Hervorhebung des Tag-Feldes in den Suchergebnissen: - Blauer Hintergrund für MEDISOFT-Tags - Oranger Hintergrund für MEDICONSULT-Tags - Verbesserte visuelle Darstellung der Tags
This commit is contained in:
398
app.py
398
app.py
@@ -20,9 +20,6 @@ logger = logging.getLogger(__name__)
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# Version der Anwendung
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VERSION = "1.2.1"
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# Pfad zur CSV-Datei
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CSV_FILE = 'data/customers.csv'
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# Pfad zur Datenbank
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DB_FILE = 'data/customers.db'
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@@ -33,213 +30,220 @@ load_dotenv()
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STATIC_PASSWORD = os.getenv('LOGIN_PASSWORD', 'default-password')
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ALLOWED_IP_RANGES = os.getenv('ALLOWED_IP_RANGES', '').split(',')
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def get_db_connection():
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"""Erstellt eine neue Datenbankverbindung mit Timeout"""
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conn = sqlite3.connect(DB_FILE, timeout=20)
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conn.row_factory = sqlite3.Row
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return conn
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def init_db():
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"""Initialisiert die SQLite-Datenbank mit der notwendigen Tabelle."""
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conn = sqlite3.connect(DB_FILE)
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conn = get_db_connection()
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c = conn.cursor()
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# Erstelle die Tabelle
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c.execute('''
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CREATE TABLE IF NOT EXISTS customers (
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id INTEGER PRIMARY KEY AUTOINCREMENT,
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nummer TEXT,
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name TEXT,
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strasse TEXT,
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plz TEXT,
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ort TEXT,
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telefon TEXT,
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mobil TEXT,
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email TEXT,
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bemerkung TEXT,
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fachrichtung TEXT
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)
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''')
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# Erstelle Indizes für alle Suchfelder
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c.execute('CREATE INDEX IF NOT EXISTS idx_customers_nummer ON customers(nummer)')
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c.execute('CREATE INDEX IF NOT EXISTS idx_customers_name ON customers(name)')
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c.execute('CREATE INDEX IF NOT EXISTS idx_customers_strasse ON customers(strasse)')
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c.execute('CREATE INDEX IF NOT EXISTS idx_customers_plz ON customers(plz)')
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c.execute('CREATE INDEX IF NOT EXISTS idx_customers_ort ON customers(ort)')
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c.execute('CREATE INDEX IF NOT EXISTS idx_customers_telefon ON customers(telefon)')
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c.execute('CREATE INDEX IF NOT EXISTS idx_customers_mobil ON customers(mobil)')
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c.execute('CREATE INDEX IF NOT EXISTS idx_customers_email ON customers(email)')
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c.execute('CREATE INDEX IF NOT EXISTS idx_customers_fachrichtung ON customers(fachrichtung)')
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# Erstelle einen zusammengesetzten Index für die häufigste Suchkombination
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c.execute('CREATE INDEX IF NOT EXISTS idx_customers_name_ort ON customers(name, ort)')
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conn.commit()
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conn.close()
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logger.info('Datenbank initialisiert')
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def import_csv():
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"""Importiert die Daten aus der CSV-Datei in die SQLite-Datenbank."""
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conn = sqlite3.connect(DB_FILE)
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c = conn.cursor()
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# Lösche bestehende Daten
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c.execute('DELETE FROM customers')
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try:
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# Lese die CSV-Datei mit pandas
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df = pd.read_csv(CSV_FILE, sep=',', encoding='utf-8', quotechar='"')
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# Erstelle die Kunden-Tabelle
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c.execute('''
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CREATE TABLE IF NOT EXISTS customers (
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id INTEGER PRIMARY KEY AUTOINCREMENT,
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nummer TEXT,
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name TEXT,
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strasse TEXT,
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plz TEXT,
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ort TEXT,
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telefon TEXT,
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mobil TEXT,
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email TEXT,
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bemerkung TEXT,
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fachrichtung TEXT,
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tag TEXT
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)
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''')
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# Entferne Anführungszeichen aus den Spaltennamen
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df.columns = df.columns.str.strip('"')
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# Erstelle Indizes für alle Suchfelder
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c.execute('CREATE INDEX IF NOT EXISTS idx_customers_nummer ON customers(nummer)')
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c.execute('CREATE INDEX IF NOT EXISTS idx_customers_name ON customers(name)')
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c.execute('CREATE INDEX IF NOT EXISTS idx_customers_strasse ON customers(strasse)')
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c.execute('CREATE INDEX IF NOT EXISTS idx_customers_plz ON customers(plz)')
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c.execute('CREATE INDEX IF NOT EXISTS idx_customers_ort ON customers(ort)')
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c.execute('CREATE INDEX IF NOT EXISTS idx_customers_telefon ON customers(telefon)')
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c.execute('CREATE INDEX IF NOT EXISTS idx_customers_mobil ON customers(mobil)')
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c.execute('CREATE INDEX IF NOT EXISTS idx_customers_email ON customers(email)')
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c.execute('CREATE INDEX IF NOT EXISTS idx_customers_fachrichtung ON customers(fachrichtung)')
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c.execute('CREATE INDEX IF NOT EXISTS idx_customers_tag ON customers(tag)')
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# Entferne Anführungszeichen aus den Werten
