Update app.py

app.py

@@ -10,7 +10,7 @@ import pickle
 loaded_model = joblib.load('./updated.joblib_')
 
 def predict_rank(NATIONAL,Institution_name, Academic_Reputation_score, Academic_Reputation_rank, Employer_Reputation_score, Employer_Reputation_rank, Faculty_Student_score, Faculty_Student_rank, Citations_per_Faculty_score, Citations_per_Faculty_rank, International_Faculty_score, International_Students_score, International_Students_rank):
-    input_data = np.array([NATIONAL, Institute_name, Academic_Reputation_score, Academic_Reputation_rank, Employer_Reputation_score, Employer_Reputation_rank, Faculty_Student_score, Faculty_Student_rank, Citations_per_Faculty_score, Citations_per_Faculty_rank, International_Faculty_score, International_Students_score,International_Students_rank]).reshape(1, -1)
+    input_data = np.array([NATIONAL, Institution_name, Academic_Reputation_score, Academic_Reputation_rank, Employer_Reputation_score, Employer_Reputation_rank, Faculty_Student_score, Faculty_Student_rank, Citations_per_Faculty_score, Citations_per_Faculty_rank, International_Faculty_score, International_Students_score,International_Students_rank]).reshape(1, -1)
     predict_score = loaded_model.predict(input_data)
     return f"Predicted Rank: {np.round(predict_score[0], 2)}"