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from sklearn.feature_extraction.text import CountVectorizer
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import MultinomialNB
from sklearn.metrics import accuracy_score, classification_report
from pandas import *
import pickle
import sys
# Step 1: Data Preparation
# Assuming you have 'text' and 'label' columns in your dataset.
# Load your data into a pandas DataFrame or use any other data loading method.
# For this example, we'll use a simple list of reviews and labels.
filename = 'classifier.bin'
def train():
data = read_csv("train.csv")
text = data['text'].tolist()
label = data['label'].tolist()
# Step 2: Text Preprocessing and Feature Extraction
vectorizer = CountVectorizer()
X = vectorizer.fit_transform(text)
# Step 4: Split Data
X_train, X_test, y_train, y_test = train_test_split(X, label, test_size=0.2, random_state=42)
# Step 5: Create and Train the Classifier
classifier = MultinomialNB()
classifier.fit(X_train, y_train)
# Step 6: Evaluate the Classifier
y_pred = classifier.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
report = classification_report(y_test, y_pred)
print(f"Accuracy: {accuracy}")
print("Classification Report:\n", report)
# save the model to disk
pickle.dump(classifier, open(filename, 'wb'))
pickle.dump(vectorizer, open('vectorizer.pkl', 'wb'))
def classify(text):
classifier = pickle.load(open(filename, 'rb'))
vectorizer = pickle.load(open('vectorizer.pkl', 'rb'))
# New text you want to classify
new_text = [text]
# Preprocess and convert new text into numerical features using the same vectorizer
new_text_features = vectorizer.transform(new_text)
# Use the trained classifier to predict the label
predicted_label = classifier.predict(new_text_features)
return predicted_label[0]
#print(f"Predicted Label: {predicted_label[0]}")
if __name__ == '__main__':
train()
classification = input("would you like to classify(Y/n)?\n")
if classification.upper() == "Y":
print(classify("I like turtles, do you?"))
else:
pass
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