Upload app.py

app.py

@@ -0,0 +1,950 @@
+import gradio as gr
+import pandas as pd
+import numpy as np
+import plotly.graph_objects as go
+import plotly.express as px
+from io import StringIO
+
+# ============================================================
+# LOAD DATA
+# ============================================================
+
+DATA_FILE = "midfielder_scores_final.csv"
+
+df = pd.read_csv(DATA_FILE)
+
+df.columns = (
+    df.columns
+    .str.strip()
+    .str.lower()
+    .str.replace(" ", "_")
+    .str.replace("-", "_")
+    .str.replace("/", "_")
+)
+
+# ============================================================
+# COLUMN SETUP BASED ON YOUR SPREADSHEET
+# ============================================================
+
+PLAYER_COL = "player_name"
+TEAM_COL = "team_name"
+COMP_COL = "competition_name"
+POSITION_COL = "primary_position"
+AGE_COL = "age"
+SEASON_COL = "season_name"
+
+MARKET_VALUE_COL = "market_value_eur"
+CONTRACT_COL = "contract_status"
+HEIGHT_COL = "player_height"
+MINUTES_COL = "player_season_minutes"
+ATTAINABILITY_COL = "attainability"
+
+# Main metrics shown in search/profile tables
+KEY_METRICS = [
+    "player_season_goals_90",
+    "player_season_assists_90",
+    "player_season_np_xg_90",
+    "player_season_xa_90",
+    "player_season_passing_ratio",
+    "player_season_defensive_actions_90",
+    "player_season_pressures_90",
+    "player_season_key_passes_90",
+    "player_season_dribbles_90",
+    "player_season_obv_90"
+]
+
+# Category scores for scouting profile
+CATEGORY_METRICS = [
+    "cat_defensive_ability",
+    "cat_aerial_ability",
+    "cat_finishing",
+    "cat_chance_creation",
+    "cat_dribbling",
+    "cat_ball_progression",
+    "cat_passing",
+    "cat_defensive_intelligence",
+    "cat_pressing_work_rate",
+    "cat_possession_security",
+    "cat_goal_threat",
+    "cat_wide_delivery",
+    "cat_impact",
+    "cat_discipline"
+]
+
+# Archetype/scoring system columns
+SCORING_METRICS = [
+    "defensive_mid_score",
+    "deep_lying_playmaker_score",
+    "box_to_box_score",
+    "advanced_playmaker_score",
+    "wide_mid_score",
+    "attacking_runner_score",
+    "best_midfield_archetype",
+    "best_midfield_archetype_score",
+    "raw_score",
+    "attainability"
+]
+
+RADAR_METRICS = [
+    "cat_defensive_ability",
+    "cat_chance_creation",
+    "cat_dribbling",
+    "cat_ball_progression",
+    "cat_passing",
+    "cat_defensive_intelligence",
+    "cat_pressing_work_rate",
+    "cat_possession_security",
+    "cat_goal_threat",
+    "cat_impact"
+]
+
+PERCENTILE_METRICS = [
+    "player_season_goals_90",
+    "player_season_assists_90",
+    "player_season_np_xg_90",
+    "player_season_xa_90",
+    "player_season_key_passes_90",
+    "player_season_passing_ratio",
+    "player_season_defensive_actions_90",
+    "player_season_pressures_90",
+    "player_season_dribbles_90",
+    "player_season_obv_90",
+    "attainability"
+]
+
+FIT_SCORE_METRICS = [
+    "cat_defensive_ability",
+    "cat_chance_creation",
+    "cat_ball_progression",
+    "cat_passing",
+    "cat_pressing_work_rate",
+    "cat_possession_security",
+    "cat_goal_threat",
+    "cat_impact",
+    "attainability"
+]
+
+# ============================================================
+# CLEANING HELPERS
+# ============================================================
+
+def available_cols(cols):
+    return [c for c in cols if c in df.columns]
+
+def label_col(col):
+    return col.replace("player_season_", "").replace("_90", " per 90").replace("_", " ").title()
+
+def format_money(x):
+    try:
+        if pd.isna(x):
+            return "Not listed"
+        x = float(x)
+        if x >= 1000000:
+            return f"€{x/1000000:.1f}M"
+        if x >= 1000:
+            return f"€{x/1000:.0f}K"
+        return f"€{x:.0f}"
+    except:
+        return "Not listed"
+
+def safe_numeric(data, col):
+    if col in data.columns:
+        return pd.to_numeric(data[col], errors="coerce")
+    return pd.Series(dtype=float)
+
+for col in available_cols(KEY_METRICS + CATEGORY_METRICS + SCORING_METRICS + PERCENTILE_METRICS + FIT_SCORE_METRICS):
+    if col != "best_midfield_archetype":
+        df[col] = pd.to_numeric(df[col], errors="coerce")
