Update app.py

app.py

@@ -19,7 +19,7 @@ import traceback
 # No Streamlit config needed
 
 # --- Constants & Defaults ---
-TIME_PERIOD_OPTIONS = { "Last Month": timedelta(days=30), "Last 3 Months": timedelta(days=90), "Last Year": timedelta(days=365), "Last 3 Years": timedelta(days=3*365) }
+TIME_PERIOD_OPTIONS = {"Last Month": timedelta(days=30), "Last 3 Months": timedelta(days=90), "Last Year": timedelta(days=365), "Last 3 Years": timedelta(days=3*365)}
 DEFAULT_TIME_PERIOD = "Last Year"
 PERF_TYPE_OPTIONS_SINGLE = ['Bullet', 'Blitz', 'Rapid']
 DEFAULT_PERF_TYPE = 'Bullet'
@@ -28,7 +28,7 @@ ECO_CSV_PATH = "eco_to_opening.csv"
 TITLES_TO_ANALYZE = ['GM', 'IM', 'FM', 'CM', 'WGM', 'WIM', 'WFM', 'WCM', 'NM']
 
 # =============================================
-# Helper Function: Categorize Time Control *** OBSESSIVELY REWRITTEN ***
+# Helper Function: Categorize Time Control
 # =============================================
 def categorize_time_control(tc_str, speed_info):
     """Categorizes time control based on speed info or parsed string."""
@@ -47,49 +47,39 @@ def categorize_time_control(tc_str, speed_info):
         parts = tc_str.split('+')
         if len(parts) == 2:
             base_str, increment_str = parts[0], parts[1]
-            base, increment = None, None # Initialize
-            # *** Isolate ONLY the conversion in try-except ***
             try:
                 base = int(base_str)
                 increment = int(increment_str)
+                total = base + 40 * increment
+                if total >= 1500: return 'Classical'
+                if total >= 480: return 'Rapid'
+                if total >= 180: return 'Blitz'
+                if total > 0: return 'Bullet'
+                return 'Unknown'
             except ValueError:
-                return 'Unknown' # Conversion failed
-
-            # *** Classification happens AFTER successful conversion ***
-            total = base + 40 * increment
-            if total >= 1500: return 'Classical'
-            if total >= 480: return 'Rapid'
-            if total >= 180: return 'Blitz'
-            if total > 0 : return 'Bullet'
-            return 'Unknown'
+                return 'Unknown'
         else:
-            # '+' was present but not exactly two parts
             return 'Unknown'
 
     # 4. Handle format like "300" (only base time)
     else:
-        base = None # Initialize
-        # *** Isolate ONLY the conversion in try-except ***
         try:
             base = int(tc_str)
+            if base >= 1500: return 'Classical'
+            if base >= 480: return 'Rapid'
+            if base >= 180: return 'Blitz'
+            if base > 0: return 'Bullet'
+            return 'Unknown'
         except ValueError:
-            # *** Fallback to keywords ONLY if conversion fails ***
             tc_lower = tc_str.lower()
             if 'classical' in tc_lower: return 'Classical'
             if 'rapid' in tc_lower: return 'Rapid'
             if 'blitz' in tc_lower: return 'Blitz'
             if 'bullet' in tc_lower: return 'Bullet'
-            return 'Unknown' # Failed conversion and keyword match
-
-        # *** Classification happens AFTER successful conversion ***
-        if base >= 1500: return 'Classical'
-        if base >= 480: return 'Rapid'
-        if base >= 180: return 'Blitz'
-        if base > 0 : return 'Bullet'
-        return 'Unknown' # Base time is 0 or negative
+            return 'Unknown'
 
 # =============================================
-# Helper Function: Load ECO Mapping (Unchanged)
+# Helper Function: Load ECO Mapping
 # =============================================
 ECO_MAPPING = {}
 try:
@@ -97,214 +87,361 @@ try:
     if "ECO Code" in df_eco_global.columns and "Opening Name" in df_eco_global.columns:
         ECO_MAPPING = df_eco_global.drop_duplicates(subset=['ECO Code']).set_index('ECO Code')['Opening Name'].to_dict()
         print(f"OK: Loaded {len(ECO_MAPPING)} ECO mappings.")
-    else: print(f"WARN: ECO file '{ECO_CSV_PATH}' missing columns.")
-except FileNotFoundError: print(f"WARN: ECO file '{ECO_CSV_PATH}' not found.")
-except Exception as e: print(f"WARN: Error loading ECO file: {e}")
+    else:
+        print(f"WARN: ECO file '{ECO_CSV_PATH}' missing columns.")
+except FileNotFoundError:
+    print(f"WARN: ECO file '{ECO_CSV_PATH}' not found.")
+except Exception as e:
+    print(f"WARN: Error loading ECO file: {e}")
 
 # =============================================
-# API Data Loading and Processing Function (Unchanged)
+# API Data Loading and Processing Function
 # =============================================
-@gr.Progress(track_tqdm=True)
 def load_from_lichess_api(username: str, time_period_key: str, perf_type: str, rated: bool, eco_map: dict, progress=None):
-    # ... (Code identical to version 15 - calls the fixed helper now) ...
     if not username: return pd.DataFrame(), "⚠️ Enter username."
     if not perf_type: return pd.DataFrame(), "⚠️ Select game type."
-    if progress: progress(0, desc="Initializing...");
-    username_lower=username.lower(); status_message=f"Fetching {perf_type} games..."
-    if progress: progress(0.1, desc=status_message);
-    since_timestamp_ms=None; time_delta=TIME_PERIOD_OPTIONS.get(time_period_key)
-    if time_delta: start_date=datetime.now(timezone.utc)-time_delta; since_timestamp_ms=int(start_date.timestamp()*1000)
-    api_params={"rated":str(rated).lower(), "perfType":perf_type.lower(), "opening":"true", "moves":"false", "tags":"false", "pgnInJson":"false" }
-    if since_timestamp_ms: api_params["since"]=since_timestamp_ms
-    api_url=f"https://lichess.org/api/games/user/{username}"; headers={"Accept":"application/x-ndjson"}
-    all_games_data=[]; error_counter=0; lines_processed=0
+    if progress: progress(0, desc="Initializing...")
+    username_lower = username.lower()
+    status_message = f"Fetching {perf_type} games..."
+    if progress: progress(0.1, desc=status_message)
+    since_timestamp_ms = None
+    time_delta = TIME_PERIOD_OPTIONS.get(time_period_key)
+    if time_delta:
+        start_date = datetime.now(timezone.utc) - time_delta
+        since_timestamp_ms = int(start_date.timestamp() * 1000)
+    api_params = {"rated": str(rated).lower(), "perfType": perf_type.lower(), "opening": "true", "moves": "false", "tags": "false", "pgnInJson": "false"}
+    if since_timestamp_ms: api_params["since"] = since_timestamp_ms
+    api_url = f"https://lichess.org/api/games/user/{username}"
+    headers = {"Accept": "application/x-ndjson"}
+    all_games_data = []
+    error_counter = 0
+    lines_processed = 0
     try:
-        response=requests.get(api_url, params=api_params, headers=headers, stream=True); response.raise_for_status()
+        response = requests.get(api_url, params=api_params, headers=headers, stream=True)
+        response.raise_for_status()
         if progress: progress(0.3, desc="Processing stream...")
         for line in response.iter_lines():
             if line:
-                lines_processed += 1; game_data_raw=line.decode('utf-8'); game_data=None;
-                if progress and lines_processed % 100 == 0: progress(0.3 + (lines_processed % 1000 / 2000), desc=f"Processing game {lines_processed}...")
+                lines_processed += 1
+                game_data_raw = line.decode('utf-8')
+                if progress and lines_processed % 100 == 0:
+                    progress(0.3 + (lines_processed % 1000 / 2000), desc=f"Processing game {lines_processed}...")
