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import gradio as gr
import joblib
# from lightgbm import LGBMClassifier
# from sklearn.feature_extraction.text import TfidfVectorizer
# Load your trained model and vectorizer (assuming they're saved as 'lgbm_model.pkl' and 'vectorizer.pkl')
model = joblib.load('lgbm_model.joblib')
vectorizer = joblib.load('vectorizer.joblib')
def classify_text(text):
# Transform the input text using the loaded vectorizer
text_vector = vectorizer.transform([text])
# Predict using the loaded model
prediction = model.predict(text_vector)
return int(prediction[0])
# Create the Gradio interface
iface = gr.Interface(
fn=classify_text,
inputs=gr.Textbox(lines=2, placeholder="Enter text here..."),
outputs=gr.Label(),
title="Fake News Classifier",
description="Enter text to classify if it's fake (1) or not fake (0).",
examples=["This is a sample news article."]
)
# Launch the interface
if __name__ == "__main__":
iface.launch()
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