+
+if AGE_COL in df.columns:
+    df[AGE_COL] = pd.to_numeric(df[AGE_COL], errors="coerce")
+
+# ============================================================
+# DROPDOWN OPTIONS
+# ============================================================
+
+player_options = sorted(df[PLAYER_COL].dropna().astype(str).unique().tolist())
+competition_options = ["All"] + sorted(df[COMP_COL].dropna().astype(str).unique().tolist())
+team_options = ["All"] + sorted(df[TEAM_COL].dropna().astype(str).unique().tolist())
+position_options = ["All"] + sorted(df[POSITION_COL].dropna().astype(str).unique().tolist())
+
+age_min = int(np.floor(df[AGE_COL].min())) if AGE_COL in df.columns else 15
+age_max = int(np.ceil(df[AGE_COL].max())) if AGE_COL in df.columns else 45
+
+metric_options = available_cols(KEY_METRICS + CATEGORY_METRICS + SCORING_METRICS)
+
+shortlist = []
+
+# ============================================================
+# ============================================================
+# REPLACED YOUR PLAYER SEARCH FUNCTION WITH THIS
+# ============================================================
+
+def search_players(search, competitions, teams, positions, min_age, max_age, min_minutes):
+    data = df.copy()
+
+    # Player name search
+    if search and PLAYER_COL in data.columns:
+        data = data[
+            data[PLAYER_COL]
+            .astype(str)
+            .str.contains(str(search), case=False, na=False)
+        ]
+
+    # Multi-select competition filter
+    if competitions and COMP_COL in data.columns:
+        data = data[data[COMP_COL].astype(str).isin(competitions)]
+
+    # Multi-select team filter
+    if teams and TEAM_COL in data.columns:
+        data = data[data[TEAM_COL].astype(str).isin(teams)]
+
+    # Multi-select position filter
+    if positions and POSITION_COL in data.columns:
+        data = data[data[POSITION_COL].astype(str).isin(positions)]
+
+    # Age filters
+    if AGE_COL in data.columns:
+        data[AGE_COL] = pd.to_numeric(data[AGE_COL], errors="coerce")
+        data = data[
+            (data[AGE_COL] >= min_age) &
+            (data[AGE_COL] <= max_age)
+        ]
+
+    # Minimum minutes filter
+    if MINUTES_COL in data.columns:
+        data[MINUTES_COL] = pd.to_numeric(data[MINUTES_COL], errors="coerce")
+        data = data[data[MINUTES_COL].fillna(0) >= min_minutes]
+
+    table_cols = available_cols([
+        PLAYER_COL,
+        TEAM_COL,
+        COMP_COL,
+        POSITION_COL,
+        AGE_COL,
+        MINUTES_COL,
+        MARKET_VALUE_COL,
+        CONTRACT_COL,
+        "best_midfield_archetype",
+        "best_midfield_archetype_score",
+        "raw_score",
+        ATTAINABILITY_COL
+    ] + KEY_METRICS)
+
+    out = data[table_cols].copy()
+
+    if out.empty:
+        return pd.DataFrame({"Message": ["No players found. Try clearing some filters."]})
+
+    if MARKET_VALUE_COL in out.columns:
+        out[MARKET_VALUE_COL] = out[MARKET_VALUE_COL].apply(format_money)
+
+    if AGE_COL in out.columns:
+        out[AGE_COL] = pd.to_numeric(out[AGE_COL], errors="coerce").round(1)
+
+    numeric_cols = out.select_dtypes(include=np.number).columns
+    out[numeric_cols] = out[numeric_cols].round(2)
+
+    if ATTAINABILITY_COL in out.columns:
+        out = out.sort_values(by=ATTAINABILITY_COL, ascending=False)
+
+    return out.reset_index(drop=True)
+
+    if MARKET_VALUE_COL in out.columns:
+        out[MARKET_VALUE_COL] = out[MARKET_VALUE_COL].apply(format_money)
+
+    if AGE_COL in out.columns:
+        out[AGE_COL] = out[AGE_COL].round(1)
+
+    numeric_cols = out.select_dtypes(include=np.number).columns
+    out[numeric_cols] = out[numeric_cols].round(2)
+
+    return out.sort_values(by=ATTAINABILITY_COL, ascending=False).reset_index(drop=True)
+
+# ============================================================
+# PLAYER PROFILE
+# ============================================================
+
+def get_player_row(player):
+    rows = df[df[PLAYER_COL].astype(str) == str(player)]
+    if rows.empty:
+        return None
+    return rows.iloc[0]
+
+def player_profile(player):
+    row = get_player_row(player)
+
+    if row is None:
+        return "Select a player to view their profile."
