| #!/usr/bin/python3 | |
| import pickle | |
| # import numpy as np # linear algebra | |
| # import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv) | |
| # import pandas as pd | |
| # import numpy as np | |
| # import re | |
| # import nltk | |
| # from nltk.corpus import stopwords | |
| # from nltk.stem import WordNetLemmatizer | |
| # from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer, TfidfTransformer | |
| # from sklearn.decomposition import LatentDirichletAllocation | |
| # from sklearn.model_selection import train_test_split | |
| # from sklearn.naive_bayes import MultinomialNB | |
| # from sklearn.metrics import accuracy_score, confusion_matrix | |
| # from sklearn.linear_model import LogisticRegression | |
| # from sklearn.tree import DecisionTreeClassifier | |
| # from sklearn.ensemble import RandomForestClassifier | |
| # from sklearn.pipeline import Pipeline | |
| # from sklearn.model_selection import GridSearchCV | |
| # from sklearn.metrics import classification_report | |
| file_name = 'best_model.pkl' | |
| with open(file_name, 'rb') as file: | |
| model = pickle.load(file) | |
| # ohe = joblib.load('state_ohe.pkl') | |
| class_mapping = ['Music', 'Death', 'Environment', 'Affection'] | |
| class Profit: | |
| def __init__(self,data): | |
| self.data = data | |
| def predict(self): | |
| d_data = [data] | |
| predict = model.predict(d_data)[0] | |
| print(f"This prediction is: {class_mapping[predict-1]}\n") | |
| if __name__ == "__main__": | |
| print("************************") | |
| print("Poem prediction") | |
| print("************************\n\n") | |
| data = input('Enter Poem: ') | |
| obj = Profit(data) | |
| obj.predict() |