diff --git a/fit_app.py b/fit_app.py
index 8b23835..5a18cc7 100644
--- a/fit_app.py
+++ b/fit_app.py
@@ -114,7 +114,7 @@ def process_fit(file_path):
 
     try:
         fit_file = FitFile(file_path)
-        print(f"Verarbeite FIT-Datei: {file_path}")
+        print(f"\nVerarbeite FIT-Datei: {file_path}")
 
         # Sammle alle record-Daten
         records = []
@@ -134,11 +134,11 @@ def process_fit(file_path):
         print(f"DataFrame erstellt mit {len(df)} Zeilen und Spalten: {list(df.columns)}")
 
         # Debugging: Schaue welche Spalten verfügbar sind
-        print(f"Verfügbare Spalten: {df.columns.tolist()}")
+        # print(f"Verfügbare Spalten: {df.columns.tolist()}")     # Uncomment if needed - DEBUG purpose!
 
         # Suche nach Heart Rate in verschiedenen Formaten
         possible_hr_cols = [col for col in df.columns if 'heart' in col.lower() or 'hr' in col.lower()]
-        print(f"Mögliche Heart Rate Spalten: {possible_hr_cols}")
+        # print(f"Mögliche Heart Rate Spalten: {possible_hr_cols}")     # Uncomment if needed - DEBUG purpose!
 
         # Standard-Spaltennamen für verschiedene FIT-Formate
         lat_cols = ['position_lat', 'lat', 'latitude']
@@ -241,12 +241,13 @@ def process_fit(file_path):
         df = safe_add_column_to_dataframe(df, 'heart_rate', heart_rate)
         # ##############
 
-        # MY DEBUG:
+        # ######################################
+        # IF issues with heart_rate values, usw these DEBUG prints:
         #print(heart_rate)
         if 'heart_rate' in df.columns:
             df['heart_rate'] = pd.to_numeric(df['heart_rate'], errors='coerce')
             df['hr_smooth'] = df['heart_rate'].rolling(window=5, center=True).mean()
-            print(f"Heart rate range: {df['heart_rate'].min():.0f} - {df['heart_rate'].max():.0f} bpm")
+        #    print(f"Heart rate range: {df['heart_rate'].min():.0f} - {df['heart_rate'].max():.0f} bpm")     # Uncomment if needed - DEBUG purpose!
         else:
             print("Keine Heart Rate Daten gefunden!")
             df['heart_rate'] = np.nan
@@ -255,6 +256,7 @@ def process_fit(file_path):
             print(f"Verarbeitete FIT-Datei: {len(df)} Datenpunkte")
             print(f"Distanz: {df['cum_dist_km'].iloc[-1]:.2f} km")
             print(f"Dauer: {df['duration_hms'].iloc[-1]}")
+        # ######################################
 
         return df
 
@@ -359,7 +361,7 @@ def create_map_plot(df):
     df,
     lat='lat',
     lon='lon',
-    zoom=13,
+    zoom=13.5,
     height=800
     )
     
@@ -697,7 +699,7 @@ def create_speed_plot(df):
         line=dict(color='royalblue')
     ))
     fig.update_layout(
-        title=dict(text=f'Tempo über die Zeit (geglättet) - Durchschnittstempo: {mean_speed_kmh:.2f} km/h', font=dict(size=16)),
+        title=dict(text=f'Tempo über die Zeit (geglättet) - Ø {mean_speed_kmh:.2f} km/h', font=dict(size=16)),
         xaxis=dict(title='Zeit', tickformat='%H:%M', type='date'),
         yaxis=dict(title='Geschwindigkeit (km/h)', rangemode='tozero'),
         template='plotly_dark',
@@ -862,7 +864,7 @@ def create_heart_rate_plot(df):
 
 
 
-def create_pace_bars_plot(df):
+def create_pace_bars_plot(df, formatted_pace=None):
     # Ensure time column is datetime
     if not pd.api.types.is_datetime64_any_dtype(df['time']):
         df['time'] = pd.to_datetime(df['time'], errors='coerce')
@@ -920,45 +922,38 @@ def create_pace_bars_plot(df):
 
 
     # #########
-    # Add horizontal reference line - X-Werte für gesamte Breite
-    # Calculate average pace
+    # Calculate average pace if not provided from Info-Banner function
     total_distance_km = df['cum_dist_km'].iloc[-1]
     total_seconds = df['time_diff_sec'].iloc[-1]
 
     # Average pace (min/km) - KORRIGIERT
     if total_distance_km > 0:
         pace_sec_per_km = total_seconds / total_distance_km
-        pace_min_per_km = pace_sec_per_km / 60  # Konvertiere zu Minuten pro km
+        pace_min = int(pace_sec_per_km // 60)
+        pace_sec = int(pace_sec_per_km % 60)
+        formatted_pace = f"{pace_min}:{pace_sec:02d} min/km"
+        # Numerischen Wert für die dash'ed Linie berechnen
+        avg_pace_numeric = pace_sec_per_km / 60
     else:
-        pace_min_per_km = 0
+        formatted_pace = "N/A"
+        avg_pace_numeric = 0
 
+    # Add horizontal dash'ed reference line (avg_pace_numeric)
     fig.add_shape(
         type='line',
         x0=0,  # Start bei 0
         x1=total_distance_km,  # Ende bei maximaler Distanz
-        y0=pace_min_per_km,
-        y1=pace_min_per_km,
+        y0=avg_pace_numeric,
+        y1=avg_pace_numeric,
         line=dict(color='gray', width=1, dash='dash'),
     )
 
-    ## Optional: Text-Annotation für die durchschnittliche Pace
-    #fig.add_annotation(
-    #    x=total_distance_km * 0.8,  # Position bei 80% der Distanz
-    #    y=pace_min_per_km,
-    #    text=f"Ø {pace_min_per_km:.1f} min/km",
-    #    showarrow=True,
-    #    arrowhead=2,
-    #    arrowcolor="gray",
-    #    bgcolor="rgba(255,0,0,0.1)",
-    #    bordercolor="gray",
-    #    font=dict(color="white")
-    #)
-
-
 
+    title_text = f'Tempo je Kilometer'
+    title_text += f' - Ø {formatted_pace}'
 
     fig.update_layout(
-        title=dict(text='Tempo (min/km) je Kilometer', font=dict(size=16)),
+        title=dict(text=title_text, font=dict(size=16, color='white')),
         xaxis_title='Distanz (km)',
         yaxis_title='Minuten pro km',
         barmode='overlay',