+
+    lines = []
+    lines.append(f"# {row[PLAYER_COL]}")
+    lines.append(f"### {row.get(TEAM_COL, 'N/A')} | {row.get(COMP_COL, 'N/A')}")
+    lines.append("")
+    lines.append("## Player Details")
+    lines.append(f"- **Position:** {row.get(POSITION_COL, 'N/A')}")
+    lines.append(f"- **Age:** {round(row.get(AGE_COL, np.nan), 1) if pd.notna(row.get(AGE_COL, np.nan)) else 'N/A'}")
+    lines.append(f"- **Height:** {row.get(HEIGHT_COL, 'N/A')}")
+    lines.append(f"- **Market Value:** {format_money(row.get(MARKET_VALUE_COL, np.nan))}")
+    lines.append(f"- **Contract Status:** {row.get(CONTRACT_COL, 'N/A')}")
+    lines.append(f"- **Minutes:** {round(row.get(MINUTES_COL, 0), 0)}")
+    lines.append("")
+    lines.append("## Scoring System")
+    lines.append(f"- **Best Midfield Archetype:** {row.get('best_midfield_archetype', 'N/A')}")
+    lines.append(f"- **Best Archetype Score:** {round(row.get('best_midfield_archetype_score', np.nan), 2) if pd.notna(row.get('best_midfield_archetype_score', np.nan)) else 'N/A'}")
+    lines.append(f"- **Raw Player Score:** {round(row.get('raw_score', np.nan), 2) if pd.notna(row.get('raw_score', np.nan)) else 'N/A'}")
+    lines.append(f"- **Attainability Score:** {round(row.get(ATTAINABILITY_COL, np.nan), 2) if pd.notna(row.get(ATTAINABILITY_COL, np.nan)) else 'N/A'}")
+    lines.append("")
+    lines.append("## Key Season Stats")
+
+    for col in available_cols(KEY_METRICS):
+        value = row.get(col, np.nan)
+        if pd.notna(value):
+            lines.append(f"- **{label_col(col)}:** {round(value, 2)}")
+
+    return "\n".join(lines)
+
+def category_table(player):
+    row = get_player_row(player)
+
+    if row is None:
+        return pd.DataFrame({"Message": ["Select a player."]})
+
+    rows = []
+    for col in available_cols(CATEGORY_METRICS):
+        value = row.get(col, np.nan)
+        if pd.notna(value):
+            rows.append({
+                "Category": label_col(col.replace("cat_", "")),
+                "Score": round(value, 2)
+            })
+
+    return pd.DataFrame(rows).sort_values("Score", ascending=False)
+
+# ============================================================
+# RADAR CHART
+# ============================================================
+
+def radar_chart(player):
+    row = get_player_row(player)
+
+    if row is None:
+        return go.Figure()
+
+    metrics = available_cols(RADAR_METRICS)
+
+    if len(metrics) < 3:
+        fig = go.Figure()
+        fig.update_layout(title="Need at least 3 radar metrics.")