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for col in df.columns:
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if df[col].dtype == 'object':
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df[col] = df[col].str.strip('"')
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# Kombiniere Vorname und Nachname
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df['name'] = df['Vorname'] + ' ' + df['Nachname']
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# Importiere die Daten
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for _, row in df.iterrows():
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c.execute('''
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INSERT INTO customers (nummer, name, strasse, plz, ort, telefon, mobil, email, bemerkung, fachrichtung)
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VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
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''', (
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row['Nummer'],
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row['name'],
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row['Strasse'],
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row['PLZ'],
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row['Ort'],
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row['Tel'],
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row['Handy'],
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row['mail'],
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f"Fachrichtung: {row['Fachrichtung']}",
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row['Fachrichtung']
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))
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# Erstelle einen zusammengesetzten Index für die häufigste Suchkombination
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c.execute('CREATE INDEX IF NOT EXISTS idx_customers_name_ort ON customers(name, ort)')
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conn.commit()
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logger.info('CSV-Daten erfolgreich in die Datenbank importiert')
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logger.info('Datenbank initialisiert')
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except Exception as e:
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logger.error(f'Fehler beim Import der CSV-Daten: {str(e)}')
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logger.error(f'Fehler bei der Datenbankinitialisierung: {str(e)}')
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raise
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finally:
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conn.close()
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def search_customers(search_params):
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"""Sucht nach Kunden basierend auf den Suchparametern."""
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# Prüfe, ob alle Suchfelder leer sind
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if not any([
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search_params.get('q'),
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search_params.get('name'),
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search_params.get('ort'),
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search_params.get('nummer'),
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search_params.get('plz'),
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search_params.get('telefon'),
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search_params.get('email'),
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search_params.get('fachrichtung')
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]):
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return []
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conn = sqlite3.connect(DB_FILE)
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c = conn.cursor()
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def import_csv():
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"""Importiert die CSV-Datei in die Datenbank"""
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conn = None
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try:
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# Baue die SQL-Abfrage dynamisch auf
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query = "SELECT * FROM customers WHERE 1=1"
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params = []
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conn = get_db_connection()
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c = conn.cursor()
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# Allgemeine Suche über alle Felder
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if search_params.get('q'):
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search_term = f"%{search_params['q']}%"
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operator = search_params.get('operator', 'or').upper()
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# Lösche bestehende Daten
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c.execute('DELETE FROM customers')
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# Importiere MEDISOFT-Daten
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if os.path.exists('data/customers.csv'):
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logger.info("Importiere MEDISOFT-Daten...")
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df = pd.read_csv('data/customers.csv', encoding='utf-8')
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df.columns = df.columns.str.strip().str.replace('"', '')
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df = df.apply(lambda x: x.str.strip().str.replace('"', '') if x.dtype == "object" else x)
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df['name'] = df['Vorname'] + ' ' + df['Nachname']
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if operator == 'AND':
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# Bei UND-Verknüpfung müssen alle Begriffe in mindestens einem Feld vorkommen
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terms = search_params['q'].split()
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conditions = []
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for term in terms:
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term = f"%{term}%"
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conditions.append("(name LIKE ? OR ort LIKE ? OR nummer LIKE ? OR telefon LIKE ? OR mobil LIKE ? OR email LIKE ? OR bemerkung LIKE ? OR fachrichtung LIKE ?)")
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params.extend([term] * 8)
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query += " AND " + " AND ".join(conditions)
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else:
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# Bei ODER-Verknüpfung (Standard) muss mindestens ein Begriff in einem Feld vorkommen
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query += " AND (name LIKE ? OR ort LIKE ? OR nummer LIKE ? OR telefon LIKE ? OR mobil LIKE ? OR email LIKE ? OR bemerkung LIKE ? OR fachrichtung LIKE ?)"
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params.extend([search_term] * 8)
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for _, row in df.iterrows():
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c.execute('''
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INSERT INTO customers (name, nummer, strasse, plz, ort, telefon, mobil, email, fachrichtung, tag)
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VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
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''', (row['name'], row['Nummer'], row['Strasse'], row['PLZ'], row['Ort'],
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row['Tel'], row['Handy'], row['mail'], row['Fachrichtung'], 'medisoft'))
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else:
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logger.warning("MEDISOFT CSV-Datei nicht gefunden")
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# Spezifische Suche für einzelne Felder
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if search_params.get('name'):
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query += " AND name LIKE ?"
