import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
import streamlit as st

data = {
    "text": [
        "NASA confirms water on Mars",
        "You won't believe what this actor did!",
        "Government passes new education bill",
        "Scientists find cure for cancer in lemons",
        "President addresses the nation",
        "Shocking! Aliens spotted in Delhi",
        "Stock market hits all-time high",
        "Celebrity marries alien in secret ceremony"
    ],
    "label": ["real", "fake", "real", "fake", "real", "fake", "real", "fake"]
}

df = pd.DataFrame(data)
df

X = df["text"]
y = df["label"]

vectorizer = TfidfVectorizer()
X_vec = vectorizer.fit_transform(X)

X_train, X_test, y_train, y_test = train_test_split(X_vec, y, test_size=0.25, random_state=42)

model = LogisticRegression()
model.fit(X_train, y_train)

y_pred = model.predict(X_test)

print("Accuracy:", accuracy_score(y_test, y_pred))

Accuracy: 0.0

sample = ["Aliens built the pyramids"]

sample_vec = vectorizer.transform(sample)

print("Prediction:", model.predict(sample_vec)[0])

Prediction: real

st.title("Fake News Detector")
user_input = st.text_area("Enter news headline:")

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  Warning: to view this Streamlit app on a browser, run it with the following
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    streamlit run C:\Users\HP\miniconda3\envs\py312\Lib\site-packages\ipykernel_launcher.py [ARGUMENTS]
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if st.button("Check"):
    vec_input = vectorizer.transform([user_input])
    prediction = model.predict(vec_input)[0]
    st.write(f"### Prediction: `{prediction.upper()}`")

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