                 try:
-                    game_data=json.loads(game_data_raw); white_info=game_data.get('players',{}).get('white',{}); black_info=game_data.get('players',{}).get('black',{})
-                    white_user=white_info.get('user',{}); black_user=black_info.get('user',{}); opening_info=game_data.get('opening',{}); clock_info=game_data.get('clock')
-                    game_id=game_data.get('id','N/A'); created_at_ms=game_data.get('createdAt'); game_date=pd.to_datetime(created_at_ms,unit='ms',utc=True,errors='coerce');
+                    game_data = json.loads(game_data_raw)
+                    white_info = game_data.get('players', {}).get('white', {})
+                    black_info = game_data.get('players', {}).get('black', {})
+                    white_user = white_info.get('user', {})
+                    black_user = black_info.get('user', {})
+                    opening_info = game_data.get('opening', {})
+                    clock_info = game_data.get('clock')
+                    game_id = game_data.get('id', 'N/A')
+                    created_at_ms = game_data.get('createdAt')
+                    game_date = pd.to_datetime(created_at_ms, unit='ms', utc=True, errors='coerce')
                     if pd.isna(game_date): continue
-                    variant=game_data.get('variant','standard'); speed=game_data.get('speed','unknown'); perf=game_data.get('perf','unknown'); status=game_data.get('status','unknown'); winner=game_data.get('winner')
-                    white_name=white_user.get('name','Unknown'); black_name=black_user.get('name','Unknown'); white_title=white_user.get('title'); black_title=black_user.get('title')
-                    white_rating=pd.to_numeric(white_info.get('rating'),errors='coerce'); black_rating=pd.to_numeric(black_info.get('rating'),errors='coerce')
-                    player_color,player_elo,opp_name_raw,opp_title_raw,opp_elo=(None,None,'Unknown',None,None)
-                    if username_lower==white_name.lower(): player_color,player_elo,opp_name_raw,opp_title_raw,opp_elo=('White',white_rating,black_name,black_title,black_rating)
-                    elif username_lower==black_name.lower(): player_color,player_elo,opp_name_raw,opp_title_raw,opp_elo=('Black',black_rating,white_name,white_title,white_rating)
-                    else: continue
+                    variant = game_data.get('variant', 'standard')
+                    speed = game_data.get('speed', 'unknown')
+                    perf = game_data.get('perf', 'unknown')
+                    status = game_data.get('status', 'unknown')
+                    winner = game_data.get('winner')
+                    white_name = white_user.get('name', 'Unknown')
+                    black_name = black_user.get('name', 'Unknown')
+                    white_title = white_user.get('title')
+                    black_title = black_user.get('title')
+                    white_rating = pd.to_numeric(white_info.get('rating'), errors='coerce')
+                    black_rating = pd.to_numeric(black_info.get('rating'), errors='coerce')
+                    player_color, player_elo, opp_name_raw, opp_title_raw, opp_elo = (None, None, 'Unknown', None, None)
+                    if username_lower == white_name.lower():
+                        player_color, player_elo, opp_name_raw, opp_title_raw, opp_elo = ('White', white_rating, black_name, black_title, black_rating)
+                    elif username_lower == black_name.lower():
+                        player_color, player_elo, opp_name_raw, opp_title_raw, opp_elo = ('Black', black_rating, white_name, white_title, white_rating)
+                    else:
+                        continue
                     if player_color is None or pd.isna(player_elo) or pd.isna(opp_elo): continue
-                    res_num,res_str=(0.5,"Draw");
-                    if status not in ['draw','stalemate']:
-                       if winner==player_color.lower(): res_num,res_str=(1,"Win")
-                       elif winner is not None: res_num,res_str=(0,"Loss")
-                    tc_str="Unknown";
-                    if clock_info: init=clock_info.get('initial');incr=clock_info.get('increment');
-                    if init is not None and incr is not None: tc_str=f"{init}+{incr}"
-                    elif speed=='correspondence': tc_str="Correspondence"
-                    eco=opening_info.get('eco','Unknown'); op_name_api=opening_info.get('name','Unknown Opening').replace('?','').split(':')[0].strip()
-                    op_name_custom=eco_map.get(eco, f"ECO: {eco}" if eco!='Unknown' else 'Unknown Opening')
-                    term_map={"mate":"Normal","resign":"Normal","stalemate":"Normal","timeout":"Time forfeit","draw":"Normal","outoftime":"Time forfeit","cheat":"Cheat","noStart":"Aborted","unknownFinish":"Unknown","variantEnd":"Variant End"}
-                    term=term_map.get(status,"Unknown")
-                    opp_title_final='Unknown'
-                    if opp_title_raw and opp_title_raw.strip(): opp_title_clean=opp_title_raw.replace(' ','').strip().upper();
-                    if opp_title_clean and opp_title_clean!='?': opp_title_final=opp_title_clean
-                    def clean_name(n): return re.sub(r'^(GM|IM|FM|WGM|WIM|WFM|CM|WCM|NM)\s+','',n).strip()
-                    opp_name_clean=clean_name(opp_name_raw)
-                    all_games_data.append({'Date':game_date,'Event':perf,'White':white_name,'Black':black_name,'Result':"1-0" if winner=='white' else ("0-1" if winner=='black' else "1/2-1/2"),'WhiteElo':int(white_rating) if not pd.isna(white_rating) else 0,'BlackElo':int(black_rating) if not pd.isna(black_rating) else 0,'ECO':eco,'OpeningName_API':op_name_api,'OpeningName_Custom':op_name_custom,'TimeControl':tc_str,'Termination':term,'PlyCount':game_data.get('turns',0),'LichessID':game_id,'PlayerID':username,'PlayerColor':player_color,'PlayerElo':int(player_elo),'OpponentName':opp_name_clean,'OpponentNameRaw':opp_name_raw,'OpponentElo':int(opp_elo),'OpponentTitle':opp_title_final,'PlayerResultNumeric':res_num,'PlayerResultString':res_str,'Variant':variant,'Speed':speed,'Status':status,'PerfType':perf})
-                except json.JSONDecodeError: error_counter += 1
-                except Exception: error_counter += 1
-    except requests.exceptions.RequestException as e: return pd.DataFrame(), f"🚨 API Error: {e}"
-    except Exception as e: return pd.DataFrame(), f"🚨 Error: {e}\n{traceback.format_exc()}"
-    status_message = f"Processed {len(all_games_data)} games.";
+                    res_num, res_str = (0.5, "Draw")
+                    if status not in ['draw', 'stalemate']:
+                        if winner == player_color.lower(): res_num, res_str = (1, "Win")
+                        elif winner is not None: res_num, res_str = (0, "Loss")
+                    tc_str = "Unknown"
+                    if clock_info:
+                        init = clock_info.get('initial')
+                        incr = clock_info.get('increment')
+                        if init is not None and incr is not None: tc_str = f"{init}+{incr}"
+                        elif speed == 'correspondence': tc_str = "Correspondence"
+                    eco = opening_info.get('eco', 'Unknown')
+                    op_name_api = opening_info.get('name', 'Unknown Opening').replace('?', '').split(':')[0].strip()
+                    op_name_custom = eco_map.get(eco, f"ECO: {eco}" if eco != 'Unknown' else 'Unknown Opening')
+                    term_map = {"mate": "Normal", "resign": "Normal", "stalemate": "Normal", "timeout": "Time forfeit", "draw": "Normal", "outoftime": "Time forfeit", "cheat": "Cheat", "noStart": "Aborted", "unknownFinish": "Unknown", "variantEnd": "Variant End"}
+                    term = term_map.get(status, "Unknown")
+                    opp_title_final = 'Unknown'
+                    if opp_title_raw and opp_title_raw.strip():
+                        opp_title_clean = opp_title_raw.replace(' ', '').strip().upper()
+                        if opp_title_clean and opp_title_clean != '?': opp_title_final = opp_title_clean
+                    def clean_name(n): return re.sub(r'^(GM|IM|FM|WGM|WIM|WFM|CM|WCM|NM)\s+', '', n).strip()
+                    opp_name_clean = clean_name(opp_name_raw)
+                    all_games_data.append({
+                        'Date': game_date, 'Event': perf, 'White': white_name, 'Black': black_name,
+                        'Result': "1-0" if winner == 'white' else ("0-1" if winner == 'black' else "1/2-1/2"),
+                        'WhiteElo': int(white_rating) if not pd.isna(white_rating) else 0,
+                        'BlackElo': int(black_rating) if not pd.isna(black_rating) else 0,
+                        'ECO': eco, 'OpeningName_API': op_name_api, 'OpeningName_Custom': op_name_custom,
+                        'TimeControl': tc_str, 'Termination': term, 'PlyCount': game_data.get('turns', 0),
+                        'LichessID': game_id, 'PlayerID': username, 'PlayerColor': player_color,
+                        'PlayerElo': int(player_elo), 'OpponentName': opp_name_clean, 'OpponentNameRaw': opp_name_raw,
+                        'OpponentElo': int(opp_elo), 'OpponentTitle': opp_title_final, 'PlayerResultNumeric': res_num,
+                        'PlayerResultString': res_str, 'Variant': variant, 'Speed': speed, 'Status': status, 'PerfType': perf
+                    })
+                except json.JSONDecodeError:
+                    error_counter += 1
+                except Exception:
+                    error_counter += 1
+    except requests.exceptions.RequestException as e:
+        return pd.DataFrame(), f"🚨 API Error: {e}"
+    except Exception as e:
+        return pd.DataFrame(), f"🚨 Error: {e}\n{traceback.format_exc()}"
+    status_message = f"Processed {len(all_games_data)} games."
     if error_counter > 0: status_message += f" Skipped {error_counter} errors."
     if not all_games_data: return pd.DataFrame(), f"⚠️ No games found matching criteria."
     if progress: progress(0.8, desc="Finalizing...")
-    df = pd.DataFrame(all_games_data);
+    df = pd.DataFrame(all_games_data)
     if not df.empty:
-        df['Date']=pd.to_datetime(df['Date'],errors='coerce'); df=df.dropna(subset=['Date'])
+        df['Date'] = pd.to_datetime(df['Date'], errors='coerce')
+        df = df.dropna(subset=['Date'])
         if df.empty: return df, "⚠️ No games with valid dates."
