Update app.py, score_db.py, rating.py, and requirements.txt

rating.py

@@ -0,0 +1,234 @@
+import math 
+
+import pandas as pd 
+import numpy as np
+
+
+"""
+compute_mle_elo function is a modified version of the original code from the following source:
+https://github.com/lm-sys/FastChat
+https://colab.research.google.com/drive/1RAWb22-PFNI-X1gPVzc927SGUdfr6nsR?usp=sharing#scrollTo=cbKV7-s9ZA_5
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+"""
+
+def compute_mle_elo(df, SCALE=400, BASE=10, INIT_RATING=1000):
+    from sklearn.linear_model import LogisticRegression
+    models = pd.concat([df["model_a"], df["model_b"]]).unique()
+    models = pd.Series(np.arange(len(models)), index=models)
+    p = len(models.index)
+    n = df.shape[0]
+
+    X = np.zeros([n, p])
+    X[np.arange(n), models[df["model_a"]]] = +math.log(BASE)
+    X[np.arange(n), models[df["model_b"]]] = -math.log(BASE)
+
+    Y = np.zeros(n)
+    Y[df["winner"] == "model_a"] = 1.0
+
+    lr = LogisticRegression(fit_intercept=False)
+    lr.fit(X,Y)
+
+    elo_scores = SCALE * lr.coef_[0] + INIT_RATING
+
+    return pd.Series(elo_scores, index = models.index).sort_values(ascending=False)