+        return fig
+
+    comp = row[COMP_COL]
+    pos = row[POSITION_COL]
+
+    group = df[(df[COMP_COL] == comp) & (df[POSITION_COL] == pos)].copy()
+
+    labels = [label_col(m.replace("cat_", "")) for m in metrics]
+    player_values = [row[m] for m in metrics]
+    avg_values = [group[m].mean() for m in metrics]
+
+    fig = go.Figure()
+
+    fig.add_trace(go.Scatterpolar(
+        r=player_values,
+        theta=labels,
+        fill="toself",
+        name=str(player)
+    ))
+
+    fig.add_trace(go.Scatterpolar(
+        r=avg_values,
+        theta=labels,
+        fill="toself",
+        name=f"{pos} Avg in {comp}"
+    ))
+
+    fig.update_layout(
+        title=f"{player} vs Position/Competition Average",
+        polar=dict(radialaxis=dict(visible=True)),
+        showlegend=True
+    )
+
+    return fig
+
+# ============================================================
+# PERCENTILE BARS
+# ============================================================
+
+def percentile_chart(player):
+    row = get_player_row(player)
+
+    if row is None:
+        return go.Figure()
+
+    comp = row[COMP_COL]
+    pos = row[POSITION_COL]
+    group = df[(df[COMP_COL] == comp) & (df[POSITION_COL] == pos)].copy()
+
+    rows = []
+
+    for metric in available_cols(PERCENTILE_METRICS):
+        value = row.get(metric, np.nan)
+        values = pd.to_numeric(group[metric], errors="coerce").dropna()
+
+        if pd.notna(value) and len(values) > 1:
+            pct = (values < value).mean() * 100
+            rows.append({
+                "Metric": label_col(metric),
+                "Percentile": round(pct, 1),
+                "Value": round(value, 2)
+            })
+
+    plot_df = pd.DataFrame(rows)
+
+    if plot_df.empty:
+        fig = go.Figure()
+        fig.update_layout(title="No percentile data available.")
+        return fig
+
+    fig = px.bar(
+        plot_df.sort_values("Percentile"),
+        x="Percentile",
+        y="Metric",
+        orientation="h",
+        hover_data=["Value"],
+        title=f"{player} Percentiles vs Same Position and Competition",
+        range_x=[0, 100]
+    )
+
+    fig.update_layout(yaxis_title="", xaxis_title="Percentile")
+    return fig
+
+# ============================================================
+# PERFORMANCE OVER TIME
+# ============================================================
+
+def performance_chart(player, metric):
+    row_data = df[df[PLAYER_COL].astype(str) == str(player)].copy()
+
+    if row_data.empty or metric not in df.columns:
+        return go.Figure()
+
+    if SEASON_COL not in df.columns or row_data[SEASON_COL].nunique() <= 1:
+        fig = go.Figure()
+        fig.add_trace(go.Bar(
+            x=[label_col(metric)],
+            y=[row_data.iloc[0][metric]]
+        ))
+        fig.update_layout(
+            title="Only one season is currently in this file, so a true over-time line chart is not available yet.",
+            yaxis_title=label_col(metric)
+        )
+        return fig
+
+    row_data[metric] = pd.to_numeric(row_data[metric], errors="coerce")
+
+    fig = px.line(
+        row_data,
+        x=SEASON_COL,
+        y=metric,
+        markers=True,
+        title=f"{player}: {label_col(metric)} Over Time"
+    )
+
+    return fig
+
+# ============================================================
+# PLAYER COMPARISON
+# ============================================================
+
+def compare_players(player_1, player_2, player_3):
+    players = [p for p in [player_1, player_2, player_3] if p]
+
+    if not players:
+        return pd.DataFrame({"Message": ["Select at least one player."]})
+
+    data = df[df[PLAYER_COL].astype(str).isin(players)].copy()
+
+    cols = available_cols([
+        PLAYER_COL,
+        TEAM_COL,
+        COMP_COL,
+        POSITION_COL,
+        AGE_COL,
+        MINUTES_COL,
+        MARKET_VALUE_COL,
+        CONTRACT_COL,
+        "best_midfield_archetype",
+        "best_midfield_archetype_score",
+        "raw_score",
+        ATTAINABILITY_COL
+    ] + KEY_METRICS + CATEGORY_METRICS)
+
+    out = data[cols].copy()
+
+    if MARKET_VALUE_COL in out.columns:
+        out[MARKET_VALUE_COL] = out[MARKET_VALUE_COL].apply(format_money)
+
+    numeric_cols = out.select_dtypes(include=np.number).columns
+    out[numeric_cols] = out[numeric_cols].round(2)
+
+    return out.reset_index(drop=True)
+
+def comparison_radar(player_1, player_2, player_3):
+    players = [p for p in [player_1, player_2, player_3] if p]
+    metrics = available_cols(RADAR_METRICS)
+
+    fig = go.Figure()
+
+    if not players or len(metrics) < 3:
+        fig.update_layout(title="Select players to compare.")
