2022-10-07_13:36

__init__.py

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+__version__ = "0.0.1"

app.py

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+# AUTOGENERATED! DO NOT EDIT! File to edit: ../main.ipynb.
+
+# %% auto 0
+__all__ = ['analyse_fichier']
+
+# %% ../main.ipynb 2
+import gradio as gr
+import pandas as pd
+import tempfile
+from pathlib import Path
+from .rbc import add_classements
+
+
+def analyse_fichier(file):
+    fichier = file[0]
+    fichier_save = Path(fichier.name).stem
+    print(fichier_save)
+    
+    df_adherents = pd.read_excel(fichier)
+    df_classements = add_classements(df_adherents)
+    
+    with tempfile.NamedTemporaryFile(prefix=fichier_save+'_', suffix=".xlsx", delete=False) as temp:
+        print(temp.name)
+        df_classements.to_excel(temp.name)
+        return temp.name
+
+with gr.Blocks() as classement_converter:
+    file = gr.File(label="Adhérents", file_count=1)
+    greet_btn = gr.Button("Ajoute Classements")
+    outputs = gr.File(label="Adhérents avec classement", file_count=1, visible=True)
+    greet_btn.click(fn=analyse_fichier, inputs=file, outputs=outputs)
+
+classement_converter.launch()

rbc.py

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+# AUTOGENERATED! DO NOT EDIT! File to edit: ../update_classement.ipynb.
+
+# %% auto 0
+__all__ = ['PAGE', 'html_tables', 'df_adherents', 'get_classement', 'add_classements']
+
+# %% ../update_classement.ipynb 2
+PAGE = 'https://badmania.fr/club-joueurs-2254-riom-badminton-club.html'
+
+# %% ../update_classement.ipynb 3
+import pandas as pd
+html_tables = pd.read_html(PAGE)
+df_adherents = html_tables[0]
+
+
+# %% ../update_classement.ipynb 7
+def get_classement(
+    license_number,# le numero de license
+):
+#    print(license_number)
+    if len(df_adherents[df_adherents['Licence'] == license_number]['Class.'].values)>0:
+        return df_adherents[df_adherents['Licence'] == license_number]['Class.'].values[0]
+    else:
+        return ''
+
+# %% ../update_classement.ipynb 19
+def add_classements(dataframe, #le dataframe des adherents
+                   ):
+    dataframe = dataframe.copy()
+    dataframe['Classement']= dataframe['Licence'].apply(lambda x: get_classement(x))
+    return dataframe