-        df['Year']=df['Date'].dt.year; df['Month']=df['Date'].dt.month; df['Day']=df['Date'].dt.day; df['Hour']=df['Date'].dt.hour; df['DayOfWeekNum']=df['Date'].dt.dayofweek; df['DayOfWeekName']=df['Date'].dt.day_name()
-        df['PlayerElo']=df['PlayerElo'].astype(int); df['OpponentElo']=df['OpponentElo'].astype(int)
-        df['EloDiff']=df['PlayerElo']-df['OpponentElo']; df['TimeControl_Category']=df.apply(lambda r: categorize_time_control(r['TimeControl'], r['Speed']), axis=1) # Calls corrected func
-        df=df.sort_values(by='Date').reset_index(drop=True)
+        df['Year'] = df['Date'].dt.year
+        df['Month'] = df['Date'].dt.month
+        df['Day'] = df['Date'].dt.day
+        df['Hour'] = df['Date'].dt.hour
+        df['DayOfWeekNum'] = df['Date'].dt.dayofweek
+        df['DayOfWeekName'] = df['Date'].dt.day_name()
+        df['PlayerElo'] = df['PlayerElo'].astype(int)
+        df['OpponentElo'] = df['OpponentElo'].astype(int)
+        df['EloDiff'] = df['PlayerElo'] - df['OpponentElo']
+        df['TimeControl_Category'] = df.apply(lambda r: categorize_time_control(r['TimeControl'], r['Speed']), axis=1)
+        df = df.sort_values(by='Date').reset_index(drop=True)
     if progress: progress(1, desc="Complete!")
     return df, status_message
 
 # =============================================
-# Plotting Functions (Unchanged)
+# Plotting Functions
 # =============================================
-# (Insert ALL plotting functions here - code identical to previous version v15)
-# ... (plot_win_loss_pie, ..., plot_time_forfeit_by_tc) ...
 def plot_win_loss_pie(df, display_name):
     if 'PlayerResultString' not in df.columns: return go.Figure()
     result_counts = df['PlayerResultString'].value_counts()
-    fig = px.pie(values=result_counts.values, names=result_counts.index, title=f'Overall Results for {display_name}', color=result_counts.index, color_discrete_map={'Win':'#4CAF50', 'Draw':'#B0BEC5', 'Loss':'#F44336'}, hole=0.3)
-    fig.update_traces(textposition='inside', textinfo='percent+label', pull=[0.05 if x == 'Win' else 0 for x in result_counts.index]); fig.update_layout(dragmode=False); return fig
+    fig = px.pie(values=result_counts.values, names=result_counts.index, title=f'Overall Results for {display_name}',
+                 color=result_counts.index, color_discrete_map={'Win': '#4CAF50', 'Draw': '#B0BEC5', 'Loss': '#F44336'}, hole=0.3)
+    fig.update_traces(textposition='inside', textinfo='percent+label', pull=[0.05 if x == 'Win' else 0 for x in result_counts.index])
+    fig.update_layout(dragmode=False)
+    return fig
+
 def plot_win_loss_by_color(df):
     if not all(col in df.columns for col in ['PlayerColor', 'PlayerResultString']): return go.Figure()
-    try: color_results=df.groupby(['PlayerColor','PlayerResultString']).size().unstack(fill_value=0)
-    except KeyError: return go.Figure().update_layout(title="Error: Missing Columns")
-    for res in ['Win','Draw','Loss']: color_results[res]=color_results.get(res,0)
-    color_results=color_results[['Win','Draw','Loss']]; total=color_results.sum(axis=1); color_results_pct=color_results.apply(lambda x:x*100/total[x.name] if total[x.name]>0 else 0,axis=1)
-    fig=px.bar(color_results_pct, barmode='stack', title='Results by Color', labels={'value':'%', 'PlayerColor':'Played As'}, color='PlayerResultString', color_discrete_map={'Win':'#4CAF50', 'Draw':'#B0BEC5', 'Loss':'#F44336'}, text_auto='.1f', category_orders={"PlayerColor":["White","Black"]})
-    fig.update_layout(yaxis_title="Percentage (%)", xaxis_title="Color Played", dragmode=False); fig.update_traces(textangle=0); return fig
+    try:
+        color_results = df.groupby(['PlayerColor', 'PlayerResultString']).size().unstack(fill_value=0)
+    except KeyError:
+        return go.Figure().update_layout(title="Error: Missing Columns")
+    for res in ['Win', 'Draw', 'Loss']: color_results[res] = color_results.get(res, 0)
+    color_results = color_results[['Win', 'Draw', 'Loss']]
+    total = color_results.sum(axis=1)
+    color_results_pct = color_results.apply(lambda x: x * 100 / total[x.name] if total[x.name] > 0 else 0, axis=1)
+    fig = px.bar(color_results_pct, barmode='stack', title='Results by Color', labels={'value': '%', 'PlayerColor': 'Played As'},
+                 color='PlayerResultString', color_discrete_map={'Win': '#4CAF50', 'Draw': '#B0BEC5', 'Loss': '#F44336'},
+                 text_auto='.1f', category_orders={"PlayerColor": ["White", "Black"]})
+    fig.update_layout(yaxis_title="Percentage (%)", xaxis_title="Color Played", dragmode=False)
+    fig.update_traces(textangle=0)
+    return fig
+
 def plot_rating_trend(df, display_name):
     if not all(col in df.columns for col in ['Date', 'PlayerElo']): return go.Figure()
-    df_plot=df.copy(); df_plot['PlayerElo']=pd.to_numeric(df_plot['PlayerElo'],errors='coerce'); df_sorted=df_plot[df_plot['PlayerElo'].notna() & (df_plot['PlayerElo']>0)].sort_values('Date')
+    df_plot = df.copy()
+    df_plot['PlayerElo'] = pd.to_numeric(df_plot['PlayerElo'], errors='coerce')
+    df_sorted = df_plot[df_plot['PlayerElo'].notna() & (df_plot['PlayerElo'] > 0)].sort_values('Date')
     if df_sorted.empty: return go.Figure().update_layout(title=f"No Elo data")
-    fig=go.Figure(); fig.add_trace(go.Scatter(x=df_sorted['Date'], y=df_sorted['PlayerElo'], mode='lines+markers', name='Elo', line=dict(color='#1E88E5',width=2), marker=dict(size=5,opacity=0.7)))
-    fig.update_layout(title=f'{display_name}\'s Rating Trend', xaxis_title='Date', yaxis_title='Elo Rating', hovermode="x unified", xaxis_rangeslider_visible=True, dragmode=False); return fig
+    fig = go.Figure()
+    fig.add_trace(go.Scatter(x=df_sorted['Date'], y=df_sorted['PlayerElo'], mode='lines+markers', name='Elo',
+                            line=dict(color='#1E88E5', width=2), marker=dict(size=5, opacity=0.7)))
+    fig.update_layout(title=f'{display_name}\'s Rating Trend', xaxis_title='Date', yaxis_title='Elo Rating',
+                      hovermode="x unified", xaxis_rangeslider_visible=True, dragmode=False)
+    return fig
+
 def plot_performance_vs_opponent_elo(df):
     if not all(col in df.columns for col in ['PlayerResultString', 'EloDiff']): return go.Figure()
-    fig=px.box(df, x='PlayerResultString', y='EloDiff', title='Elo Advantage vs. Result', labels={'PlayerResultString':'Result', 'EloDiff':'Your Elo - Opponent Elo'}, category_orders={"PlayerResultString":["Win","Draw","Loss"]}, color='PlayerResultString', color_discrete_map={'Win':'#4CAF50','Draw':'#B0BEC5','Loss':'#F44336'}, points='outliers')
-    fig.add_hline(y=0, line_dash="dash", line_color="grey"); fig.update_traces(marker=dict(opacity=0.8)); fig.update_layout(dragmode=False); return fig
+    fig = px.box(df, x='PlayerResultString', y='EloDiff', title='Elo Advantage vs. Result',
+                 labels={'PlayerResultString': 'Result', 'EloDiff': 'Your Elo - Opponent Elo'},
+                 category_orders={"PlayerResultString": ["Win", "Draw", "Loss"]}, color='PlayerResultString',
+                 color_discrete_map={'Win': '#4CAF50', 'Draw': '#B0BEC5', 'Loss': '#F44336'}, points='outliers')
+    fig.add_hline(y=0, line_dash="dash", line_color="grey")
+    fig.update_traces(marker=dict(opacity=0.8))
+    fig.update_layout(dragmode=False)
+    return fig
+
 def plot_games_by_dow(df):
     if 'DayOfWeekName' not in df.columns: return go.Figure()
-    dow_order=["Monday","Tuesday","Wednesday","Thursday","Friday","Saturday","Sunday"]
-    games_by_dow=df['DayOfWeekName'].value_counts().reindex(dow_order, fill_value=0)
-    fig=px.bar(games_by_dow, x=games_by_dow.index, y=games_by_dow.values, title="Games by Day of Week", labels={'x':'Day','y':'Games'}, text=games_by_dow.values)
-    fig.update_traces(marker_color='#9C27B0', textposition='outside'); fig.update_layout(dragmode=False); return fig
+    dow_order = ["Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday"]
+    games_by_dow = df['DayOfWeekName'].value_counts().reindex(dow_order, fill_value=0)
+    fig = px.bar(games_by_dow, x=games_by_dow.index, y=games_by_dow.values, title="Games by Day of Week",
+                 labels={'x': 'Day', 'y': 'Games'}, text=games_by_dow.values)
+    fig.update_traces(marker_color='#9C27B0', textposition='outside')
+    fig.update_layout(dragmode=False)
+    return fig
+
 def plot_winrate_by_dow(df):
     if not all(col in df.columns for col in ['DayOfWeekName', 'PlayerResultNumeric']): return go.Figure()
-    dow_order=["Monday","Tuesday","Wednesday","Thursday","Friday","Saturday","Sunday"]