+        return fig
+
+    labels = [label_col(m.replace("cat_", "")) for m in metrics]
+
+    for player in players:
+        row = get_player_row(player)
+        if row is not None:
+            fig.add_trace(go.Scatterpolar(
+                r=[row[m] for m in metrics],
+                theta=labels,
+                fill="toself",
+                name=str(player)
+            ))
+
+    fig.update_layout(
+        title="Side-by-Side Radar Comparison",
+        polar=dict(radialaxis=dict(visible=True)),
+        showlegend=True
+    )
+
+    return fig
+
+# ============================================================
+# FIT SCORE CALCULATOR
+# ============================================================
+
+def fit_score(defense_w, chance_w, progression_w, passing_w, pressing_w, security_w, goal_w, impact_w, attain_w):
+    weights = {
+        "cat_defensive_ability": defense_w,
+        "cat_chance_creation": chance_w,
+        "cat_ball_progression": progression_w,
+        "cat_passing": passing_w,
+        "cat_pressing_work_rate": pressing_w,
+        "cat_possession_security": security_w,
+        "cat_goal_threat": goal_w,
+        "cat_impact": impact_w,
+        "attainability": attain_w
+    }
+
+    data = df.copy()
+    total_weight = sum(weights.values())
+
+    if total_weight == 0:
+        return pd.DataFrame({"Message": ["At least one weight must be above 0."]})
+
+    score = 0
+
+    for col, weight in weights.items():
+        if col in data.columns:
+            values = pd.to_numeric(data[col], errors="coerce")
+            min_v = values.min()
+            max_v = values.max()
+
+            if pd.notna(min_v) and pd.notna(max_v) and max_v != min_v:
+                normalized = ((values - min_v) / (max_v - min_v)) * 100
+            else:
+                normalized = values
+
+            score += normalized.fillna(0) * weight
+
+    data["custom_fit_score"] = score / total_weight
+
+    cols = available_cols([
+        PLAYER_COL,
+        TEAM_COL,
+        COMP_COL,
+        POSITION_COL,
+        AGE_COL,
+        MARKET_VALUE_COL,
+        "best_midfield_archetype",
+        "raw_score",
+        ATTAINABILITY_COL
+    ]) + ["custom_fit_score"]
+
+    out = data[cols].sort_values("custom_fit_score", ascending=False).head(25).copy()
+
+    if MARKET_VALUE_COL in out.columns:
+        out[MARKET_VALUE_COL] = out[MARKET_VALUE_COL].apply(format_money)
+
+    numeric_cols = out.select_dtypes(include=np.number).columns
+    out[numeric_cols] = out[numeric_cols].round(2)
+
+    return out.reset_index(drop=True)
+
+# ============================================================
+# SIMILAR PLAYER FINDER
+# ============================================================
+
+def similar_players(player):
+    row = get_player_row(player)
+
+    if row is None:
+        return pd.DataFrame({"Message": ["Select a player."]})
+
+    metrics = available_cols(CATEGORY_METRICS + KEY_METRICS)
+
+    comp = row[COMP_COL]
+    pos = row[POSITION_COL]
+
+    candidates = df[
+        (df[PLAYER_COL].astype(str) != str(player)) &
+        (df[POSITION_COL] == pos)
+    ].copy()
+
+    if candidates.empty:
+        candidates = df[df[PLAYER_COL].astype(str) != str(player)].copy()
+
+    for metric in metrics:
+        candidates[metric] = pd.to_numeric(candidates[metric], errors="coerce")
+        df_metric = pd.to_numeric(df[metric], errors="coerce")
+        sd = df_metric.std()
+
+        if pd.isna(sd) or sd == 0:
+            candidates[f"dist_{metric}"] = 0
+        else:
+            candidates[f"dist_{metric}"] = ((candidates[metric] - row[metric]) / sd) ** 2
+
+    dist_cols = [f"dist_{m}" for m in metrics]
+    candidates["similarity_distance"] = candidates[dist_cols].sum(axis=1)
+    candidates["similarity_score"] = 100 / (1 + candidates["similarity_distance"])
+
+    cols = available_cols([
+        PLAYER_COL,
+        TEAM_COL,
+        COMP_COL,
+        POSITION_COL,
+        AGE_COL,
+        MARKET_VALUE_COL,
+        "best_midfield_archetype",
+        "raw_score",
+        ATTAINABILITY_COL
