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dbd48b2 9b6c5fa dbd48b2 | 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 34 35 36 37 38 39 40 41 42 43 | import streamlit as st
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
# Load and preprocess the data
# Replace 'your_dataset.csv' with the actual file path
data = pd.read_csv('dataset.csv')
vectorizer = TfidfVectorizer()
X = vectorizer.fit_transform(data['text'])
y = data['label']
# Train the model
classifier = LogisticRegression()
classifier.fit(X, y)
# Define the prediction function
def predict(text):
text_vectorized = vectorizer.transform([text])
prediction = classifier.predict(text_vectorized)[0]
if prediction == 'AI':
score = classifier.predict_proba(text_vectorized)[0][0]
else:
score = 1 - classifier.predict_proba(text_vectorized)[0][1]
response = [
{
'label': prediction,
'score': round(float(score), 4)
}
]
return response
# Streamlit app
st.title("AI detector")
# Text input for prediction
text = st.text_area("Enter some text")
# Perform prediction if text is provided
if text:
result = predict(text)
st.json(result)
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