-    wins_by_dow=df[df['PlayerResultNumeric']==1].groupby('DayOfWeekName').size(); total_by_dow=df.groupby('DayOfWeekName').size()
-    win_rate=(wins_by_dow.reindex(total_by_dow.index,fill_value=0)/total_by_dow).fillna(0)*100
-    win_rate=win_rate.reindex(dow_order,fill_value=0)
-    fig=px.bar(win_rate, x=win_rate.index, y=win_rate.values, title="Win Rate (%) by Day", labels={'x':'Day','y':'Win Rate (%)'}, text=win_rate.values)
-    fig.update_traces(marker_color='#FF9800', texttemplate='%{text:.1f}%', textposition='outside'); fig.update_layout(yaxis_range=[0,100], dragmode=False); return fig
+    dow_order = ["Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday"]
+    wins_by_dow = df[df['PlayerResultNumeric'] == 1].groupby('DayOfWeekName').size()
+    total_by_dow = df.groupby('DayOfWeekName').size()
+    win_rate = (wins_by_dow.reindex(total_by_dow.index, fill_value=0) / total_by_dow).fillna(0) * 100
+    win_rate = win_rate.reindex(dow_order, fill_value=0)
+    fig = px.bar(win_rate, x=win_rate.index, y=win_rate.values, title="Win Rate (%) by Day",
+                 labels={'x': 'Day', 'y': 'Win Rate (%)'}, text=win_rate.values)
+    fig.update_traces(marker_color='#FF9800', texttemplate='%{text:.1f}%', textposition='outside')
+    fig.update_layout(yaxis_range=[0, 100], dragmode=False)
+    return fig
+
 def plot_games_by_hour(df):
     if 'Hour' not in df.columns: return go.Figure()
-    games_by_hour=df['Hour'].value_counts().sort_index().reindex(range(24),fill_value=0)
-    fig=px.bar(games_by_hour, x=games_by_hour.index, y=games_by_hour.values, title="Games by Hour (UTC)", labels={'x':'Hour','y':'Games'}, text=games_by_hour.values)
-    fig.update_traces(marker_color='#03A9F4', textposition='outside'); fig.update_layout(xaxis=dict(tickmode='linear'), dragmode=False); return fig
+    games_by_hour = df['Hour'].value_counts().sort_index().reindex(range(24), fill_value=0)
+    fig = px.bar(games_by_hour, x=games_by_hour.index, y=games_by_hour.values, title="Games by Hour (UTC)",
+                 labels={'x': 'Hour', 'y': 'Games'}, text=games_by_hour.values)
+    fig.update_traces(marker_color='#03A9F4', textposition='outside')
+    fig.update_layout(xaxis=dict(tickmode='linear'), dragmode=False)
+    return fig
+
 def plot_winrate_by_hour(df):
     if not all(col in df.columns for col in ['Hour', 'PlayerResultNumeric']): return go.Figure()
-    wins_by_hour=df[df['PlayerResultNumeric']==1].groupby('Hour').size(); total_by_hour=df.groupby('Hour').size()
-    win_rate=(wins_by_hour.reindex(total_by_hour.index,fill_value=0)/total_by_hour).fillna(0)*100
-    win_rate=win_rate.reindex(range(24),fill_value=0)
-    fig=px.line(win_rate, x=win_rate.index, y=win_rate.values, markers=True, title="Win Rate (%) by Hour (UTC)", labels={'x':'Hour','y':'Win Rate (%)'})
-    fig.update_traces(line_color='#8BC34A'); fig.update_layout(yaxis_range=[0,100], xaxis=dict(tickmode='linear'), dragmode=False); return fig
+    wins_by_hour = df[df['PlayerResultNumeric'] == 1].groupby('Hour').size()
+    total_by_hour = df.groupby('Hour').size()
+    win_rate = (wins_by_hour.reindex(total_by_hour.index, fill_value=0) / total_by_hour).fillna(0) * 100
+    win_rate = win_rate.reindex(range(24), fill_value=0)
+    fig = px.line(win_rate, x=win_rate.index, y=win_rate.values, markers=True, title="Win Rate (%) by Hour (UTC)",
+                  labels={'x': 'Hour', 'y': 'Win Rate (%)'})
+    fig.update_traces(line_color='#8BC34A')
+    fig.update_layout(yaxis_range=[0, 100], xaxis=dict(tickmode='linear'), dragmode=False)
+    return fig
+
 def plot_games_per_year(df):
     if 'Year' not in df.columns: return go.Figure()
     games_per_year = df['Year'].value_counts().sort_index()
-    fig = px.bar(games_per_year, x=games_per_year.index, y=games_per_year.values, title='Games Per Year', labels={'x':'Year','y':'Games'}, text=games_per_year.values)
-    fig.update_traces(marker_color='#2196F3', textposition='outside'); fig.update_layout(xaxis_title="Year", yaxis_title="Number of Games", xaxis={'type':'category'}, dragmode=False); return fig
+    fig = px.bar(games_per_year, x=games_per_year.index, y=games_per_year.values, title='Games Per Year',
+                 labels={'x': 'Year', 'y': 'Games'}, text=games_per_year.values)
+    fig.update_traces(marker_color='#2196F3', textposition='outside')
+    fig.update_layout(xaxis_title="Year", yaxis_title="Number of Games", xaxis={'type': 'category'}, dragmode=False)
+    return fig
+
 def plot_win_rate_per_year(df):
     if not all(col in df.columns for col in ['Year', 'PlayerResultNumeric']): return go.Figure()
-    wins_per_year=df[df['PlayerResultNumeric']==1].groupby('Year').size(); total_per_year=df.groupby('Year').size()
-    win_rate=(wins_per_year.reindex(total_per_year.index,fill_value=0)/total_per_year).fillna(0)*100
-    win_rate.index=win_rate.index.astype(str)
-    fig=px.line(win_rate, x=win_rate.index, y=win_rate.values, title='Win Rate (%) Per Year', markers=True, labels={'x':'Year','y':'Win Rate (%)'})
-    fig.update_traces(line_color='#FFC107', line_width=2.5); fig.update_layout(yaxis_range=[0,100], dragmode=False); return fig
+    wins_per_year = df[df['PlayerResultNumeric'] == 1].groupby('Year').size()
+    total_per_year = df.groupby('Year').size()
+    win_rate = (wins_per_year.reindex(total_per_year.index, fill_value=0) / total_per_year).fillna(0) * 100
+    win_rate.index = win_rate.index.astype(str)
+    fig = px.line(win_rate, x=win_rate.index, y=win_rate.values, title='Win Rate (%) Per Year', markers=True,
+                  labels={'x': 'Year', 'y': 'Win Rate (%)'})
+    fig.update_traces(line_color='#FFC107', line_width=2.5)
+    fig.update_layout(yaxis_range=[0, 100], dragmode=False)
+    return fig
+
 def plot_performance_by_time_control(df):
-     if not all(col in df.columns for col in ['TimeControl_Category', 'PlayerResultString']): return go.Figure()
-     try:
-        tc_results=df.groupby(['TimeControl_Category','PlayerResultString']).size().unstack(fill_value=0)
-        for res in ['Win','Draw','Loss']: tc_results[res]=tc_results.get(res,0)
-        tc_results=tc_results[['Win','Draw','Loss']]; total=tc_results.sum(axis=1)
-        tc_results_pct=tc_results.apply(lambda x:x*100/total[x.name] if total[x.name]>0 else 0, axis=1)
-        found=df['TimeControl_Category'].unique(); pref=['Bullet','Blitz','Rapid','Classical','Correspondence','Unknown']
-        order=[c for c in pref if c in found]+[c for c in found if c not in pref]
-        tc_results_pct=tc_results_pct.reindex(index=order).dropna(axis=0,how='all')
-        fig=px.bar(tc_results_pct, title='Performance by Time Control', labels={'value':'%','TimeControl_Category':'Category'}, color='PlayerResultString', color_discrete_map={'Win':'#4CAF50','Draw':'#B0BEC5','Loss':'#F44336'}, barmode='group', text_auto='.1f')
-        fig.update_layout(xaxis_title="Time Control Category", yaxis_title="Percentage (%)", dragmode=False); fig.update_traces(textangle=0); return fig
-     except Exception: return go.Figure().update_layout(title="Error")
+    if not all(col in df.columns for col in ['TimeControl_Category', 'PlayerResultString']): return go.Figure()
+    try:
+        tc_results = df.groupby(['TimeControl_Category', 'PlayerResultString']).size().unstack(fill_value=0)
+        for res in ['Win', 'Draw', 'Loss']: tc_results[res] = tc_results.get(res, 0)
+        tc_results = tc_results[['Win', 'Draw', 'Loss']]
+        total = tc_results.sum(axis=1)
+        tc_results_pct = tc_results.apply(lambda x: x * 100 / total[x.name] if total[x.name] > 0 else 0, axis=1)
+        found = df['TimeControl_Category'].unique()
+        pref = ['Bullet', 'Blitz', 'Rapid', 'Classical', 'Correspondence', 'Unknown']
+        order = [c for c in pref if c in found] + [c for c in found if c not in pref]
+        tc_results_pct = tc_results_pct.reindex(index=order).dropna(axis=0, how='all')
+        fig = px.bar(tc_results_pct, title='Performance by Time Control', labels={'value': '%', 'TimeControl_Category': 'Category'},
+                     color='PlayerResultString', color_discrete_map={'Win': '#4CAF50', 'Draw': '#B0BEC5', 'Loss': '#F44336'},
+                     barmode='group', text_auto='.1f')
+        fig.update_layout(xaxis_title="Time Control Category", yaxis_title="Percentage (%)", dragmode=False)
+        fig.update_traces(textangle=0)
+        return fig
+    except Exception:
+        return go.Figure().update_layout(title="Error")
+
 def plot_opening_frequency(df, top_n=20, opening_col='OpeningName_API'):
