Create app.py

app.py

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+import pandas as pd
+import numpy as np
+import seaborn as sns
+import matplotlib.pyplot as plt
+import gradio as gr
+import warnings
+warnings.filterwarnings("ignore")
+
+url='https://drive.google.com/file/d/1VfCaU5vFVWsSYrvKQF2x9iS6I7KoWEVH/view?usp=share_link'
+url='https://drive.google.com/uc?id=' + url.split('/')[-2]
+df = pd.read_csv(url)
+
+df['cf_rating']=df['cf_rating'].astype(str)
+df['cc_rating']=df['cc_rating'].astype(str)
+df['cf_username']=df['cf_username'].astype(str)
+df['cc_username']=df['cc_username'].astype(str)
+df['ss_username']=df['ss_username'].astype(str)
+
+only_cc = df[(df['cf_username'] == "nan")]
+only_cf = df[(df['cc_username'] == "nan")]
+both_cc_cf = df[(df['cf_username'] != "nan") & (df['cf_rating'] != "nan") & (df['cc_username'] != "nan") & (df['cc_rating'] != "nan")]
+both_cc_cf.drop(columns=['ss_username','cf_username','cc_username'],inplace=True)
+
+
+def linear_regression(X, y):
+    #calculating mean
+    mean_x = np.mean(X)
+    mean_y = np.mean(y)
+    
+    n = 0
+    d = 0
+    for i in range(len(X)):
+        n += (X[i] - mean_x) * (y[i] - mean_y)
+        d += (X[i] - mean_x) ** 2
+    #calculating weights and bias
+    w = n/d
+    b = mean_y-(w * mean_x)
+  
+    return (b[0], w[0])
+
+X=both_cc_cf['cc_rating'].values.reshape(-1,1).astype(float)
+Y=both_cc_cf['cf_rating'].values.reshape(-1,1).astype(float)
+
+b_cc, w_cc = linear_regression(X,Y)
+b_cf, w_cf = linear_regression(Y,X)
+
+def predict_rating(platform, current_rating):
+    if platform == "CodeChef":
+        predicted_rating = current_rating * w_cc + b_cc
+    else:
+        predicted_rating = current_rating * w_cf + b_cf
+    return int(predicted_rating)
+
+interface = gr.Interface(
+    fn=predict_rating,
+    inputs=[
+        gr.Radio(choices=["CodeChef", "Codeforces"], label="Platform"),
+        gr.Number(label="Current Rating")
+    ],
+    outputs="number",
+    title="CodeChef and Codeforces Rating Predictor",
+)
+
+interface.launch()