+    ]) + ["similarity_score"]
+
+    out = candidates[cols].sort_values("similarity_score", ascending=False).head(5).copy()
+
+    if MARKET_VALUE_COL in out.columns:
+        out[MARKET_VALUE_COL] = out[MARKET_VALUE_COL].apply(format_money)
+
+    numeric_cols = out.select_dtypes(include=np.number).columns
+    out[numeric_cols] = out[numeric_cols].round(2)
+
+    return out.reset_index(drop=True)
+
+# ============================================================
+# SHORTLIST
+# ============================================================
+
+def add_to_shortlist(player):
+    global shortlist
+
+    if player and player not in shortlist:
+        shortlist.append(player)
+
+    return view_shortlist()
+
+def clear_shortlist():
+    global shortlist
+    shortlist = []
+    return view_shortlist()
+
+def view_shortlist():
+    if not shortlist:
+        return pd.DataFrame({"Message": ["No players added to shortlist yet."]})
+
+    data = df[df[PLAYER_COL].astype(str).isin(shortlist)].copy()
+
+    cols = available_cols([
+        PLAYER_COL,
+        TEAM_COL,
+        COMP_COL,
+        POSITION_COL,
+        AGE_COL,
+        MARKET_VALUE_COL,
+        CONTRACT_COL,
+        "best_midfield_archetype",
+        "raw_score",
+        ATTAINABILITY_COL
+    ] + KEY_METRICS)
+
+    out = data[cols].copy()
+
+    if MARKET_VALUE_COL in out.columns:
+        out[MARKET_VALUE_COL] = out[MARKET_VALUE_COL].apply(format_money)
+
+    numeric_cols = out.select_dtypes(include=np.number).columns
+    out[numeric_cols] = out[numeric_cols].round(2)
+
+    return out.reset_index(drop=True)
+
+def export_shortlist_csv():
+    if not shortlist:
+        return None
+
+    data = df[df[PLAYER_COL].astype(str).isin(shortlist)].copy()
+    out_file = "shortlist_export.csv"
+    data.to_csv(out_file, index=False)
+    return out_file
+
+# ============================================================
+# SCOUTING REPORT EXPORT
+# ============================================================
+
+def export_player_report(player, notes):
+    row = get_player_row(player)
+
+    if row is None:
+        return None
+
+    report = []
+    report.append(f"Player Scouting Report: {row[PLAYER_COL]}")
+    report.append("=" * 60)
+    report.append("")
+    report.append(f"Club: {row.get(TEAM_COL, 'N/A')}")
+    report.append(f"Competition: {row.get(COMP_COL, 'N/A')}")
+    report.append(f"Position: {row.get(POSITION_COL, 'N/A')}")
+    report.append(f"Age: {round(row.get(AGE_COL, np.nan), 1) if pd.notna(row.get(AGE_COL, np.nan)) else 'N/A'}")
+    report.append(f"Height: {row.get(HEIGHT_COL, 'N/A')}")
+    report.append(f"Market Value: {format_money(row.get(MARKET_VALUE_COL, np.nan))}")
+    report.append(f"Contract Status: {row.get(CONTRACT_COL, 'N/A')}")
+    report.append("")
+    report.append("Scoring System")
+    report.append("-" * 60)
+    report.append(f"Best Midfield Archetype: {row.get('best_midfield_archetype', 'N/A')}")
+    report.append(f"Best Archetype Score: {round(row.get('best_midfield_archetype_score', np.nan), 2) if pd.notna(row.get('best_midfield_archetype_score', np.nan)) else 'N/A'}")
+    report.append(f"Raw Score: {round(row.get('raw_score', np.nan), 2) if pd.notna(row.get('raw_score', np.nan)) else 'N/A'}")
+    report.append(f"Attainability: {round(row.get(ATTAINABILITY_COL, np.nan), 2) if pd.notna(row.get(ATTAINABILITY_COL, np.nan)) else 'N/A'}")
+    report.append("")
+    report.append("Key Metrics")
+    report.append("-" * 60)
+
+    for col in available_cols(KEY_METRICS):
+        value = row.get(col, np.nan)
+        if pd.notna(value):
+            report.append(f"{label_col(col)}: {round(value, 2)}")
+
+    report.append("")
+    report.append("Category Scores")
+    report.append("-" * 60)
+
+    for col in available_cols(CATEGORY_METRICS):
+        value = row.get(col, np.nan)
+        if pd.notna(value):
+            report.append(f"{label_col(col.replace('cat_', ''))}: {round(value, 2)}")
+
+    report.append("")
+    report.append("Scout Notes")
+    report.append("-" * 60)
+    report.append(notes if notes else "No notes entered.")
