app.py

import gradio as gr
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
import statsmodels.api as sm

# =======================
# LOAD DATA
# =======================
df = pd.read_csv("chess_analysis.csv")

df_long = pd.concat([
    pd.DataFrame({
        "year": df["Year"],
        "acpl": df["White.ACPL"],
        "player": df["White.Player"],
        "color": "White"
    }),
    pd.DataFrame({
        "year": df["Year"],
        "acpl": df["Black.ACPL"],
        "player": df["Black.Player"],
        "color": "Black"
    })
]).dropna()

df_long["engine"] = np.where(df_long["year"] >= 1996, "Post-1996", "Pre-1996")

players = sorted(df_long["player"].unique().tolist())

# =======================
# PRECOMPUTE MODELS
# =======================
X = sm.add_constant(df_long["year"])
trend_model = sm.OLS(df_long["acpl"], X).fit()

df_sorted = df_long.sort_values("year")
df_sorted["pred"] = trend_model.predict(sm.add_constant(df_sorted["year"]))

future_years = pd.DataFrame({
    "year": np.arange(df_long["year"].max(), df_long["year"].max() + 10)
})
future_years["pred"] = trend_model.predict(sm.add_constant(future_years["year"]))

# =======================
# STATIC PLOTS (FAST)
# =======================

def overview_plot():
    fig = px.scatter(df_long, x="year", y="acpl", opacity=0.2)
    fig.add_scatter(x=df_sorted["year"], y=df_sorted["pred"], mode="lines", name="Trend")
    fig.update_layout(title="📈 Performance Over Time", template="plotly_white")
    return fig


def engine_plot():
    fig = px.scatter(df_long, x="year", y="acpl", color="engine", opacity=0.3)
    fig.update_layout(title="🤖 Engine Effect", template="plotly_white")
    return fig


def prediction_plot():
    fig = go.Figure()

    fig.add_trace(go.Scatter(
        x=df_sorted["year"], y=df_sorted["acpl"],
        mode="markers", opacity=0.2, name="Observed"
    ))

    fig.add_trace(go.Scatter(
        x=df_sorted["year"], y=df_sorted["pred"],
        mode="lines", name="Trend"
    ))

    fig.add_trace(go.Scatter(
        x=future_years["year"], y=future_years["pred"],
        mode="lines", line=dict(dash="dash"), name="Future"
    ))

    fig.update_layout(title="🔮 Future Prediction", template="plotly_white")
    return fig


# =======================
# PLAYER ANALYSIS (ONLY INTERACTIVE PART)
# =======================

def player_analysis(player):
    data = df_long[df_long["player"] == player]

    avg = data["acpl"].mean()
    games = len(data)

    fig = px.scatter(data, x="year", y="acpl", title=f"{player} Performance")
    fig.update_layout(template="plotly_white")

    return f"Avg ACPL: {avg:.2f} | Games: {games}", fig


# =======================
# KPI
# =======================

def summary_stats():
    avg_acpl = df_long["acpl"].mean()
    best_player = df_long.groupby("player")["acpl"].mean().idxmin()

    return f"📊 Avg ACPL: {avg_acpl:.2f}", f"🏆 Best Player: {best_player}"


# =======================
# UI (NO TABS)
# =======================

with gr.Blocks(theme=gr.themes.Soft()) as app:

    gr.Markdown("# ♟️ Chess Performance Dashboard")
    gr.Markdown("Explore performance trends, player insights, and engine impact.")

    # KPI Row
    with gr.Row():
        kpi1 = gr.Textbox(label="Average Performance", interactive=False)
        kpi2 = gr.Textbox(label="Top Player", interactive=False)

    app.load(summary_stats, outputs=[kpi1, kpi2])

    # =======================
    # MAIN DASHBOARD
    # =======================

    with gr.Row():

        # LEFT PANEL (CONTROLS)
        with gr.Column(scale=1):
            gr.Markdown("## ⚙️ Controls")

            player_dropdown = gr.Dropdown(
                players,
                label="Select Player",
                value=players[0]
            )

            output_text = gr.Textbox(label="Player Summary")

        # RIGHT PANEL (VISUALS)
        with gr.Column(scale=3):

            gr.Markdown("## 📈 Performance Over Time")
            gr.Plot(overview_plot())

            gr.Markdown("## 🤖 Engine Effect")
            gr.Plot(engine_plot())

            gr.Markdown("## 🔮 Future Prediction")
            gr.Plot(prediction_plot())

            gr.Markdown("## 🏆 Player Analysis")
            player_plot = gr.Plot()

    # Connect interaction
    player_dropdown.change(
        player_analysis,
        inputs=player_dropdown,
        outputs=[output_text, player_plot]
    )

# RUN
app.launch()