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import streamlit as st
import os
import pandas as pd
from tensorflow.keras.models import load_model
from joblib import load
# Set Streamlit page configuration
st.set_page_config(page_title="Gender Prediction", page_icon="🧑🎓", layout="centered")
# Load the pre-trained model
@st.cache_resource
def load_prediction_model():
return load_model('gender_prediction_model.h5')
# Load the TF-IDF vectorizer
@st.cache_resource
def load_vectorizer():
tfidf_vectorizer_file = 'tfidf_vectorizer.joblib'
if not os.path.exists(tfidf_vectorizer_file):
st.error(f"❌ {tfidf_vectorizer_file} not found. Please ensure the file exists in the current directory.")
st.stop()
return load(tfidf_vectorizer_file)
# Prediction function
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 model and vectorizer
model = load_prediction_model()
tfidf = load_vectorizer()
# Streamlit UI
st.title("Gender Prediction from Name")
st.write("Enter a name to predict the gender using the pre-trained model.")
# Input form
name = st.text_input("Enter a name:")
if st.button("Predict"):
if name:
predicted_gender = predict_gender(name, model, tfidf)
st.success(f"The predicted gender for '{name}' is: **{predicted_gender}**")
else:
st.warning("Please enter a valid name.")
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