i, len(bodyparts))
-               pw_all.plot(likelihood, pen=pg.mkPen(color=color), name=bodypart)
-         exporter_all = ImageExporter(pw_all.plotItem)
-         filename_all = os.path.join(self.output_folder, "likelihood_plot.png")
-         exporter_all.export(filename_all)
-         image_paths.append(filename_all)
-         return image_paths
+        # 全付属肢の尤度グラフを作成
+        pw_all = pg.PlotWidget(title='フレーム別の尤度 (全付属肢)')
+        pw_all.setLabel('bottom', 'Frames')
+        pw_all.setLabel('left', '尤度')
+        pw_all.setYRange(0, 1.0)
+        for i, bodypart in enumerate(bodyparts):
+            try:
+                likelihood = np.array(df_likelihood[f"{bodypart}|likelihood"].to_list(), dtype=float)
+            except Exception as e:
+                continue
+            color = pg.intColor(i, len(bodyparts))
+            pw_all.plot(likelihood, pen=pg.mkPen(color=color), name=bodypart)
+        exporter_all = ImageExporter(pw_all.plotItem)
+        filename_all = os.path.join(self.output_folder, "likelihood_plot.png")
+        exporter_all.export(filename_all)
+        image_paths.append(filename_all)
+        return image_paths
 
-   class GradioInterface:
-      def __init__(self):
-         self.interface = gr.Interface(
-               fn=self.process_and_plot,
-               inputs=[
-                  gr.File(label="CSVファイルをドラッグ＆ドロップ"),
-                  gr.Textbox(
-                     label="付属肢の名前（カンマ区切り）",
-                     value="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(左), 触角(右), 頭部, 腹尾節"
-                  ),
-                  gr.Number(label="X軸の最大値", value=1920),
-                  gr.Number(label="Y軸の最大値", value=1080),
-                  gr.CheckboxGroup(
-                     label="プロットするグラフを選択",
-                     choices=["散布図", "軌跡図", "尤度グラフ"],
-                     value=["散布図", "軌跡図", "尤度グラフ"],
-                     type="value"
-                  )
-               ],
-               outputs=[
-                  gr.Gallery(label="散布図"),
-                  gr.File(label="ZIPダウンロード")
-               ],
-               title="DeepLabCutグラフ出力ツール",
-               description="CSVファイルからグラフを作成します。"
-         )
+class GradioInterface:
+    def __init__(self):
+        self.interface = gr.Interface(
+            fn=self.process_and_plot,
+            inputs=[
+                gr.File(label="CSVファイルをドラッグ＆ドロップ"),
+                gr.Textbox(
+                    label="付属肢の名前（カンマ区切り）",
+                    value="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(左), 触角(右), 頭部, 腹尾節"
+                ),
+                gr.Number(label="X軸の最大値", value=1920),
+                gr.Number(label="Y軸の最大値", value=1080),
+                gr.CheckboxGroup(
+                    label="プロットするグラフを選択",
+                    choices=["散布図", "軌跡図", "尤度グラフ"],
+                    value=["散布図", "軌跡図", "尤度グラフ"],
+                    type="value"
+                )
+            ],
+            outputs=[
+                gr.Gallery(label="散布図"),
+                gr.File(label="ZIPダウンロード")
+            ],
+            title="DeepLabCutグラフ出力ツール",
+            description="CSVファイルからグラフを作成します。"
+        )
 
-      def process_and_plot(self, file, bodypart_names, x_max, y_max, graph_choices):
-         processor = DataProcessor(bodypart_names, x_max, y_max)
-         df, df_likelihood = processor.process_csv(file.name)
+    def process_and_plot(self, file, bodypart_names, x_max, y_max, graph_choices):
+        processor = DataProcessor(bodypart_names, x_max, y_max)
+        df, df_likelihood = processor.process_csv(file.name)
 
-         all_image_paths = []
-         if "散布図" in graph_choices:
-               all_image_paths += processor.plot_scatter(df)
-         if "軌跡図" in graph_choices:
-               all_image_paths += processor.plot_trajectories(df)
-         if "尤度グラフ" in graph_choices:
-               all_image_paths += processor.plot_likelihood(df_likelihood)
+        all_image_paths = []
+        if "散布図" in graph_choices:
+            all_image_paths += processor.plot_scatter(df)
+        if "軌跡図" in graph_choices:
+            all_image_paths += processor.plot_trajectories(df)
+        if "尤度グラフ" in graph_choices:
+            all_image_paths += processor.plot_likelihood(df_likelihood)
 
-         shutil.make_archive(processor.output_folder, 'zip', processor.output_folder)
-         return all_image_paths, processor.output_folder + '.zip'
+        shutil.make_archive(processor.output_folder, 'zip', processor.output_folder)
+        return all_image_paths, processor.output_folder + '.zip'
 
-      def launch(self):
-         self.interface.launch()
+    def launch(self):
+        self.interface.launch()
 
 
-   if __name__ == "__main__":
-      gradio_app = GradioInterface()
-      gradio_app.launch()
+if __name__ == "__main__":
+    gradio_app = GradioInterface()
+    gradio_app.launch()