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| import os.path | |
| import pickle | |
| import pandas as pd | |
| from transformers import AutoTokenizer | |
| from sklearn.model_selection import train_test_split | |
| from sklearn.feature_extraction.text import CountVectorizer | |
| from xgboost import XGBClassifier | |
| from sklearn.preprocessing import LabelEncoder | |
| class MentalHealthClassifier: | |
| def __init__(self, data_path, model_path): | |
| self.data = pd.read_csv(data_path, skip_blank_lines=True) | |
| self.data['category'] = ['anxiety' if x == 'axienty' else x for x in self.data['category']] | |
| self.data.dropna(subset=['text'], inplace=True) | |
| self.data.dropna(subset=['clean_text'], inplace=True) | |
| self.data_selected = self.data[['clean_text', 'category']] | |
| self.df = pd.DataFrame(self.data_selected) | |
| self.label_encoder = LabelEncoder() | |
| self.df['category_encoded'] = self.label_encoder.fit_transform(self.df['category']) | |
| self.tokenizer = None | |
| self.vectorizer = CountVectorizer() | |
| self.model_path = model_path | |
| self.model = self.load_model() if os.path.exists(model_path) else XGBClassifier() | |
| def preprocess_data(self): | |
| tokenized_texts = [self.tokenizer.tokenize(text, padding=True, truncation=True) for text in self.df['clean_text']] | |
| X = self.vectorizer.fit_transform([' '.join(tokens) for tokens in tokenized_texts]).toarray() | |
| return X, self.df['category_encoded'] | |
| def train_model(self, X, y): | |
| X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) | |
| self.model.fit(X_train, y_train) | |
| y_pred = self.model.predict(X_test) | |
| return y_test, y_pred | |
| def predict_category(self, input_text): | |
| if self.tokenizer is None: | |
| raise ValueError("Tokenizer not initialized. Call 'initialize_tokenizer' first.") | |
| tokenized_input = self.tokenizer.tokenize(input_text, padding=True, truncation=True) | |
| input_feature_vector = self.vectorizer.transform([' '.join(tokenized_input)]).toarray() | |
| predicted_category_encoded = self.model.predict(input_feature_vector) | |
| predicted_category = self.label_encoder.inverse_transform(predicted_category_encoded) | |
| return predicted_category[0] | |
| def initialize_tokenizer(self, model_name): | |
| self.model_name = model_name | |
| self.tokenizer = AutoTokenizer.from_pretrained(self.model_name) | |
| def save_model(self): | |
| print("saving model...to pickle...") | |
| with open(self.model_path, 'wb') as f: | |
| pickle.dump(self.model, f) | |
| def load_model(self): | |
| print("loading model...from pickle...") | |
| with open(self.model_path, 'rb') as f: | |
| return pickle.load(f) | |
| if __name__ == "__main__": | |
| tokenizer_model_name = "nlptown/bert-base-multilingual-uncased-sentiment" | |
| data_path = 'data.csv' | |
| model_path = 'mental_health_model.pkl' | |
| mental_classifier = MentalHealthClassifier(data_path, model_path) | |
| if not os.path.exists(model_path): | |
| mental_classifier.initialize_tokenizer(tokenizer_model_name) | |
| X, y = mental_classifier.preprocess_data() | |
| y_test, y_pred = mental_classifier.train_model(X, y) | |
| mental_classifier.save_model() | |
| else: | |
| mental_classifier.load_model() | |
| mental_classifier.initialize_tokenizer(tokenizer_model_name) # Ensure tokenizer is initialized if loading model from pickle | |
| mental_classifier.preprocess_data() | |
| # input_text = "I feel anxiety whenever i am doing nothing." | |
| # predicted_category = mental_classifier.predict_category(input_text) | |
| # print("Predicted mental health condition:", predicted_category) | |