Create app.py

app.py

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+import gradio as gr
+import pickle
+import statsmodels.api as sm
+import pandas as pd
+import numpy as np
+import plotly.express as px
+
+# =======================
+# LOAD + PREP 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())
+
+# =======================
+# FUNCTIONS
+# =======================
+
+def overview_plot():
+    fig = px.scatter(
+        df_long,
+        x="year",
+        y="acpl",
+        opacity=0.3,
+        title="📈 Performance Over Time"
+    )
+    fig.update_layout(template="plotly_white")
+    return fig
+
+
+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",
+    )
+
+    return f"Avg ACPL: {avg:.2f} | Games: {games}", fig
+
+
+def engine_plot():
+    fig = px.scatter(
+        df_long,
+        x="year",
+        y="acpl",
+        color="engine",
+        opacity=0.3,
+        title="🤖 Engine Effect (Pre vs Post 1996)"
+    )
+    fig.update_layout(template="plotly_white")
+    return fig
+
+
+def prediction_plot():
+
+
+
+
+    with open("acpl_trend_model.pkl", "rb") as f:
+        model = pickle.load(f)
+
+    df_sorted = df_long.sort_values("year")
+
+    # Predictions on existing data
+    df_sorted["pred"] = model.predict(sm.add_constant(df_sorted["year"]))
+
+    # Future years
+    future_years = pd.DataFrame({
+        "year": np.arange(df_long["year"].max(), df_long["year"].max() + 10)
+    })
+
+    future_years["pred"] = model.predict(sm.add_constant(future_years["year"]))
+    # ---- Fit model ----
+    X = sm.add_constant(df_long["year"])
+    model = sm.OLS(df_long["acpl"], X).fit()
+
+    # ---- Sort data ----
+    df_sorted = df_long.sort_values("year")
+
+    # ---- Fitted values ----
+    df_sorted["pred"] = model.predict(sm.add_constant(df_sorted["year"]))
+
+    # ---- Future years ----
+    future_years = pd.DataFrame({
+        "year": np.arange(df_long["year"].max(), df_long["year"].max() + 10)
+    })
+
+    future_years["pred"] = model.predict(sm.add_constant(future_years["year"]))
+    import plotly.graph_objects as go
+
+    # ---- Plot ----
+    fig = go.Figure()
+
+    # Scatter (actual data)
+    fig.add_trace(go.Scatter(
+        x=df_sorted["year"],
+        y=df_sorted["acpl"],
+        mode="markers",
+        opacity=0.2,
+        name="Observed"
+    ))
+
+    # Fitted trend line
+    fig.add_trace(go.Scatter(
+        x=df_sorted["year"],
+        y=df_sorted["pred"],
+        mode="lines",
+        name="Trend (Fitted)"
+    ))
+
+    # Future prediction line
+    fig.add_trace(go.Scatter(
+        x=future_years["year"],
+        y=future_years["pred"],
+        mode="lines",
+        line=dict(dash="dash"),
+        name="Future Prediction"
+    ))
+
+    fig.update_layout(
+        title="🔮 Future Performance Trend",
+        xaxis_title="Year",
+        yaxis_title="ACPL",
+        template="plotly_white"
+    )
+
+    return fig
+
+
+def summary_stats():
+    avg_acpl = df_long["acpl"].mean()
+
+    player_stats = df_long.groupby("player")["acpl"].mean()
+    best_player = player_stats.idxmin()
+
+    return f"📊 Avg ACPL: {avg_acpl:.2f}", f"🏆 Best Player: {best_player}"
+
+
+# =======================
+# UI DESIGN
+# =======================
+
+with gr.Blocks(theme=gr.themes.Soft()) as app:
+
+    # ===== HEADER =====
+    gr.Markdown(
+        """
+        # ♟️ Chess Performance Dashboard
+        Explore how player performance has evolved over time using ACPL.
+        """
+    )
+
+    # ===== 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 LAYOUT =====
+    with gr.Row():
+
+        # ===== SIDEBAR =====
+        with gr.Column(scale=1):
+            gr.Markdown("## ⚙️ Controls")
+
+            player_dropdown = gr.Dropdown(
+                players,
+                label="Select Player",
+                value=players[0]
+            )
+
+            gr.Markdown("### 💡 Tips")
+            gr.Markdown("""
+            - Lower ACPL = better performance  
+            - Compare players across years  
+            - Observe trends after 1996  
+            """)
+
+        # ===== MAIN CONTENT =====
+        with gr.Column(scale=3):
+
+            with gr.Tabs():
+
+                # -------- OVERVIEW --------
+                with gr.Tab("📈 Overview"):
+                    gr.Markdown("### Overall Performance Trend")
+                    gr.Plot(overview_plot)
+
+                # -------- PLAYER --------
+                with gr.Tab("🏆 Player Analysis"):
+                    output_text = gr.Textbox()
+                    player_plot = gr.Plot()
+
+                    player_dropdown.change(
+                        player_analysis,
+                        inputs=player_dropdown,
+                        outputs=[output_text, player_plot]
+                    )
+
+                # -------- ENGINE --------
+                with gr.Tab("🤖 Engine Impact"):
+                    gr.Plot(engine_plot)
+
+                # -------- PREDICTION --------
+                with gr.Tab("🔮 Prediction"):
+                    gr.Plot(prediction_plot)
+
+# RUN
+app.launch(share=True, debug=True)