Add some example data and script to load and compute matches

app.py

@@ -0,0 +1,14 @@
+import json
+
+import requests
+
+from datasets import load_dataset
+import gradio as gr
+from huggingface_hub import HfApi, hf_hub_download
+from huggingface_hub.repocard import metadata_load
+import pandas as pd
+from matchmaking import *
+
+
+block = gr.Blocks()
+

match_history/2022-12-21_11-07-43_445125.csv

@@ -0,0 +1,131 @@
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match_history/2022-12-21_11-08-46_636491.csv

@@ -0,0 +1,131 @@
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match_history/2022-12-21_11-09-01_275927.csv

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matchmaking.py

@@ -0,0 +1,50 @@
+import random
+import pandas as pd
+import os
+
+
+class Model:
+    def __init__(self, name, elo):
+        self.name = name
+        self.elo = elo
+        self.games_played = 0
+
+
+class Matchmaking:
+    def __init__(self):
+        self.models = []
+        self.queue = []
+        self.start_elo = 1200
+        self.k = 20
+        self.max_diff = 500
+        self.matches = pd.DataFrame()
+
+    def read_history(self):
+        path = "match_history"
+        files = os.listdir(path)
+        for file in files:
+            self.matches = pd.concat([self.matches, pd.read_csv(os.path.join(path, file))], ignore_index=True)
+        self.matches["datetime"] = pd.to_datetime(self.matches["datetime"], format="%Y-%m-%d %H:%M:%S.%f", errors="coerce")
+        self.matches = self.matches.dropna()
+        self.matches = self.matches.sort_values("datetime")
+        self.matches.reset_index(drop=True, inplace=True)
+        model_names = self.matches["model1"].unique()
+        self.models = [Model(name, self.start_elo) for name in model_names]
+
+    def compute_elo(self):
+        for i, row in self.matches.iterrows():
+            model1 = self.get_model(row["model1"])
+            model2 = self.get_model(row["model2"])
+            result = row["result"]
+            delta = model1.elo - model2.elo
+            win_probability = 1 / (1 + 10 ** (-delta / 500))
+            model1.elo += self.k * (result - win_probability)
+            model2.elo -= self.k * (result - win_probability)
+            model1.games_played += 1
+            model2.games_played += 1
+
+    def get_model(self, name):
+        for model in self.models:
+            if model.name == name:
+                return model
+        return None