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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.") | |