import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.naive_bayes import MultinomialNB
import joblib
import gradio as gr
df_true['label'] = 1
df_fake['label'] = 0
df_combined = pd.concat([df_true, df_fake])
X = df_combined['text']
y = df_combined['label']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
tfidf_vectorizer = TfidfVectorizer(max_features=5000)
X_train_tfidf = tfidf_vectorizer.fit_transform(X_train)
X_test_tfidf = tfidf_vectorizer.transform(X_test)
clf = MultinomialNB()
clf.fit(X_train_tfidf, y_train)
accuracy = clf.score(X_test_tfidf, y_test)
print("Model Accuracy:", accuracy)
joblib.dump(clf, 'fake_news_classifier_model.pkl')
joblib.dump(tfidf_vectorizer, 'tfidf_vectorizer.pkl')
def predict_fake_or_true_news(text):
    text_tfidf = tfidf_vectorizer.transform([text])
    prediction = clf.predict(text_tfidf)
    return "True" if prediction[0] == 1 else "Fake"

iface = gr.Interface(
    fn=predict_fake_or_true_news,
    inputs="text",
    outputs="text",
    live=True,
    title="Fake or True News Classifier",
    description="Enter a news article text to classify as 'Fake' or 'True'."
)

iface.launch()