diff --git a/scripts/representative.py b/scripts/representative.py
index 047a959eb3bebd235caae96218d5a6dcf5aa2fd5..1228e9a5438ca0cd89b6e176e1f78f181895198c 100644
--- a/scripts/representative.py
+++ b/scripts/representative.py
@@ -1,13 +1,14 @@
-### Made by Hugo Gillet ###
 import pandas as pd
-import os
+import os 
 
-"""
+'''
 This part of the code take as input a gtf modified file 
 and return a dictionary of transcripts with best
 support level for each gene of the input
 
-"""
+'''
+
+
 
 
 def import_gtfSelection_to_df(gtf_modified_file: str) -> pd.DataFrame:
@@ -26,24 +27,20 @@ def import_gtfSelection_to_df(gtf_modified_file: str) -> pd.DataFrame:
     """
     pass
     if not type(gtf_modified_file) is str:
-        raise TypeError("Only str path is allowed")
-    df_input = pd.read_csv(
-        gtf_modified_file,
-        sep="\t",
-        lineterminator="\n",
-        names=["Gene_mixed", "Transcript", "Support_level", "Na1", "Na2"],
-    )
+      raise TypeError("Only str path is allowed")
+    df_input = pd.read_csv(gtf_modified_file, sep = '\t', lineterminator = '\n', 
+names = ["Gene_mixed", "Transcript", "Support_level", "Na1", "Na2"] )
     df_input["Support_level"] = df_input["Support_level"].replace(" ", "")
-    df_input["Gene"] = df_input["Gene_mixed"].str.extract("([A-Z]\w{0,})", expand=True)
-    df_input["Transcript_number"] = df_input["Gene_mixed"].str.extract(
-        "(^\d)", expand=True
-    )
-    df_clean = df_input.loc[:, ["Gene", "Transcript", "Support_level"]]
-    df_clean["Gene"] = df_clean["Gene"].fillna(method="ffill")
-    df_clean = df_clean.dropna(axis=0)
+    df_input["Gene"] = df_input["Gene_mixed"].str.extract('([A-Z]\w{0,})', expand=True)
+    df_input["Transcript_number"] = df_input["Gene_mixed"].str.extract('(^\d)', expand=True)
+    df_clean = df_input.loc[:, ["Gene", "Transcript","Support_level"]]
+    df_clean["Gene"] = df_clean["Gene"].fillna(method = 'ffill')
+    df_clean = df_clean.dropna(axis = 0)
     return df_clean
 
 
+
+
 def representative_transcripts_inDict(df_gtfSelection: pd.DataFrame) -> pd.DataFrame:
     """Return a dict containing for each gene transcripts 
         with highest confidence level
@@ -58,22 +55,18 @@ def representative_transcripts_inDict(df_gtfSelection: pd.DataFrame) -> pd.DataF
         Raises:
             TypeError : Only pandas DataFrame is allowed
     """
-    pass
+    pass 
 
     if not type(df_gtfSelection) is pd.DataFrame:
         raise TypeError("Only pandas DataFrame is allowed")
-    df_min = df_gtfSelection[
-        df_gtfSelection["Support_level"]
-        == df_gtfSelection.groupby("Gene")["Support_level"].transform(min)
-    ]
-    df_final = df_min.drop(columns=["Support_level"])
-    dict_representative_transcripts = (
-        df_final.groupby("Gene")["Transcript"].apply(list).to_dict()
-    )
-    return dict_representative_transcripts
-
-
-def find_repr_by_SupportLevel(intermediate_file: str) -> dict[str, str]:
+    df_min = df_gtfSelection[df_gtfSelection["Support_level"]==df_gtfSelection.groupby("Gene")["Support_level"].transform(min)]
+    df_final = df_min.drop(columns = ["Support_level"])
+    dict_representative_transcripts = df_final.groupby("Gene")["Transcript"].apply(list).to_dict()
+    return dict_representative_transcripts  
+
+
+
+def find_repr_by_SupportLevel(intermediate_file: str) -> dict[str,str]: 
     """Combine functions import_gtfSelection_to_df() 
         and representative_transcripts_inDict()
 
@@ -88,12 +81,11 @@ def find_repr_by_SupportLevel(intermediate_file: str) -> dict[str, str]:
 
           
     """
-    pass
+    pass 
     df_gtf = import_gtfSelection_to_df(intermediate_file)
     dict_reprTrans = representative_transcripts_inDict(df_gtf)
     return dict_reprTrans
 
 
-
-if __name__ == "__main__":
-   find_repr_by_SupportLevel()
+if __name__ == "__main__":  
+    find_repr_by_SupportLevel()