.gitignore

@@ -1,3 +1,2 @@
 app/__pycache__/
-app/.flask_session/
 .env

app/app.py

@@ -5,13 +5,24 @@ from io import BytesIO
 from flask_session import Session
 
 app = Flask(__name__)
-app.secret_key = "gfbierpf934hftrntr45otgß45890tfh34gft45rw"  # replace with a secure random key
+app.secret_key = "your-secret-key"  # replace with a secure random key
+# Configure server-side session (filesystem) to avoid size limits in cookies
 app.config['SESSION_TYPE'] = 'filesystem'
 app.config['SESSION_FILE_DIR'] = './.flask_session/'
 Session(app)
 
-STRIPE_COLS = ['Type', 'ID', 'Created', 'Description', 'Amount', 'Currency', 'Converted Amount', 'Fees', 'Net', 'Converted Currency', 'Details']
+STRIPE_COLS = [
-RAISENOW_COLS = ['Identifikationsnummer', 'Erstellt', 'UTC-Offset', 'Status', 'Betrag', 'Währung', 'Übernommene Gebühren - Betrag', 'Übernommene Gebühren - Währung', 'Zahlungsmethode', 'Zahlungsanbieter', 'Nettobetrag', 'Auszahlungswährung']
+    'Type','ID','Created','Description','Amount','Currency',
+    'Converted Amount','Fees','Net','Converted Currency',
+    'Customer Name','Customer Email','Details'
+]
+RAISENOW_COLS = [
+    'Identifikationsnummer','Erstellt','UTC-Offset','Status',
+    'Betrag','Währung','Übernommene Gebühren - Betrag',
+    'Übernommene Gebühren - Währung','Zahlungsmethode',
+    'Zahlungsanbieter','Vorname','Nachname','E-Mail-Adresse',
+    'custom_parameters.altruja_action_name','custom_parameters.altruja_custom1_code'
+]
 
 
 def get_dataframe(key, cols):
@@ -26,126 +37,92 @@ def get_dataframe(key, cols):
     return df
 
 
+import pandas as pd
+
 def get_merged_df(table_name):
     """
-    Return a DataFrame for the given table_name based on Stripe and Raisenow inputs,
+    Return a DataFrame for the given table_name based on stripe and raisenow inputs,
-    enforcing strict one-to-one matching with:
+    including a secondary merge for date tolerance of ±1 day.
-      - exact same-day matches first
-      - then ±1-day fuzzy matches
-      - no pandas merge suffixes at all
-      - all original columns (including Raisenow's norm_zweck) preserved
     """
+    stripe_df = get_dataframe('stripe_import', STRIPE_COLS)
+    raisenow_df = get_dataframe('raiseNow_import', RAISENOW_COLS)
 
-    # --- load & normalize Stripe ---
+    # Normalize stripe
-    stripe = (
+    stripe_df = stripe_df.query("Type == 'Charge'")
-        get_dataframe('stripe_import', STRIPE_COLS)
+    stripe_df['norm_date'] = pd.to_datetime(stripe_df['Created'], format='%Y-%m-%d %H:%M')
-        .query("Type == 'Charge'")
+    stripe_df['norm_amount'] = stripe_df['Amount'].astype(str).str.replace(',', '.')
-        .copy()
+    stripe_df['norm_amount'] = stripe_df['norm_amount'].astype(float)
-    )
+    stripe_df['norm_email'] = stripe_df['Customer Email'].astype(str)
-    stripe['idx_stripe']  = stripe.index
+    stripe_df['norm_name'] = stripe_df.apply(
-    stripe['norm_date']   = pd.to_datetime(stripe['Created'],    format='%Y-%m-%d %H:%M')
+        lambda x: x['Customer Name'] if x.get('Customer Name') else x['Details'],
-    stripe['norm_amount'] = stripe['Amount'].astype(str).str.replace(',', '.').astype(float)
-    stripe['norm_email']  = stripe['Customer Email'].astype(str)
-    stripe['norm_name']   = stripe.apply(
-        lambda r: r['Customer Name'] or r['Details'], axis=1
-    )
-    
-    # --- load & normalize Raisenow ---
-    raisenow = (
-        get_dataframe('raiseNow_import', RAISENOW_COLS)
-        .query("Zahlungsmethode != 'paypal'")
-        .query("Status == 'succeeded'")
-        .copy()
-    )
-    
-    raisenow['idx_raisenow'] = raisenow.index
-    raisenow['norm_date']    = pd.to_datetime(raisenow['Erstellt'], format='%Y-%m-%d %H:%M')
-    raisenow['norm_amount']  = raisenow['Betrag'].astype(float)
-    raisenow['norm_email']   = raisenow['E-Mail-Adresse'].astype(str)
-    raisenow['norm_name']    = raisenow['Vorname'].astype(str) + ' ' + raisenow['Nachname'].astype(str)
-    
-    # start with two‐step assignment
-    raisenow['norm_zweck'] = raisenow.apply(
-        lambda r: r.get('custom_parameters.altruja_action_name')
-                or r.get('custom_parameters.altruja_custom1_code'),
         axis=1
     )
-    # additional assignment: build a mask of rows where norm_zweck is still empty/NaN
+
-    mask = raisenow['norm_zweck'].isna() | (raisenow['norm_zweck'] == '')
+    # Normalize raisenow
-    raisenow.loc[mask, 'norm_zweck'] = (
+    raisenow_df = raisenow_df.query("Zahlungsmethode != 'paypal'")
-        raisenow.loc[mask, 'raisenow_parameters.product.source_url']
+    raisenow_df = raisenow_df.query("Status == 'succeeded'")
-            .str.extract(r'https?://[^/]+/([^/?#]+)')[0]
+    raisenow_df['norm_date'] = pd.to_datetime(raisenow_df['Erstellt'], format='%Y-%m-%d %H:%M')
+    raisenow_df['norm_amount'] = raisenow_df['Betrag'].astype(float)
+    raisenow_df['norm_name'] = (
+        raisenow_df['Vorname'].astype(str) + ' ' + raisenow_df['Nachname'].astype(str)
+    )
+    raisenow_df['norm_email'] = raisenow_df['E-Mail-Adresse'].astype(str)
+    raisenow_df['norm_zweck'] = raisenow_df.apply(
+        lambda x: x['custom_parameters.altruja_action_name']
+        if x.get('custom_parameters.altruja_action_name')
+        else x.get('custom_parameters.altruja_custom1_code'),
+        axis=1
     )
 
