initial leaderboard

app.py

@@ -2,31 +2,81 @@ import gradio as gr
 import pandas as pd
 import matplotlib.pyplot as plt
 import numpy as np
+import plotly.graph_objects as go
 
 df = pd.read_csv("results.csv").dropna()
 dataset_columns = df.columns[7:].tolist()
 
 
-def create_radar_plot(df, model_name):
-    model_data = df[df["Model"] == model_name].iloc[0]
-    metrics = df.columns[7:].tolist()
+# def create_radar_plot(df, model_name):
+#     model_data = df[df["Model"] == model_name].iloc[0]
+#     datasets = df.columns[7:].tolist()
 
-    values = [model_data[m] for m in metrics]
-    angles = np.linspace(0, 2 * np.pi, len(metrics), endpoint=False)
+#     values = [model_data[m] for m in datasets]
+#     angles = np.linspace(0, 2 * np.pi, len(datasets), endpoint=False)
 
-    # Close the plot by appending values to match angles length
-    angles = np.concatenate((angles, [angles[0]]))  # Add first angle again
-    values = np.concatenate((values, [values[0]]))  # Add first value again
+#     # Close the plot by appending values to match angles length
+#     angles = np.concatenate((angles, [angles[0]]))  # Add first angle again
+#     values = np.concatenate((values, [values[0]]))  # Add first value again
 
-    fig, ax = plt.subplots(figsize=(10, 10), subplot_kw=dict(projection="polar"))
-    ax.plot(angles, values)
-    ax.fill(angles, values, alpha=0.25)
-    ax.set_xticks(angles[:-1])  # Exclude the last duplicate angle
-    ax.set_xticklabels(metrics, size=8)
-    ax.set_title(model_name)
+#     fig, ax = plt.subplots(figsize=(10, 10), subplot_kw=dict(projection="polar"))
+#     ax.plot(angles, values)
+#     ax.fill(angles, values, alpha=0.25)
+#     ax.set_xticks(angles[:-1])  # Exclude the last duplicate angle
+#     ax.set_xticklabels(datasets, size=8, fontweight="bold")
+#     ax.set_title(model_name, pad=5, y=1.05, fontsize=12, fontweight="bold")
 
-    return fig
+#     return fig
 
+def create_radar_plot(df, model_name):
+    model_data = df[df["Model"] == model_name].iloc[0]
+    datasets = df.columns[7:].tolist()
+    values = [model_data[m] for m in datasets]
+    values.append(values[0])
+    datasets.append(datasets[0])
+
+    fig = go.Figure(
+        data=go.Scatterpolar(
+            r=values,
+            theta=datasets,
+            fill="toself",
+            fillcolor="rgba(99, 102, 241, 0.3)",
+            line=dict(color="#4F46E5", width=2),
+            name=model_name,
+            text=[f"{val:.3f}" for val in values],
+            textposition="middle right",
+            mode="lines+markers+text",
+        )
+    )
+
+    fig.update_layout(
+        polar=dict(
+            radialaxis=dict(
+                visible=True,
+                range=[0, 1],
+                showline=False,
+                tickfont=dict(size=12),
+            ),
+            angularaxis=dict(
+                tickfont=dict(size=13, family="Arial"),
+                rotation=90,
+                direction="clockwise",
+            ),
+        ),
+        showlegend=False,
+        title=dict(
+            text=model_name,
+            x=0.5,
+            y=0.95,
+            font=dict(size=24, family="Arial", color="#1F2937"),
+        ),
+        paper_bgcolor="rgba(0,0,0,0)",
+        plot_bgcolor="rgba(0,0,0,0)",
+        height=800,
+        width=1000,
+    )
+
+    return fig
 
 def model_info_tab(model_name=None):
     if model_name is None:
@@ -202,7 +252,7 @@ with gr.Blocks() as app:
                         value=df.sort_values("Model Avg", ascending=False).iloc[0][
                             "Model"
                         ],
-                        label="Select Model",
+                        label="Model",
                     )
                 with gr.Column(scale=4):
                     model_info = gr.HTML()

requirements.txt

@@ -1,2 +1,3 @@
 pandas
-matplotlib
+matplotlib
+plotly