Update app.py

app.py

@@ -1,12 +1,13 @@
 import gradio as gr
 import pickle
-import statsmodels.api as sm
 import pandas as pd
 import numpy as np
 import plotly.express as px
+import plotly.graph_objects as go
+from functools import lru_cache
 
 # =======================
-# LOAD + PREP DATA
+# LOAD DATA (ONCE)
 # =======================
 df = pd.read_csv("chess_analysis.csv")
 
@@ -30,188 +31,151 @@ df_long["engine"] = np.where(df_long["year"] >= 1996, "Post-1996", "Pre-1996")
 players = sorted(df_long["player"].unique().tolist())
 
 # =======================
-# FUNCTIONS
+# LOAD MODEL ONCE
+# =======================
+with open("acpl_trend_model.pkl", "rb") as f:
+    model = pickle.load(f)
+
+# =======================
+# PRECOMPUTE (VERY IMPORTANT)
+# =======================
+df_sorted = df_long.sort_values("year")
+
+df_sorted["pred"] = model.predict(
+    pd.DataFrame({"const": 1, "year": df_sorted["year"]})
+)
+
+future_years = pd.DataFrame({
+    "year": np.arange(df_long["year"].max(), df_long["year"].max() + 10)
+})
+future_years["pred"] = model.predict(
+    pd.DataFrame({"const": 1, "year": future_years["year"]})
+)
+
+# Downsample for fast rendering
+df_sample = df_long.sample(min(len(df_long), 1000))
+
+# =======================
+# FUNCTIONS (OPTIMIZED)
 # =======================
 
 def overview_plot():
     fig = px.scatter(
-        df_long,
+        df_sample,
         x="year",
         y="acpl",
         opacity=0.3,
-        title="📈 Performance Over Time"
+        render_mode="webgl"  # FAST
+    )
+    fig.add_scatter(
+        x=df_sorted["year"],
+        y=df_sorted["pred"],
+        mode="lines",
+        name="Trend"
     )
     fig.update_layout(template="plotly_white")
     return fig
 
 
+@lru_cache(maxsize=128)
 def player_analysis(player):
     data = df_long[df_long["player"] == player]
 
     avg = data["acpl"].mean()
     games = len(data)
 
+    # Downsample
+    data = data.sample(min(len(data), 200))
+
     fig = px.scatter(
         data,
         x="year",
         y="acpl",
-        title=f"{player} Performance",
+        render_mode="webgl"
     )
 
     return f"Avg ACPL: {avg:.2f} | Games: {games}", fig
 
 
 def engine_plot():
+    data = df_sample
+
     fig = px.scatter(
-        df_long,
+        data,
         x="year",
         y="acpl",
         color="engine",
         opacity=0.3,
-        title="🤖 Engine Effect (Pre vs Post 1996)"
+        render_mode="webgl"
     )
     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"],
+        x=df_sample["year"],
+        y=df_sample["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)"
+        name="Trend"
     ))
 
-    # 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"
+        name="Future"
     ))
 
-    fig.update_layout(
-        title="🔮 Future Performance Trend",
-        xaxis_title="Year",
-        yaxis_title="ACPL",
-        template="plotly_white"
-    )
-
+    fig.update_layout(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()
+    best_player = df_long.groupby("player")["acpl"].mean().idxmin()
 
     return f"📊 Avg ACPL: {avg_acpl:.2f}", f"🏆 Best Player: {best_player}"
 
 
 # =======================
-# UI DESIGN
+# UI
 # =======================
 
 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.
-        """
-    )
+    gr.Markdown("# ♟️ Chess Performance Dashboard")
 
-    # ===== 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 =====
+            player_dropdown = gr.Dropdown(players, value=players[0])
+
         with gr.Column(scale=3):
 
             with gr.Tabs():
 
-                # -------- OVERVIEW --------
-                with gr.Tab("📈 Overview"):
-                    gr.Markdown("### Overall Performance Trend")
+                with gr.Tab("Overview"):
                     gr.Plot(overview_plot)
 
-                # -------- PLAYER --------
-                with gr.Tab("🏆 Player Analysis"):
+                with gr.Tab("Player"):
                     output_text = gr.Textbox()
                     player_plot = gr.Plot()
 
@@ -221,12 +185,10 @@ with gr.Blocks(theme=gr.themes.Soft()) as app:
                         outputs=[output_text, player_plot]
                     )
 
-                # -------- ENGINE --------
-                with gr.Tab("🤖 Engine Impact"):
+                with gr.Tab("Engine"):
                     gr.Plot(engine_plot)
 
-                # -------- PREDICTION --------
-                with gr.Tab("🔮 Prediction"):
+                with gr.Tab("Prediction"):
                     gr.Plot(prediction_plot)
 
 # RUN