--- a
+++ b/scripts/adicap.py
@@ -0,0 +1,72 @@
+"""
+Process ADICAP codes
+Thésaurus de la codification ADICAP - Index raisonné des lésions
+source : https://smt.esante.gouv.fr/terminologie-adicap/
+
+"""
+
+import gzip
+import json
+import re
+from pathlib import Path
+
+import pandas as pd
+import typer
+
+
+def parse_each_dict(df, dictionaryCode: str):
+    d_spec = df.query(f"dictionaryCode=='{dictionaryCode}'")
+    d_spec.fillna("", inplace=True)
+
+    decode_d_spec = {}
+
+    for code, label, anatomyCode in d_spec[["code", "label", "anatomyCode"]].values:
+        if dictionaryCode == "D5":
+            if re.match(r"[0-9]{4}", code) is None:
+                decode_d_spec[str(anatomyCode) + str(code)] = label
+        else:
+            decode_d_spec[str(anatomyCode) + str(code)] = label
+
+    d_value = decode_d_spec.pop(dictionaryCode)
+
+    return dict(label=d_value, codes=decode_d_spec)
+
+
+def get_decode_dict(df, dict_keys=["D1", "D2", "D3", "D4", "D5", "D6", "D7"]):
+    decode_dict = {}
+    for key in dict_keys:
+
+        decode_dict[key] = parse_each_dict(df, dictionaryCode=key)
+
+    return decode_dict
+
+
+def run(
+    raw: Path = typer.Argument(..., help="Path to the raw file"),
+    output: Path = typer.Option(
+        "edsnlp/resources/adicap.json.gz", help="Path to the output CSV table."
+    ),
+) -> None:
+    """
+    Convenience script to automatically process the ADICAP codes
+    into a processable file.
+    """
+
+    df = pd.read_excel(
+        raw,
+        sheet_name="rawdatas",
+        header=0,
+    )
+
+    decode_dict = get_decode_dict(df)
+
+    typer.echo(f"Saving to {output}")
+
+    with gzip.open(output, "w") as f:
+        f.write(json.dumps(decode_dict).encode("utf-8"))
+
+    typer.echo("Done !")
+
+
+if __name__ == "__main__":
+    typer.run(run)