+
+    safe_name = str(row[PLAYER_COL]).replace(" ", "_").replace("/", "_")
+    out_file = f"{safe_name}_scouting_report.txt"
+
+    with open(out_file, "w", encoding="utf-8") as f:
+        f.write("\n".join(report))
+
+    return out_file
+
+# ============================================================
+# APP LAYOUT
+# ============================================================
+
+with gr.Blocks(title="Oldham Athletic Player Scouting") as app:
+
+    gr.Markdown(
+        """
+        # Oldham Athletic Player Scouting
+        
+        Interactive player scouting dashboard for midfielders across League One, League Two, the National League, National League N/S, and the Scottish Championship.
+        """
+    )
+
+    with gr.Tab("Player Search"):
+        gr.Markdown("## Search and Filter Players")
+    
+        with gr.Row():
+            search_box = gr.Textbox(label="Search Player Name")
+    
+            competition_filter = gr.Dropdown(
+                choices=competition_options[1:],
+                value=[],
+                label="Competition",
+                multiselect=True
+            )
+    
+            team_filter = gr.Dropdown(
+                choices=team_options[1:],
+                value=[],
+                label="Team",
+                multiselect=True
+            )
+    
+        with gr.Row():
+            position_filter = gr.Dropdown(
+                choices=position_options[1:],
+                value=[],
+                label="Position",
+                multiselect=True
+            )
+    
+            min_age_filter = gr.Slider(
+                minimum=age_min,
+                maximum=age_max,
+                value=age_min,
+                step=1,
+                label="Minimum Age"
+            )
+    
+            max_age_filter = gr.Slider(
+                minimum=age_min,
+                maximum=age_max,
+                value=age_max,
+                step=1,
+                label="Maximum Age"
+            )
+    
+            minutes_filter = gr.Slider(
+                minimum=0,
+                maximum=int(df[MINUTES_COL].max()) if MINUTES_COL in df.columns else 3000,
+                value=0,
+                step=100,
+                label="Minimum Minutes"
+            )
+    
+        search_button = gr.Button("Search Players")
+        search_results = gr.Dataframe(label="Sortable Player Results", interactive=False)
+    
+        search_button.click(
+            fn=search_players,
+            inputs=[
+                search_box,
+                competition_filter,
+                team_filter,
+                position_filter,
+                min_age_filter,
+                max_age_filter,
+                minutes_filter
+            ],
+            outputs=search_results
+        )
+
+    with gr.Tab("Player Profile"):
+        gr.Markdown("## Full Player Profile")
+
+        selected_player = gr.Dropdown(player_options, label="Select Player")
+
+        with gr.Row():
+            profile_output = gr.Markdown()
+            category_output = gr.Dataframe(label="Season Stats by Category", interactive=False)
+
+        with gr.Row():
+            radar_output = gr.Plot(label="Radar Chart")
+            percentile_output = gr.Plot(label="Percentile Bars")
+
+        with gr.Row():
+            profile_metric = gr.Dropdown(metric_options, value=metric_options[0] if metric_options else None, label="Performance Metric")
+            trend_button = gr.Button("Show Performance Chart")
+
+        trend_output = gr.Plot(label="Performance Over Time")
+
+        scout_notes = gr.Textbox(label="Scout Notes", lines=5, placeholder="Enter notes to include in the scouting report.")
