Add Plotly Express

app.py

@@ -21,11 +21,23 @@ import gradio as gr
 import numpy as np
 import numpy.typing as npt
 import pandas as pd
+import plotly.express as px
+from plotly.graph_objects import Figure
+
+
+def visualize(df_pairwise: pd.DataFrame) -> Figure:
+    fig = px.imshow(df_pairwise, color_continuous_scale='RdBu', text_auto='.2f')
+
+    fig.update_layout(xaxis_title='Loser', yaxis_title='Winner', xaxis_side='top')
+
+    fig.update_traces(hovertemplate='Winner: %{y}<br>Loser: %{x}<br>Fraction of Wins: %{z}<extra></extra>')
+
+    return fig
 
 
 # https://gist.github.com/dustalov/41678b70c40ba5a55430fa5e77b121d9#file-newman-py
 def aggregate(wins: npt.NDArray[np.int64], ties: npt.NDArray[np.int64],
-              seed: int = 0, tolerance: float = 10e-6, limit: int = 20) -> npt.ArrayLike:
+              seed: int = 0, tolerance: float = 10e-6, limit: int = 20) -> npt.NDArray[np.float64]:
     assert wins.shape == ties.shape, 'wins and ties shapes are different'
 
     rng = np.random.default_rng(seed)
@@ -71,7 +83,7 @@ def aggregate(wins: npt.NDArray[np.int64], ties: npt.NDArray[np.int64],
     return pi
 
 
-def handler(file: typing.IO[bytes], seed: int) -> pd.DataFrame:
+def handler(file: typing.IO[bytes], seed: int) -> typing.Tuple[pd.DataFrame, Figure]:
     if file is None:
         raise gr.Error('File must be uploaded')
 
@@ -114,7 +126,12 @@ def handler(file: typing.IO[bytes], seed: int) -> pd.DataFrame:
     df_result.sort_values(by=['rank', 'score'], ascending=[True, False], inplace=True)
     df_result.reset_index(inplace=True)
 
-    return df_result
+    df_pairwise = pd.DataFrame(data=scores[:, np.newaxis] / (scores + scores[:, np.newaxis]),
+                               index=index, columns=index)
+
+    fig = visualize(df_pairwise)
+
+    return df_result, fig
 
 
 def main() -> None:
@@ -131,10 +148,15 @@ def main() -> None:
                 precision=0
             )
         ],
-        outputs=gr.Dataframe(
-            headers=['item', 'score', 'rank'],
-            label='Ranking'
-        ),
+        outputs=[
+            gr.Dataframe(
+                headers=['item', 'score', 'rank'],
+                label='Ranking'
+            ),
+            gr.Plot(
+                label='Chances of Winning the Comparison'
+            )
+        ],
         title='Pair2Rank: Turn Your Side-by-Side Comparisons into Ranking!',
         description='''
 This easy-to-use tool transforms pairwise comparisons (aka side-by-side) to a meaningful ranking of items.