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import pandas as pd
import os
from tensorflow.keras.models import load_model
from joblib import load
# Function to predict gender based on a name
def predict_gender(name, model, tfidf):
vectorized_name = tfidf.transform([name]).toarray() # Transform name into feature vector
gender = model.predict(vectorized_name) > 0.5 # Get prediction
return 'Male' if gender[0][0] == 1 else 'Female'
# Load the pre-trained model
model = load_model('gender_prediction_model.h5')
# Check if the TF-IDF vectorizer file exists
tfidf_vectorizer_file = 'tfidf_vectorizer.joblib'
if not os.path.exists(tfidf_vectorizer_file):
raise FileNotFoundError(f"{tfidf_vectorizer_file} not found. Please ensure the file exists in the current directory.")
# Load the TF-IDF vectorizer
tfidf = load(tfidf_vectorizer_file)
# Main loop to take user input for predictions
while True:
name = input("Enter a name to predict gender (or type 'exit' to quit): ")
if name.lower() == 'exit':
break
predicted_gender = predict_gender(name, model, tfidf)
print(f"The predicted gender for '{name}' is: {predicted_gender}")