     if opening_col not in df.columns: return go.Figure()
     source_label = "Lichess API" if opening_col == 'OpeningName_API' else "Custom Mapping"
     opening_counts = df[df[opening_col] != 'Unknown Opening'][opening_col].value_counts().nlargest(top_n)
-    fig = px.bar(opening_counts, y=opening_counts.index, x=opening_counts.values, orientation='h', title=f'Top {top_n} Openings ({source_label})', labels={'y':'Opening','x':'Games'}, text=opening_counts.values)
-    fig.update_layout(yaxis={'categoryorder':'total ascending'}, dragmode=False); fig.update_traces(marker_color='#673AB7', textposition='outside'); return fig
+    fig = px.bar(opening_counts, y=opening_counts.index, x=opening_counts.values, orientation='h',
+                 title=f'Top {top_n} Openings ({source_label})', labels={'y': 'Opening', 'x': 'Games'}, text=opening_counts.values)
+    fig.update_layout(yaxis={'categoryorder': 'total ascending'}, dragmode=False)
+    fig.update_traces(marker_color='#673AB7', textposition='outside')
+    return fig
+
 def plot_win_rate_by_opening(df, min_games=5, top_n=20, opening_col='OpeningName_API'):
     if not all(col in df.columns for col in [opening_col, 'PlayerResultNumeric']): return go.Figure()
     source_label = "Lichess API" if opening_col == 'OpeningName_API' else "Custom Mapping"
-    opening_stats=df.groupby(opening_col).agg(total_games=('PlayerResultNumeric','count'), wins=('PlayerResultNumeric',lambda x:(x==1).sum()))
-    opening_stats=opening_stats[(opening_stats['total_games']>=min_games)&(opening_stats.index!='Unknown Opening')].copy()
+    opening_stats = df.groupby(opening_col).agg(total_games=('PlayerResultNumeric', 'count'),
+                                                wins=('PlayerResultNumeric', lambda x: (x == 1).sum()))
+    opening_stats = opening_stats[(opening_stats['total_games'] >= min_games) & (opening_stats.index != 'Unknown Opening')].copy()
     if opening_stats.empty: return go.Figure().update_layout(title=f"No openings >= {min_games} games ({source_label})")
-    opening_stats['win_rate']=(opening_stats['wins']/opening_stats['total_games'])*100
-    opening_stats_plot=opening_stats.nlargest(top_n, 'win_rate')
-    fig=px.bar(opening_stats_plot, y=opening_stats_plot.index, x='win_rate', orientation='h', title=f'Top {top_n} Openings by Win Rate (Min {min_games} games, {source_label})', labels={'win_rate':'Win Rate (%)',opening_col:'Opening'}, text='win_rate')
-    fig.update_traces(texttemplate='%{text:.1f}%', textposition='inside', marker_color='#009688'); fig.update_layout(yaxis={'categoryorder':'total ascending'}, xaxis_title="Win Rate (%)", dragmode=False); return fig
+    opening_stats['win_rate'] = (opening_stats['wins'] / opening_stats['total_games']) * 100
+    opening_stats_plot = opening_stats.nlargest(top_n, 'win_rate')
+    fig = px.bar(opening_stats_plot, y=opening_stats_plot.index, x='win_rate', orientation='h',
+                 title=f'Top {top_n} Openings by Win Rate (Min {min_games} games, {source_label})',
+                 labels={'win_rate': 'Win Rate (%)', opening_col: 'Opening'}, text='win_rate')
+    fig.update_traces(texttemplate='%{text:.1f}%', textposition='inside', marker_color='#009688')
+    fig.update_layout(yaxis={'categoryorder': 'total ascending'}, xaxis_title="Win Rate (%)", dragmode=False)
+    return fig
+
 def plot_most_frequent_opponents(df, top_n=20):
     if 'OpponentName' not in df.columns: return go.Figure()
-    opp_counts=df[df['OpponentName']!='Unknown']['OpponentName'].value_counts().nlargest(top_n)
-    fig=px.bar(opp_counts, y=opp_counts.index, x=opp_counts.values, orientation='h', title=f'Top {top_n} Opponents', labels={'y':'Opponent','x':'Games'}, text=opp_counts.values)
-    fig.update_layout(yaxis={'categoryorder':'total ascending'}, dragmode=False); fig.update_traces(marker_color='#FF5722', textposition='outside'); return fig
+    opp_counts = df[df['OpponentName'] != 'Unknown']['OpponentName'].value_counts().nlargest(top_n)
+    fig = px.bar(opp_counts, y=opp_counts.index, x=opp_counts.values, orientation='h',
+                 title=f'Top {top_n} Opponents', labels={'y': 'Opponent', 'x': 'Games'}, text=opp_counts.values)
+    fig.update_layout(yaxis={'categoryorder': 'total ascending'}, dragmode=False)
+    fig.update_traces(marker_color='#FF5722', textposition='outside')
+    return fig
+
 def plot_games_by_dom(df):
     if 'Day' not in df.columns: return go.Figure()
     games_by_dom = df['Day'].value_counts().sort_index().reindex(range(1, 32), fill_value=0)
-    fig = px.bar(games_by_dom, x=games_by_dom.index, y=games_by_dom.values, title="Games Played per Day of Month", labels={'x': 'Day of Month', 'y': 'Number of Games'}, text=games_by_dom.values)
-    fig.update_traces(marker_color='#E91E63', textposition='outside'); fig.update_layout(xaxis=dict(tickmode='linear'), dragmode=False); return fig
+    fig = px.bar(games_by_dom, x=games_by_dom.index, y=games_by_dom.values, title="Games Played per Day of Month",
+                 labels={'x': 'Day of Month', 'y': 'Number of Games'}, text=games_by_dom.values)
+    fig.update_traces(marker_color='#E91E63', textposition='outside')
+    fig.update_layout(xaxis=dict(tickmode='linear'), dragmode=False)
+    return fig
+
 def plot_winrate_by_dom(df):
     if not all(col in df.columns for col in ['Day', 'PlayerResultNumeric']): return go.Figure()
-    wins_by_dom=df[df['PlayerResultNumeric']==1].groupby('Day').size(); total_by_dom=df.groupby('Day').size()
-    win_rate=(wins_by_dom.reindex(total_by_dom.index,fill_value=0)/total_by_dom).fillna(0)*100
-    win_rate=win_rate.reindex(range(1,32),fill_value=0)
-    fig=px.line(win_rate, x=win_rate.index, y=win_rate.values, markers=True, title="Win Rate (%) per Day of Month", labels={'x': 'Day of Month', 'y': 'Win Rate (%)'})
-    fig.update_traces(line_color='#FF5722'); fig.update_layout(yaxis_range=[0,100], xaxis=dict(tickmode='linear'), dragmode=False); return fig
+    wins_by_dom = df[df['PlayerResultNumeric'] == 1].groupby('Day').size()
+    total_by_dom = df.groupby('Day').size()
+    win_rate = (wins_by_dom.reindex(total_by_dom.index, fill_value=0) / total_by_dom).fillna(0) * 100
+    win_rate = win_rate.reindex(range(1, 32), fill_value=0)
+    fig = px.line(win_rate, x=win_rate.index, y=win_rate.values, markers=True, title="Win Rate (%) per Day of Month",
+                  labels={'x': 'Day of Month', 'y': 'Win Rate (%)'})
+    fig.update_traces(line_color='#FF5722')
+    fig.update_layout(yaxis_range=[0, 100], xaxis=dict(tickmode='linear'), dragmode=False)
+    return fig
+
 def plot_time_forfeit_summary(wins_tf, losses_tf):
-    data={'Outcome':['Won on Time','Lost on Time'],'Count':[wins_tf,losses_tf]}
-    df_tf=pd.DataFrame(data)
-    fig=px.bar(df_tf,x='Outcome',y='Count',title="Time Forfeit Summary", color='Outcome', color_discrete_map={'Won on Time':'#4CAF50','Lost on Time':'#F44336'}, text='Count')
-    fig.update_layout(showlegend=False, dragmode=False); fig.update_traces(textposition='outside'); return fig
+    data = {'Outcome': ['Won on Time', 'Lost on Time'], 'Count': [wins_tf, losses_tf]}
+    df_tf = pd.DataFrame(data)
+    fig = px.bar(df_tf, x='Outcome', y='Count', title="Time Forfeit Summary", color='Outcome',
+                 color_discrete_map={'Won on Time': '#4CAF50', 'Lost on Time': '#F44336'}, text='Count')
+    fig.update_layout(showlegend=False, dragmode=False)
+    fig.update_traces(textposition='outside')
+    return fig
+
 def plot_time_forfeit_by_tc(tf_games_df):
     if 'TimeControl_Category' not in tf_games_df.columns or tf_games_df.empty: return go.Figure().update_layout(title="No TF Data by Category")
-    tf_by_tc=tf_games_df['TimeControl_Category'].value_counts()
-    fig=px.bar(tf_by_tc,x=tf_by_tc.index,y=tf_by_tc.values, title="Time Forfeits by Time Control", labels={'x':'Category','y':'Forfeits'}, text=tf_by_tc.values)
-    fig.update_layout(dragmode=False); fig.update_traces(marker_color='#795548', textposition='outside'); return fig
+    tf_by_tc = tf_games_df['TimeControl_Category'].value_counts()
+    fig = px.bar(tf_by_tc, x=tf_by_tc.index, y=tf_by_tc.values, title="Time Forfeits by Time Control",
+                 labels={'x': 'Category', 'y': 'Forfeits'}, text=tf_by_tc.values)
+    fig.update_layout(dragmode=False)
+    fig.update_traces(marker_color='#795548', textposition='outside')
+    return fig
 
 # =============================================
 # Helper Functions
 # =============================================
-# ... (Functions identical to v15) ...