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params.append(f"%{search_params['name']}%")
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# Importiere MEDICONSULT-Daten
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if os.path.exists('data/customers_snk.csv'):
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logger.info("Importiere MEDICONSULT-Daten...")
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df_snk = pd.read_csv('data/customers_snk.csv', encoding='utf-8')
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df_snk.columns = df_snk.columns.str.strip().str.replace('"', '')
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df_snk = df_snk.apply(lambda x: x.str.strip().str.replace('"', '') if x.dtype == "object" else x)
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df_snk['name'] = df_snk['Vorname'] + ' ' + df_snk['Nachname']
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for _, row in df_snk.iterrows():
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c.execute('''
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INSERT INTO customers (name, nummer, strasse, plz, ort, telefon, mobil, email, fachrichtung, tag)
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VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
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''', (row['name'], row['Nummer'], row['Strasse'], row['PLZ'], row['Ort'],
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row['Tel'], row['Handy'], row['mail'], row['Fachrichtung'], 'mediconsult'))
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else:
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logger.warning("MEDICONSULT CSV-Datei nicht gefunden")
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if search_params.get('ort'):
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query += " AND ort LIKE ?"
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params.append(f"%{search_params['ort']}%")
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if search_params.get('nummer'):
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query += " AND nummer LIKE ?"
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params.append(f"%{search_params['nummer']}%")
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if search_params.get('plz'):
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query += " AND plz LIKE ?"
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params.append(f"%{search_params['plz']}%")
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conn.commit()
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logger.info("CSV-Daten erfolgreich in die Datenbank importiert")
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except Exception as e:
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logger.error(f"Fehler beim Importieren der CSV-Datei: {str(e)}")
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raise
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finally:
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if conn:
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conn.close()
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def search_customers():
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try:
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q = request.args.get('q', '')
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name = request.args.get('name', '')
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ort = request.args.get('ort', '')
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nummer = request.args.get('nummer', '')
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plz = request.args.get('plz', '')
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fachrichtung = request.args.get('fachrichtung', '')
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operator = request.args.get('operator', 'or')
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conn = get_db_connection()
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c = conn.cursor()
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# Basis-SQL-Query
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query = '''
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SELECT DISTINCT
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c.id,
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c.name,
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c.nummer,
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c.strasse,
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c.plz,
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c.ort,
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c.telefon,
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c.mobil,
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c.email,
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c.fachrichtung,
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c.tag
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FROM customers c
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WHERE 1=1
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'''
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params = []
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# Suchbedingungen
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conditions = []
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if q:
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search_terms = q.split()
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if operator == 'and':
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for term in search_terms:
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conditions.append('''
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(c.name LIKE ? OR c.nummer LIKE ? OR c.strasse LIKE ?
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OR c.plz LIKE ? OR c.ort LIKE ? OR c.telefon LIKE ?
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OR c.mobil LIKE ? OR c.email LIKE ? OR c.fachrichtung LIKE ?
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OR c.tag LIKE ?)
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''')
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params.extend([f'%{term}%'] * 10)
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else:
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term_conditions = []
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for term in search_terms:
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term_conditions.append('''
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(c.name LIKE ? OR c.nummer LIKE ? OR c.strasse LIKE ?
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OR c.plz LIKE ? OR c.ort LIKE ? OR c.telefon LIKE ?
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OR c.mobil LIKE ? OR c.email LIKE ? OR c.fachrichtung LIKE ?
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OR c.tag LIKE ?)
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''')
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params.extend([f'%{term}%'] * 10)
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conditions.append('(' + ' OR '.join(term_conditions) + ')')
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if name:
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conditions.append('c.name LIKE ?')
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params.append(f'%{name}%')
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if ort:
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conditions.append('c.ort LIKE ?')
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params.append(f'%{ort}%')
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if nummer:
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conditions.append('c.nummer LIKE ?')
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params.append(f'%{nummer}%')
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if plz:
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conditions.append('c.plz LIKE ?')
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params.append(f'%{plz}%')
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if fachrichtung:
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conditions.append('c.fachrichtung LIKE ?')
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params.append(f'%{fachrichtung}%')
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if conditions:
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query += ' AND ' + ' AND '.join(conditions)
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if search_params.get('fachrichtung'):
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query += " AND fachrichtung LIKE ?"
