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import streamlit as st
import pandas as pd
import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
import joblib
# Title and Description of the App
st.title("Human vs LLM-Generated Text Differentiator")
st.write("This app predicts whether a given text is human-written or generated by a language model (LLM).")
# Step 1: Upload Dataset
st.header("Step 1: Upload the RoFT Dataset")
uploaded_file = st.file_uploader("Upload your roft.csv file", type="csv")
if uploaded_file is not None:
# Load dataset
data = pd.read_csv(uploaded_file)
st.write("Dataset Loaded Successfully!")
# Display the first few rows of the dataset
st.subheader("Sample of the Dataset:")
st.dataframe(data.head())
# Preprocessing the data
st.header("Step 2: Preprocess the Data")
# Combine prompt_body and gen_body to form the complete text
data['text'] = data['prompt_body'].fillna('') + ' ' + data['gen_body'].fillna('')
data['label'] = data['true_boundary_index'].apply(lambda x: 1 if x == 9 else 0) # 1 = Human, 0 = LLM
st.write("Data Preprocessing Complete!")
# Show distribution of labels
st.subheader("Label Distribution:")
st.bar_chart(data['label'].value_counts())
# Feature Extraction
st.header("Step 3: Train the Model")
st.write("Extracting features using TF-IDF and training a Random Forest classifier.")
# TF-IDF Vectorization
vectorizer = TfidfVectorizer(max_features=5000)
X = vectorizer.fit_transform(data['text']).toarray()
y = data['label']
# Train-Test Split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Train a Random Forest Classifier
model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(X_train, y_train)
# Evaluate the model
y_pred = model.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
st.write(f"Model Accuracy: {accuracy * 100:.2f}%")
# Save the model and vectorizer
joblib.dump(model, 'text_classifier.pkl')
joblib.dump(vectorizer, 'vectorizer.pkl')
st.success("Model Trained and Saved Successfully!")
# Step 4: User Input for Prediction
st.header("Step 4: Predict Human vs LLM-Generated Text")
# Load the trained model and vectorizer
model = joblib.load('text_classifier.pkl')
vectorizer = joblib.load('vectorizer.pkl')
# Input text from the user
user_input = st.text_area("Enter the text you want to classify:")
if st.button("Predict"):
if user_input.strip():
# Vectorize the input text
input_vector = vectorizer.transform([user_input]).toarray()
# Predict and show the result
prediction = model.predict(input_vector)
confidence = model.predict_proba(input_vector).max() * 100
if prediction[0] == 1:
st.success(f"The text is likely **Human-Written** with a confidence of {confidence:.2f}%.")
else:
st.warning(f"The text is likely **LLM-Generated** with a confidence of {confidence:.2f}%.")
else:
st.error("Please enter some text for prediction.")
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