Delete reconciliate_and_upload.py

reconciliate_and_upload.py

@@ -1,106 +0,0 @@
-from uploader import save_logs
-import os 
-import pandas as pd
-from rapidfuzz import process, fuzz
-from random import randint
-from time import sleep
-#other imports (google API)
-def fuzzy_match(row, choices, scorer, cutoff):
-    match = process.extractOne(row['Libellé d\'opération'], choices, scorer=scorer, score_cutoff=cutoff)
-    if match:
-        return match[0]
-    return "missing receipt"
-def reconciliate_and_upload(data_path,
-    name_of_csv,
-    folder_to_save_processed_reciepts,
-    folder_to_save_reconciled_data,
-    name_of_raw_transcripts = "transcript_raw.txt",
-    name_of_output = "[AI Generated] Output.xlsx" ):
-    
-    with open(f"{data_path}/{name_of_raw_transcripts}") as file:
-        transcripts = eval(file.read())
-
-    imgs = []
-    path_to_pdfs =data_path
-
-    for root, dirs, files in os.walk(path_to_pdfs):
-        for file in files:
-            if file.endswith('.png'):
-                print(os.path.join(root, file))
-                imgs.append({"path": os.path.join(root, file)})
-                pass
-        
-    list_transcripts_evaled = []
-    objects = []
-    for i,t in enumerate(transcripts):
-        content = eval(t["content"].replace('null', '-1'))
-        try:
-            obk = save_logs(imgs[i]["path"], f"P{i+1}.png", folder_to_save_processed_reciepts)
-        except:
-            print("sleeping a bit innit")
-            breakpoint()
-            sleep(randint(30,40))
-            obk = save_logs(imgs[i]["path"], f"P{i+1}.png", folder_to_save_processed_reciepts)
-            
-        objects.append(obk)
-        print("uploaded image!")
-        try:
-            list_transcripts_evaled.append({
-                "path": imgs[i]["path"],
-                "name_of_supplier" :content["name_of_supplier"],
-                                            "amount":content["amount"],
-                                            "currency":content["currency"],
-                                            "date": content["date"]})
-        except:
-            breakpoint()
-    urls = []
-    for ob in objects:
-        url = "https://drive.google.com/file/d/" + ob["id"]
-        urls.append(url)
-    df_app = pd.DataFrame(list_transcripts_evaled)
-    df_app["amount"] = df_app["amount"].astype(str).str.replace(" ", "").str.replace(",", ".").str.replace("N/A", "-1").astype("float")
-    df_app["date"] = pd.to_datetime(df_app['date'], format="%d/%m/%Y", errors='coerce')
-    df_app["url"] = urls
-    df_app = df_app.drop_duplicates(["name_of_supplier", "amount", "date"]).reset_index(drop=True)
-    
-    df_opp_app = pd.read_csv(f"{data_path}/{name_of_csv}",skiprows=3)
-    df_opp_app["Débit"] = df_opp_app["Débit"].str.replace(" ", "").str.replace(",", ".").astype("float")
-    df_opp_app["Crédit"] = df_opp_app["Crédit"].str.replace(" ", "").str.replace(",", ".").astype("float")
-    df_opp_app["Date"] = pd.to_datetime(df_opp_app['Date'], format="%d/%m/%Y", errors='coerce')
-
-    
-    merged_df_app = pd.merge(df_opp_app, df_app, left_on=['Débit'], right_on=['amount'], how='left').drop(columns=["currency", "date","path"]).rename(columns={"name_of_supplier": "Nom fournisseur facture"})
-    merged_df_app["Nom fournisseur facture"] = merged_df_app["Nom fournisseur facture"].fillna("* Facture manquante *")
-    # Merge on amount (Débit and amount)
-    merged_df_app = pd.merge(df_opp_app, df_app, left_on='Débit', right_on='amount', how='left', suffixes=('_ops', '_df'))
-
-
-
-    # Apply fuzzy matching
-    raw_choices = df_app['name_of_supplier'].tolist()
-    choices = []
-    for r in raw_choices:
-        choices.append(r.upper())
-    merged_df_app['fuzzy_matched_supplier'] = merged_df_app.apply(lambda row: fuzzy_match(row, choices, fuzz.WRatio, 80), axis=1)
-    merged_df_app = merged_df_app.drop_duplicates(subset=["Date", "Valeur", "Libellé d'opération", "Débit"])
-    # Identify residuals in df that were not matched
-    df_residuals_app = df_app[~df_app['name_of_supplier'].isin(merged_df_app['name_of_supplier'])]
-
-    # Replace original supplier column with fuzzy_matched_supplier and drop the name_of_supplier column from df
-    merged_df_app['name_of_supplier'] = merged_df_app['fuzzy_matched_supplier']
-    # merged_df_app.drop(columns=['name_of_supplier', 'fuzzy_matched_supplier'], inplace=True)
-    merged_df_app.drop(columns=["name_of_supplier", "currency", "date", "path", "fuzzy_matched_supplier"], inplace=True)
-    df_residuals_app.drop(columns=["path"], inplace=True)
-    merged_df_app['url'] = merged_df_app['url'].apply(lambda x: f'=HYPERLINK("{x}", "Voir Facture")' if pd.notna(x) else '')
-    df_residuals_app['url'] = df_residuals_app['url'].apply(lambda x: f'=HYPERLINK("{x}", "Voir Facture")' if pd.notna(x) else '')
-
-
-    with pd.ExcelWriter(name_of_output) as writer:
-        merged_df_app.to_excel(writer, sheet_name='Données réconciliées', index=False)
-        df_residuals_app.to_excel(writer, sheet_name='Résidus et transactions introuvables', index=False)
-
-
-
-    id_output = save_logs(name_of_output, name_of_output , folder_to_save_reconciled_data)
-
-    return id_output