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params.append(f"%{search_params['fachrichtung']}%")
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# Führe die Abfrage aus
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c.execute(query, params)
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results = c.fetchall()
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# Formatiere die Ergebnisse
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customers = []
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formatted_results = []
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for row in results:
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customer = {
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'id': row[0],
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'nummer': row[1],
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'name': row[2],
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'name': row[1],
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'nummer': row[2],
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'strasse': row[3],
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'plz': row[4],
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'ort': row[5],
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'telefon': row[6],
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'mobil': row[7],
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'email': row[8],
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'bemerkung': row[9],
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'fachrichtung': row[10]
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'fachrichtung': row[9],
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'tag': row[10]
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}
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customers.append(customer)
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return customers
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except Exception as e:
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logger.error(f"Fehler bei der Kundensuche: {str(e)}")
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raise
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finally:
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formatted_results.append(customer)
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conn.close()
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return jsonify(formatted_results)
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except Exception as e:
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logger.error(f"Fehler bei der Suche: {str(e)}")
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return jsonify({'error': str(e)}), 500
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def clean_dataframe(df):
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"""Konvertiert NaN-Werte in None für JSON-Kompatibilität"""
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return df.replace({np.nan: None})
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# CSV-Datei laden
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def load_data():
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try:
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logger.info("Versuche CSV-Datei zu laden...")
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if not os.path.exists(CSV_FILE):
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logger.error(f"CSV-Datei '{CSV_FILE}' nicht gefunden!")
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return None
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# Lade CSV mit Komma als Trennzeichen
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df = pd.read_csv(CSV_FILE, sep=',', encoding='utf-8', quotechar='"')
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# Entferne Anführungszeichen aus den Spaltennamen
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df.columns = df.columns.str.strip('"')
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# Entferne Anführungszeichen aus den Werten
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for col in df.columns:
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if df[col].dtype == 'object':
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df[col] = df[col].str.strip('"')
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df = clean_dataframe(df)
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logger.info(f"CSV-Datei erfolgreich geladen. {len(df)} Einträge gefunden.")
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return df
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except Exception as e:
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logger.error(f"Fehler beim Laden der CSV-Datei: {str(e)}")
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return None
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@app.route('/login', methods=['GET', 'POST'])
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def login():
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@@ -296,38 +300,44 @@ def index():
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@app.route('/search')
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def search():
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try:
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# Hole die Suchparameter aus der Anfrage
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search_params = {
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'name': request.args.get('name', ''),
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'ort': request.args.get('ort', ''),
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'nummer': request.args.get('nummer', ''),
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'plz': request.args.get('plz', ''),
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'telefon': request.args.get('telefon', ''),
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'email': request.args.get('email', ''),
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'q': request.args.get('q', ''),
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'fachrichtung': request.args.get('fachrichtung', ''),
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'operator': request.args.get('operator', 'or')
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}
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# Führe die Suche in der Datenbank durch
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results = search_customers(search_params)
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# Führe die Suche durch und hole die Ergebnisse
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results = search_customers()
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# Wenn results ein Response-Objekt ist, geben wir es direkt zurück
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if isinstance(results, tuple):
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return results
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# Protokolliere die Anzahl der gefundenen Ergebnisse
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logger.info(f'Suchergebnisse gefunden: {len(results)}')
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logger.info(f'Suchergebnisse gefunden: {len(results.get_json())}')
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return jsonify(results)
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return results
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except Exception as e:
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logger.error(f'Fehler bei der Suche: {str(e)}')
|
||||
return jsonify({"error": str(e)}), 500
|
||||
return jsonify({'error': str(e)}), 500
|
||||
|
||||
def init_app(app):
|
||||
"""Initialisiert die Anwendung mit allen notwendigen Einstellungen."""
|
||||
with app.app_context():
|
||||
# Initialisiere die Datenbank
|
||||
init_db()
|
||||
# Importiere die CSV-Daten
|
||||
import_csv()
|
||||
logger.info("Anwendung erfolgreich initialisiert")
|
||||
try:
|
||||
# Stelle sicher, dass der data-Ordner existiert
|
||||
os.makedirs('data', exist_ok=True)
|
||||
|
||||
# Lösche die alte Datenbank, falls sie existiert
|
||||
if os.path.exists(DB_FILE):
|
||||
try:
|
||||
os.remove(DB_FILE)
|
||||
logger.info(f"Alte Datenbank {DB_FILE} wurde gelöscht")
|
||||
except Exception as e:
|
||||
logger.error(f"Fehler beim Löschen der alten Datenbank: {str(e)}")
|
||||
|
||||
# Initialisiere die Datenbank
|
||||
init_db()
|
||||
# Importiere die CSV-Daten
|
||||
import_csv()
|
||||
logger.info("Anwendung erfolgreich initialisiert")
|
||||
except Exception as e:
|
||||
logger.error(f"Fehler bei der Initialisierung: {str(e)}")
|
||||
raise
|
||||
|
||||
# Initialisiere die App
|
||||
init_app(app)
|
||||
|
||||
Reference in New Issue
Block a user