-    # --- return raw tables if requested ---
+    if table_name in ('stripe_import', 'raiseNow_import'):
-    if table_name == 'stripe_import':
+        df = stripe_df if table_name == 'stripe_import' else raisenow_df
-        return stripe.dropna(axis=1, how='all')
+        return df.dropna(axis=1, how='all')
-    if table_name == 'raiseNow_import':
-        return raisenow.dropna(axis=1, how='all')
 
-    # --- 1) Greedy exact same-day matches ---
+    # Exact merge
-    pairs = []
+    exact = pd.merge(
-    # index Raisenow rows for fast lookup + dropping
+        stripe_df,
-    rr = raisenow.set_index('idx_raisenow')
+        raisenow_df,
-    for _, s in stripe.iterrows():
+        on=['norm_amount', 'norm_name'],
-        # filter candidates by amount & name
+        how='outer',
-        cand = rr[
+        suffixes=('_stripe', '_raisenow'),
-            (rr['norm_amount'] == s['norm_amount']) &
+        indicator=True
-            (rr['norm_name']   == s['norm_name'])
+    )
-        ].copy()
+    exact['date_diff'] = (
-        if cand.empty:
+        exact['norm_date_stripe'].dt.date - exact['norm_date_raisenow'].dt.date
-            continue
+    ).abs()
-        # compute absolute date difference (days only)
-        date_diff = (cand['norm_date'].dt.normalize() - s['norm_date'].normalize()).abs()
-        exact_cand = cand[date_diff == pd.Timedelta(0)]
-        if not exact_cand.empty:
-            # pick the first exact match
-            best = exact_cand.index[0]
-            pairs.append((int(s['idx_stripe']), int(best)))
-            rr = rr.drop(best)
 
-    # --- 2) Greedy fuzzy ±1-day matches on remaining rows ---
+    # Separate matches
-    used_stripe = {s for s, _ in pairs}
+    exact_matches = exact[(exact['_merge'] == 'both') & (exact['date_diff'] == pd.Timedelta(0))].copy()
-    stripe_left = stripe[~stripe['idx_stripe'].isin(used_stripe)].copy()
+    stripe_only = exact[exact['_merge'] == 'left_only'].copy()
-    for _, s in stripe_left.iterrows():
+    raisenow_only = exact[exact['_merge'] == 'right_only'].copy()
-        cand = rr[
-            (rr['norm_amount'] == s['norm_amount']) &
-            (rr['norm_name']   == s['norm_name'])
-        ].copy()
-        if cand.empty:
-            continue
-        date_diff = (cand['norm_date'].dt.normalize() - s['norm_date'].normalize()).abs()
-        cand = cand[date_diff <= pd.Timedelta(days=1)]
-        if cand.empty:
-            continue
-        # pick the one with the smallest gap
-        best = date_diff.idxmin()
-        pairs.append((int(s['idx_stripe']), int(best)))
-        rr = rr.drop(best)
 