+        report_button = gr.Button("Generate Scouting Report")
+        report_file = gr.File(label="Download Scouting Report")
+
+        shortlist_button = gr.Button("Add Player to Shortlist")
+        shortlist_from_profile = gr.Dataframe(label="Current Shortlist", interactive=False)
+
+        selected_player.change(player_profile, selected_player, profile_output)
+        selected_player.change(category_table, selected_player, category_output)
+        selected_player.change(radar_chart, selected_player, radar_output)
+        selected_player.change(percentile_chart, selected_player, percentile_output)
+
+        trend_button.click(performance_chart, [selected_player, profile_metric], trend_output)
+        report_button.click(export_player_report, [selected_player, scout_notes], report_file)
+        shortlist_button.click(add_to_shortlist, selected_player, shortlist_from_profile)
+
+    with gr.Tab("Player Comparison Tool"):
+        gr.Markdown("## Compare Up To Three Players")
+
+        with gr.Row():
+            compare_1 = gr.Dropdown(player_options, label="Player 1")
+            compare_2 = gr.Dropdown(player_options, label="Player 2")
+            compare_3 = gr.Dropdown(player_options, label="Player 3")
+
+        compare_button = gr.Button("Compare Players")
+
+        comparison_table = gr.Dataframe(label="Stat Comparison Table", interactive=False)
+        comparison_radar_plot = gr.Plot(label="Side-by-Side Radar Chart")
+
+        compare_button.click(compare_players, [compare_1, compare_2, compare_3], comparison_table)
+        compare_button.click(comparison_radar, [compare_1, compare_2, compare_3], comparison_radar_plot)
+
+    with gr.Tab("Fit Score Calculator"):
+        gr.Markdown(
+            """
+            ## Custom Fit Score Calculator
+            
+            Move the sliders to weight the traits Oldham cares about most.  
+            The app will rank players based on your custom scouting profile.
+            """
+        )
+
+        with gr.Row():
+            defense_w = gr.Slider(0, 10, value=5, step=1, label="Defensive Ability")
+            chance_w = gr.Slider(0, 10, value=5, step=1, label="Chance Creation")
+            progression_w = gr.Slider(0, 10, value=5, step=1, label="Ball Progression")
+
+        with gr.Row():
+            passing_w = gr.Slider(0, 10, value=5, step=1, label="Passing")
+            pressing_w = gr.Slider(0, 10, value=5, step=1, label="Pressing Work Rate")
+            security_w = gr.Slider(0, 10, value=5, step=1, label="Possession Security")
+
+        with gr.Row():
+            goal_w = gr.Slider(0, 10, value=3, step=1, label="Goal Threat")
+            impact_w = gr.Slider(0, 10, value=5, step=1, label="Impact")
+            attain_w = gr.Slider(0, 10, value=5, step=1, label="Attainability")
+
+        fit_button = gr.Button("Generate Ranked Recommendations")
+        fit_table = gr.Dataframe(label="Ranked Recommendations", interactive=False)
+
+        fit_button.click(
+            fit_score,
+            [defense_w, chance_w, progression_w, passing_w, pressing_w, security_w, goal_w, impact_w, attain_w],
+            fit_table
+        )
+
+    with gr.Tab("Similar Player Finder"):
+        gr.Markdown("## Find Similar Players")
+
+        similar_player_select = gr.Dropdown(player_options, label="Select Player")
+        similar_button = gr.Button("Find Similar Players")
+        similar_table = gr.Dataframe(label="Five Similar Players", interactive=False)
+
+        similar_button.click(similar_players, similar_player_select, similar_table)
+
+    with gr.Tab("Shortlist Manager"):
+        gr.Markdown("## Shortlist Manager")
+
+        shortlist_player = gr.Dropdown(player_options, label="Add Player")
+        add_shortlist_button = gr.Button("Add to Shortlist")
+        clear_shortlist_button = gr.Button("Clear Shortlist")
+        export_shortlist_button = gr.Button("Export Shortlist CSV")
+
+        shortlist_table = gr.Dataframe(label="Saved Players", interactive=False)
+        shortlist_file = gr.File(label="Download Shortlist CSV")
+
+        add_shortlist_button.click(add_to_shortlist, shortlist_player, shortlist_table)
+        clear_shortlist_button.click(clear_shortlist, None, shortlist_table)
+        export_shortlist_button.click(export_shortlist_csv, None, shortlist_file)
+
+app.launch()