 def filter_and_analyze_titled(df, titles):
     if 'OpponentTitle' not in df.columns: return pd.DataFrame()
-    titled_games = df[df['OpponentTitle'].isin(titles)].copy(); return titled_games
+    titled_games = df[df['OpponentTitle'].isin(titles)].copy()
+    return titled_games
+
 def filter_and_analyze_time_forfeits(df):
     if 'Termination' not in df.columns: return pd.DataFrame(), 0, 0
     tf_games = df[df['Termination'].str.contains("Time forfeit", na=False, case=False)].copy()
@@ -314,30 +451,52 @@ def filter_and_analyze_time_forfeits(df):
     return tf_games, wins_tf, losses_tf
 
 # =============================================
-# Gradio Main Analysis Function (Unchanged)
+# Gradio Main Analysis Function
 # =============================================
-# ... (Function identical to v15) ...
 def perform_full_analysis(username, time_period_key, perf_type, selected_titles_list, progress=gr.Progress(track_tqdm=True)):
     df, status_msg = load_from_lichess_api(username, time_period_key, perf_type, DEFAULT_RATED_ONLY, ECO_MAPPING, progress)
-    num_outputs = 30 # Define the total number of expected output components
+    num_outputs = 30
     if not isinstance(df, pd.DataFrame) or df.empty:
-        return status_msg, pd.DataFrame(), *( [None] * (num_outputs - 2) ) # Return None for plot/df components
+        return status_msg, pd.DataFrame(), *([None] * (num_outputs - 2))
     try:
-        fig_pie=plot_win_loss_pie(df,username); fig_color=plot_win_loss_by_color(df); fig_rating=plot_rating_trend(df,username); fig_elo_diff=plot_performance_vs_opponent_elo(df)
-        total_g=len(df); w=len(df[df['PlayerResultNumeric']==1]); l=len(df[df['PlayerResultNumeric']==0]); d=len(df[df['PlayerResultNumeric']==0.5])
-        wr=(w/total_g*100) if total_g>0 else 0; avg_opp=df['OpponentElo'].mean(); overview_stats_md=f"**Total:** {total_g:,} | **WR:** {wr:.1f}% | **W/L/D:** {w}/{l}/{d} | **Avg Opp:** {avg_opp:.0f if not pd.isna(avg_opp) else 'N/A'}"
-        fig_games_yr=plot_games_per_year(df); fig_wr_yr=plot_win_rate_per_year(df); fig_perf_tc=plot_performance_by_time_control(df)
-        fig_games_dow=plot_games_by_dow(df); fig_wr_dow=plot_winrate_by_dow(df); fig_games_hod=plot_games_by_hour(df); fig_wr_hod=plot_winrate_by_hour(df)
-        fig_games_dom=plot_games_by_dom(df); fig_wr_dom=plot_winrate_by_dom(df)
-        fig_open_freq_api=plot_opening_frequency(df,top_n=15,opening_col='OpeningName_API'); fig_open_wr_api=plot_win_rate_by_opening(df,min_games=5,top_n=15,opening_col='OpeningName_API')
-        fig_open_freq_cust=plot_opening_frequency(df,top_n=15,opening_col='OpeningName_Custom') if ECO_MAPPING else go.Figure().update_layout(title="Custom Map Unavailable"); fig_open_wr_cust=plot_win_rate_by_opening(df,min_games=5,top_n=15,opening_col='OpeningName_Custom') if ECO_MAPPING else go.Figure().update_layout(title="Custom Map Unavailable")
-        fig_opp_freq=plot_most_frequent_opponents(df,top_n=20); df_opp_list=df[df['OpponentName']!='Unknown']['OpponentName'].value_counts().reset_index(name='Games').head(20) if 'OpponentName' in df else pd.DataFrame(); fig_opp_elo=plot_performance_vs_opponent_elo(df)
-        tf_games,wins_tf,losses_tf=filter_and_analyze_time_forfeits(df)
-        fig_tf_summary=plot_time_forfeit_summary(wins_tf,losses_tf) if not tf_games.empty else go.Figure().update_layout(title="No Time Forfeit Data"); fig_tf_tc=plot_time_forfeit_by_tc(tf_games) if not tf_games.empty else go.Figure().update_layout(title="No TF Data by Category")
-        df_tf_list=tf_games[['Date','OpponentName','PlayerColor','PlayerResultString','TimeControl','PlyCount','Termination']].sort_values('Date',ascending=False).head(20) if not tf_games.empty else pd.DataFrame()
-        term_counts=df['Termination'].value_counts(); fig_term_all=px.bar(term_counts,x=term_counts.index,y=term_counts.values,title="Overall Termination Reasons",labels={'x':'Reason','y':'Count'},text=term_counts.values)
-        fig_term_all.update_layout(dragmode=False); fig_term_all.update_traces(textposition='outside')
-        titled_status_msg = ""; fig_titled_pie, fig_titled_color, fig_titled_rating, df_titled_h2h = go.Figure(), go.Figure(), go.Figure(), pd.DataFrame()
+        fig_pie = plot_win_loss_pie(df, username)
+        fig_color = plot_win_loss_by_color(df)
+        fig_rating = plot_rating_trend(df, username)
+        fig_elo_diff = plot_performance_vs_opponent_elo(df)
+        total_g = len(df)
+        w = len(df[df['PlayerResultNumeric'] == 1])
+        l = len(df[df['PlayerResultNumeric'] == 0])
+        d = len(df[df['PlayerResultNumeric'] == 0.5])
+        wr = (w / total_g * 100) if total_g > 0 else 0
+        avg_opp = df['OpponentElo'].mean()
+        overview_stats_md = f"**Total:** {total_g:,} | **WR:** {wr:.1f}% | **W/L/D:** {w}/{l}/{d} | **Avg Opp:** {avg_opp:.0f if not pd.isna(avg_opp) else 'N/A'}"
+        fig_games_yr = plot_games_per_year(df)
+        fig_wr_yr = plot_win_rate_per_year(df)
+        fig_perf_tc = plot_performance_by_time_control(df)
+        fig_games_dow = plot_games_by_dow(df)
+        fig_wr_dow = plot_winrate_by_dow(df)
+        fig_games_hod = plot_games_by_hour(df)
+        fig_wr_hod = plot_winrate_by_hour(df)
+        fig_games_dom = plot_games_by_dom(df)
+        fig_wr_dom = plot_winrate_by_dom(df)
+        fig_open_freq_api = plot_opening_frequency(df, top_n=15, opening_col='OpeningName_API')
+        fig_open_wr_api = plot_win_rate_by_opening(df, min_games=5, top_n=15, opening_col='OpeningName_API')
+        fig_open_freq_cust = plot_opening_frequency(df, top_n=15, opening_col='OpeningName_Custom') if ECO_MAPPING else go.Figure().update_layout(title="Custom Map Unavailable")
+        fig_open_wr_cust = plot_win_rate_by_opening(df, min_games=5, top_n=15, opening_col='OpeningName_Custom') if ECO_MAPPING else go.Figure().update_layout(title="Custom Map Unavailable")
+        fig_opp_freq = plot_most_frequent_opponents(df, top_n=20)
+        df_opp_list = df[df['OpponentName'] != 'Unknown']['OpponentName'].value_counts().reset_index(name='Games').head(20) if 'OpponentName' in df else pd.DataFrame()
+        fig_opp_elo = plot_performance_vs_opponent_elo(df)
+        tf_games, wins_tf, losses_tf = filter_and_analyze_time_forfeits(df)
+        fig_tf_summary = plot_time_forfeit_summary(wins_tf, losses_tf) if not tf_games.empty else go.Figure().update_layout(title="No Time Forfeit Data")
+        fig_tf_tc = plot_time_forfeit_by_tc(tf_games) if not tf_games.empty else go.Figure().update_layout(title="No TF Data by Category")
+        df_tf_list = tf_games[['Date', 'OpponentName', 'PlayerColor', 'PlayerResultString', 'TimeControl', 'PlyCount', 'Termination']].sort_values('Date', ascending=False).head(20) if not tf_games.empty else pd.DataFrame()
+        term_counts = df['Termination'].value_counts()
+        fig_term_all = px.bar(term_counts, x=term_counts.index, y=term_counts.values, title="Overall Termination Reasons",
+                              labels={'x': 'Reason', 'y': 'Count'}, text=term_counts.values)
+        fig_term_all.update_layout(dragmode=False)
+        fig_term_all.update_traces(textposition='outside')
+        titled_status_msg = ""
+        fig_titled_pie, fig_titled_color, fig_titled_rating, df_titled_h2h = go.Figure(), go.Figure(), go.Figure(), pd.DataFrame()
