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1ba8298 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 | import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.naive_bayes import MultinomialNB
import gradio as gr
# Load dataset
df = pd.read_csv("spam.csv", encoding='latin-1')[['v1', 'v2']]
df.columns = ['label', 'message']
df['label'] = df['label'].map({'ham': 0, 'spam': 1})
# Training
X = df['message']
y = df['label']
vectorizer = TfidfVectorizer(stop_words='english')
X_vec = vectorizer.fit_transform(X)
model = MultinomialNB()
model.fit(X_vec, y)
# Fungsi prediksi
def predict_spam(text):
text_vec = vectorizer.transform([text])
pred = model.predict(text_vec)[0]
return "SPAM ❌" if pred else "HAM ✅ (Bukan Spam)"
# UI Gradio
gr.Interface(
fn=predict_spam,
inputs=gr.Textbox(lines=4, placeholder="Masukkan pesan..."),
outputs="text",
title="Spam Message Classifier",
description="Model klasifikasi pesan spam menggunakan Naive Bayes dan TF-IDF"
).launch()
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