-    # --- build the merged DataFrame without suffixes ---
+    # Fuzzy merge within ±1 day for remaining
-    merged_rows = []
+    # Merge stripe_only with raisenow_only on name and amount
-    for s_idx, r_idx in pairs:
+    fuzzy = pd.merge(
-        srow = stripe.loc[s_idx].to_dict()
+        stripe_only.drop(columns=['_merge']),
-        rrow = raisenow.loc[r_idx].to_dict()
+        raisenow_only.drop(columns=['_merge']),
-        # drop any overlapping keys so we never get suffixes
+        on=['norm_amount', 'norm_name'],
-        for k in ['norm_amount','norm_name','norm_date','norm_email','idx_stripe']:
+        suffixes=('_stripe', '_raisenow')
-            rrow.pop(k, None)
+    )
-        # now combine so stripe values win for those keys, and raisenow adds its own columns
+    fuzzy['date_diff'] = (
-        merged = {**srow, **rrow}
+        fuzzy['norm_date_stripe'].dt.date - fuzzy['norm_date_raisenow'].dt.date
-        merged_rows.append(merged)
+    ).abs()
+    fuzzy_matches = fuzzy[fuzzy['date_diff'] <= pd.Timedelta(days=1)].copy()
 
-    combined = pd.DataFrame(merged_rows)
+    # Combine exact and fuzzy
+    combined = pd.concat([exact_matches, fuzzy_matches], ignore_index=True)
+    combined = combined.drop(columns=['_merge', 'date_diff'], errors='ignore')
 
-    # --- slice out the requested view ---
+    # Determine outputs
     if table_name == 'merged':
         result = combined
     elif table_name == 'stripe_only':
-        used = {s for s, _ in pairs}
+        # Exclude those in combined
-        result = stripe[~stripe['idx_stripe'].isin(used)]
+        matched_stripe_ids = combined['<unique_id_column>_stripe'] if '<unique_id_column>_stripe' in combined else None
+        result = stripe_df[~stripe_df.index.isin(matched_stripe_ids)]
     elif table_name == 'raisenow_only':
-        used = {r for _, r in pairs}
+        matched_raisenow_ids = combined['<unique_id_column>_raisenow'] if '<unique_id_column>_raisenow' in combined else None
-        result = raisenow[~raisenow['idx_raisenow'].isin(used)]
+        result = raisenow_df[~raisenow_df.index.isin(matched_raisenow_ids)]
     else:
         raise ValueError(f"Unknown table_name '{table_name}'")
 
@@ -171,6 +148,7 @@ def upload():
         raw = raw.dropna(how='all').dropna(axis=1, how='all')
         raw = raw.astype(object).replace({np.nan: None})
         cols = list(raw.columns)
+
         if cols[:len(STRIPE_COLS)] == STRIPE_COLS:
             key = 'stripe_import'
             dedupe_col = 'ID'
@@ -193,8 +171,10 @@ def upload():
 @app.route('/get_table')
 def get_table():
     table = request.args.get('table')
-    
+    try:
-    df = get_merged_df(table)
+        df = get_merged_df(table)
+    except Exception as e:
+        return jsonify({'error': str(e)}), 400
 
     df = df.astype(object).where(pd.notnull(df), None)
     return jsonify({
@@ -205,35 +185,13 @@ def get_table():
 
 @app.route('/download')
 def download():
-    sheets = {
+    sheets = { name: get_merged_df(name)
-        name: get_merged_df(name)
+        for name in ['stripe_import','raiseNow_import','merged','stripe_only','raisenow_only'] }
-        for name in [
-            'stripe_import',
-            'raiseNow_import',
-            'merged',
-            'stripe_only',
-            'raisenow_only'
-        ]
-    }
 
     output = BytesIO()
     with pd.ExcelWriter(output, engine='xlsxwriter') as writer:
-        workbook = writer.book
         for name, df in sheets.items():
             df.to_excel(writer, sheet_name=name, index=False)
-            worksheet = writer.sheets[name]
-
-            # 1) Freeze header row
-            worksheet.freeze_panes(1, 0)
-
-            # 2) Autofilter on the header row across all columns
-            #    (0,0) is the top-left cell; (len(df), len(df.columns)-1) covers all data rows
-            worksheet.autofilter(0, 0, df.shape[0], df.shape[1] - 1)
-
-            # 3) Set column widths to match first-row entries
-            first_row = df.iloc[0].astype(str)
-            for col_idx, cell_value in enumerate(first_row):
-                worksheet.set_column(col_idx, col_idx, len(cell_value) + 2)
 
     output.seek(0)
     return send_file(

app/requirements.txt

@@ -1,5 +1,4 @@
 Flask
 flask_session
 pandas
-openpyxl
+openpyxl
-xlsxwriter

app/templates/index.html

@@ -70,6 +70,7 @@
       
       // error handling
       if (!resp.ok) {
+        if (table) table.hideLoader();
         return alert(json.error || 'Error loading');
       }