         if selected_titles_list:
             titled_games = filter_and_analyze_titled(df, selected_titles_list)
             if not titled_games.empty:
@@ -346,46 +505,110 @@ def perform_full_analysis(username, time_period_key, perf_type, selected_titles_
                 fig_titled_color = plot_win_loss_by_color(titled_games)
                 fig_titled_rating = plot_rating_trend(titled_games, f"{username} (vs Titles)")
                 h2h = titled_games.groupby('OpponentNameRaw')['PlayerResultString'].value_counts().unstack(fill_value=0)
-                for res in ['Win','Loss','Draw']: h2h[res]=h2h.get(res,0)
-                h2h = h2h[['Win','Loss','Draw']]; h2h['Total']=h2h.sum(axis=1); h2h['Score']=h2h['Win']+0.5*h2h['Draw']
+                for res in ['Win', 'Loss', 'Draw']: h2h[res] = h2h.get(res, 0)
+                h2h = h2h[['Win', 'Loss', 'Draw']]
+                h2h['Total'] = h2h.sum(axis=1)
+                h2h['Score'] = h2h['Win'] + 0.5 * h2h['Draw']
                 df_titled_h2h = h2h.sort_values('Total', ascending=False).reset_index()
-            else: titled_status_msg = f"ℹ️ No games found vs selected titles ({', '.join(selected_titles_list)})."
-        else: titled_status_msg = "ℹ️ Select titles from the sidebar to analyze."
-        return ( status_msg, df, fig_pie, overview_stats_md, fig_color, fig_rating, fig_elo_diff, fig_games_yr, fig_wr_yr, "(Results by color shown in Overview)", fig_games_dow, fig_wr_dow, fig_games_hod, fig_wr_hod, fig_games_dom, fig_wr_dom, fig_perf_tc, fig_open_freq_api, fig_open_wr_api, fig_open_freq_cust, fig_open_wr_cust, fig_opp_freq, df_opp_list, fig_opp_elo, titled_status_msg, fig_titled_pie, fig_titled_color, fig_titled_rating, df_titled_h2h, fig_tf_summary, fig_tf_tc, df_tf_list, fig_term_all )
-    except Exception as e: error_msg = f"🚨 Error generating results: {e}\n{traceback.format_exc()}"; return error_msg, pd.DataFrame(), *( [None] * num_outputs )
+            else:
+                titled_status_msg = f"ℹ️ No games found vs selected titles ({', '.join(selected_titles_list)})."
+        else:
+            titled_status_msg = "ℹ️ Select titles from the sidebar to analyze."
+        return (status_msg, df, fig_pie, overview_stats_md, fig_color, fig_rating, fig_elo_diff, fig_games_yr, fig_wr_yr,
+                "(Results by color shown in Overview)", fig_games_dow, fig_wr_dow, fig_games_hod, fig_wr_hod, fig_games_dom,
+                fig_wr_dom, fig_perf_tc, fig_open_freq_api, fig_open_wr_api, fig_open_freq_cust, fig_open_wr_cust,
+                fig_opp_freq, df_opp_list, fig_opp_elo, titled_status_msg, fig_titled_pie, fig_titled_color, fig_titled_rating,
+                df_titled_h2h, fig_tf_summary, fig_tf_tc, df_tf_list, fig_term_all)
+    except Exception as e:
+        error_msg = f"🚨 Error generating results: {e}\n{traceback.format_exc()}"
+        return error_msg, pd.DataFrame(), *([None] * num_outputs)
 
 # =============================================
-# Gradio Interface Definition (Unchanged UI Structure)
+# Gradio Interface Definition
 # =============================================
-# ... (Code identical to version 15) ...
 css = """.gradio-container { font-family: 'IBM Plex Sans', sans-serif; } footer { display: none !important; }"""
 with gr.Blocks(theme=gr.themes.Soft(), css=css) as demo:
     gr.Markdown("# ♟️ Lichess Insights\nAnalyze rated game statistics from Lichess API.")
     df_state = gr.State(pd.DataFrame())
     with gr.Row():
-        with gr.Column(scale=1, min_width=250): # Sidebar
-            gr.Markdown("## ⚙️ Settings"); username_input=gr.Textbox(label="Lichess Username", placeholder="e.g., DrNykterstein", elem_id="username_box"); time_period_input=gr.Dropdown(label="Time Period", choices=list(TIME_PERIOD_OPTIONS.keys()), value=DEFAULT_TIME_PERIOD); perf_type_input=gr.Dropdown(label="Game Type", choices=PERF_TYPE_OPTIONS_SINGLE, value=DEFAULT_PERF_TYPE); analyze_btn=gr.Button("Analyze Games", variant="primary"); status_output=gr.Markdown(""); gr.Markdown("---"); gr.Markdown("### Analyze vs Titled Players"); titled_player_select=gr.CheckboxGroup(label="Select Opponent Titles", choices=TITLES_TO_ANALYZE, value=['GM', 'IM'], elem_id="titled_select"); gr.Markdown("*(Analysis updates on 'Analyze Games' click)*");
-        with gr.Column(scale=4): # Main Content
-            overview_plot_pie=gr.Plot(label="Overall Results"); overview_stats_md_out=gr.Markdown(); overview_plot_color=gr.Plot(label="Results by Color"); overview_plot_rating=gr.Plot(label="Rating Trend"); overview_plot_elo_diff=gr.Plot(label="Elo Advantage vs. Result")
-            time_plot_games_yr=gr.Plot(label="Games per Year"); time_plot_wr_yr=gr.Plot(label="Win Rate per Year")
-            color_plot_placeholder=gr.Markdown()
-            time_plot_games_dow=gr.Plot(label="Games by Day of Week"); time_plot_wr_dow=gr.Plot(label="Win Rate by Day of Week"); time_plot_games_hod=gr.Plot(label="Games by Hour (UTC)"); time_plot_wr_hod=gr.Plot(label="Win Rate by Hour (UTC)"); time_plot_games_dom=gr.Plot(label="Games by Day of Month"); time_plot_wr_dom=gr.Plot(label="Win Rate by Day of Month"); time_plot_perf_tc=gr.Plot(label="Performance by Time Control")
-            eco_plot_freq_api=gr.Plot(label="Opening Frequency (API)"); eco_plot_wr_api=gr.Plot(label="Opening Win Rate (API)"); eco_plot_freq_cust=gr.Plot(label="Opening Frequency (Custom)"); eco_plot_wr_cust=gr.Plot(label="Opening Win Rate (Custom)")
-            opp_plot_freq=gr.Plot(label="Frequent Opponents"); opp_df_list=gr.DataFrame(label="Top Opponents List", wrap=True); opp_plot_elo=gr.Plot(label="Elo Advantage vs Result")
-            titled_status=gr.Markdown(); titled_plot_pie=gr.Plot(label="Results vs Selected Titles"); titled_plot_color=gr.Plot(label="Results by Color vs Selected Titles"); titled_plot_rating=gr.Plot(label="Rating Trend vs Selected Titles"); titled_df_h2h_comp=gr.DataFrame(label="Head-to-Head vs Selected Titles", wrap=True); # Component name
-            term_plot_tf_summary=gr.Plot(label="Time Forfeit Summary"); term_plot_tf_tc=gr.Plot(label="Time Forfeits by Time Control"); term_df_tf_list=gr.DataFrame(label="Recent TF Games", wrap=True); term_plot_all=gr.Plot(label="Overall Termination")
+        with gr.Column(scale=1, min_width=250):  # Sidebar
+            gr.Markdown("## ⚙️ Settings")
+            username_input = gr.Textbox(label="Lichess Username", placeholder="e.g., DrNykterstein", elem_id="username_box")
+            time_period_input = gr.Dropdown(label="Time Period", choices=list(TIME_PERIOD_OPTIONS.keys()), value=DEFAULT_TIME_PERIOD)
+            perf_type_input = gr.Dropdown(label="Game Type", choices=PERF_TYPE_OPTIONS_SINGLE, value=DEFAULT_PERF_TYPE)
+            analyze_btn = gr.Button("Analyze Games", variant="primary")
+            status_output = gr.Markdown("")
+            gr.Markdown("---")
+            gr.Markdown("### Analyze vs Titled Players")
+            titled_player_select = gr.CheckboxGroup(label="Select Opponent Titles", choices=TITLES_TO_ANALYZE, value=['GM', 'IM'], elem_id="titled_select")
+            gr.Markdown("*(Analysis updates on 'Analyze Games' click)*")
+        with gr.Column(scale=4):  # Main Content
             with gr.Tabs() as tabs:
-                with gr.TabItem("1. Overview", id=0): overview_stats_md_out; with gr.Row(): overview_plot_pie; overview_plot_color; overview_plot_rating; overview_plot_elo_diff
-                with gr.TabItem("2. Perf. Over Time", id=1): overview_plot_rating; time_plot_games_yr; time_plot_wr_yr
-                with gr.TabItem("3. Perf. by Color", id=2): overview_plot_color; color_plot_placeholder
-                with gr.TabItem("4. Time & Date", id=3): gr.Markdown("### Day of Week"); with gr.Row(): time_plot_games_dow; time_plot_wr_dow; gr.Markdown("### Hour of Day (UTC)"); with gr.Row(): time_plot_games_hod; time_plot_wr_hod; gr.Markdown("### Day of Month"); with gr.Row(): time_plot_games_dom; time_plot_wr_dom; gr.Markdown("### Time Control Category"); time_plot_perf_tc
-                with gr.TabItem("5. ECO & Openings", id=4): gr.Markdown("#### API Names"); eco_plot_freq_api; eco_plot_wr_api; gr.Markdown("---"); gr.Markdown("#### Custom Map"); if not ECO_MAPPING: gr.Markdown("⚠️ Custom map not loaded."); else: eco_plot_freq_cust; eco_plot_wr_cust
-                with gr.TabItem("6. Opponents", id=5): opp_plot_freq; opp_df_list; opp_plot_elo
-                with gr.TabItem("7. vs Titled", id=6): gr.Markdown("Analysis based on sidebar selection."); titled_status; with gr.Row(): titled_plot_pie; titled_plot_color; titled_plot_rating; titled_df_h2h_comp
-                with gr.TabItem("8. Termination", id=7): gr.Markdown("### Time Forfeit"); term_plot_tf_summary; term_plot_tf_tc; with gr.Accordion("View Recent TF Games",open=False): term_df_tf_list; gr.Markdown("### Overall Termination"); term_plot_all
-    outputs_list = [ status_output, df_state, overview_plot_pie, overview_stats_md_out, overview_plot_color, overview_plot_rating, overview_plot_elo_diff, time_plot_games_yr, time_plot_wr_yr, color_plot_placeholder, time_plot_games_dow, time_plot_wr_dow, time_plot_games_hod, time_plot_wr_hod, time_plot_games_dom, time_plot_wr_dom, time_plot_perf_tc, eco_plot_freq_api, eco_plot_wr_api, eco_plot_freq_cust, eco_plot_wr_cust, opp_plot_freq, opp_df_list, opp_plot_elo, titled_status, titled_plot_pie, titled_plot_color, titled_plot_rating, titled_df_h2h_comp, # Correct component name
-                    term_plot_tf_summary, term_plot_tf_tc, term_df_tf_list, term_plot_all ]
-    analyze_btn.click(fn=perform_full_analysis, inputs=[username_input, time_period_input, perf_type_input, titled_player_select], outputs=outputs_list)
+                with gr.TabItem("1. Overview", id=0):
+                    overview_stats_md_out = gr.Markdown()
+                    with gr.Row():
+                        overview_plot_pie = gr.Plot(label="Overall Results")
+                        overview_plot_color = gr.Plot(label="Results by Color")
+                        overview_plot_rating = gr.Plot(label="Rating Trend")
+                        overview_plot_elo_diff = gr.Plot(label="Elo Advantage vs. Result")
+                with gr.TabItem("2. Perf. Over Time", id=1):
+                    time_plot_games_yr = gr.Plot(label="Games per Year")
+                    time_plot_wr_yr = gr.Plot(label="Win Rate per Year")
+                with gr.TabItem("3. Perf. by Color", id=2):
+                    color_plot_placeholder = gr.Markdown("(Results by color shown in Overview)")
+                with gr.TabItem("4. Time & Date", id=3):
+                    gr.Markdown("### Day of Week")
+                    with gr.Row():
+                        time_plot_games_dow = gr.Plot(label="Games by Day of Week")
+                        time_plot_wr_dow = gr.Plot(label="Win Rate by Day of Week")
+                    gr.Markdown("### Hour of Day (UTC)")
+                    with gr.Row():
+                        time_plot_games_hod = gr.Plot(label="Games by Hour (UTC)")
+                        time_plot_wr_hod = gr.Plot(label="Win Rate by Hour (UTC)")
+                    gr.Markdown("### Day of Month")
+                    with gr.Row():
+                        time_plot_games_dom = gr.Plot(label="Games by Day of Month")
+                        time_plot_wr_dom = gr.Plot(label="Win Rate by Day of Month")
+                    gr.Markdown("### Time Control Category")
+                    time_plot_perf_tc = gr.Plot(label="Performance by Time Control")
+                with gr.TabItem("5. ECO & Openings", id=4):
+                    gr.Markdown("#### API Names")
+                    eco_plot_freq_api = gr.Plot(label="Opening Frequency (API)")
+                    eco_plot_wr_api = gr.Plot(label="Opening Win Rate (API)")
+                    gr.Markdown("---")
+                    gr.Markdown("#### Custom Map")
+                    if not ECO_MAPPING:
+                        gr.Markdown("⚠️ Custom map not loaded.")
+                    eco_plot_freq_cust = gr.Plot(label="Opening Frequency (Custom)")
+                    eco_plot_wr_cust = gr.Plot(label="Opening Win Rate (Custom)")
+                with gr.TabItem("6. Opponents", id=5):
+                    opp_plot_freq = gr.Plot(label="Frequent Opponents")
+                    opp_df_list = gr.DataFrame(label="Top Opponents List", wrap=True)
+                    opp_plot_elo = gr.Plot(label="Elo Advantage vs Result")
+                with gr.TabItem("7. vs Titled", id=6):
+                    gr.Markdown("Analysis based on sidebar selection.")
+                    titled_status = gr.Markdown()
+                    with gr.Row():
+                        titled_plot_pie = gr.Plot(label="Results vs Selected Titles")
+                        titled_plot_color = gr.Plot(label="Results by Color vs Selected Titles")
+                        titled_plot_rating = gr.Plot(label="Rating Trend vs Selected Titles")
+                    titled_df_h2h_comp = gr.DataFrame(label="Head-to-Head vs Selected Titles", wrap=True)
+                with gr.TabItem("8. Termination", id=7):
+                    gr.Markdown("### Time Forfeit")
+                    term_plot_tf_summary = gr.Plot(label="Time Forfeit Summary")
+                    term_plot_tf_tc = gr.Plot(label="Time Forfeits by Time Control")
+                    with gr.Accordion("View Recent TF Games", open=False):
+                        term_df_tf_list = gr.DataFrame(label="Recent TF Games", wrap=True)
+                    gr.Markdown("### Overall Termination")
+                    term_plot_all = gr.Plot(label="Overall Termination")
+    outputs_list = [status_output, df_state, overview_plot_pie, overview_stats_md_out, overview_plot_color, overview_plot_rating,
+                    overview_plot_elo_diff, time_plot_games_yr, time_plot_wr_yr, color_plot_placeholder, time_plot_games_dow,
+                    time_plot_wr_dow, time_plot_games_hod, time_plot_wr_hod, time_plot_games_dom, time_plot_wr_dom,
+                    time_plot_perf_tc, eco_plot_freq_api, eco_plot_wr_api, eco_plot_freq_cust, eco_plot_wr_cust, opp_plot_freq,
+                    opp_df_list, opp_plot_elo, titled_status, titled_plot_pie, titled_plot_color, titled_plot_rating,
+                    titled_df_h2h_comp, term_plot_tf_summary, term_plot_tf_tc, term_df_tf_list, term_plot_all]
+    analyze_btn.click(fn=perform_full_analysis, inputs=[username_input, time_period_input, perf_type_input, titled_player_select],
+                      outputs=outputs_list)
 
 # --- Launch the Gradio App ---
 if __name__ == "__main__":