def main(): """ Creates a Streamlit web app that classifies a given body of text as either human-made or AI-generated, using a pre-trained model. """ import streamlit as st import numpy as np import joblib import string import time import scipy import spacy import re from transformers import AutoTokenizer import torch from eli5.lime import TextExplainer from eli5.lime.samplers import MaskingTextSampler import eli5 import shap from custom_models import HF_DistilBertBasedModelAppDocs, HF_BertBasedModelAppDocs # Initialize Spacy nlp = spacy.load("en_core_web_sm") # device to run DL model device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') def format_text(text: str) -> str: """ This function takes a string as input and returns a formatted version of the string. The function replaces specific substrings in the input string with empty strings, converts the string to lowercase, removes any leading or trailing whitespace, and removes any punctuation from the string. """ text = nlp(text) text = " ".join([token.text for token in text if token.ent_type_ not in ["PERSON", "DATE"]]) pattern = r"\b[A-Za-z]+\d+\b" text = re.sub(pattern, "", text) return text.replace("REDACTED", "").lower().replace("[Name]", "").replace("[your name]", "").\ replace("dear admissions committee,", "").replace("sincerely,","").\ replace("[university's name]","fordham").replace("dear sir/madam,","").\ replace("– statement of intent ","").\ replace('program: master of science in data analytics name of applicant: ',"").\ replace("data analytics", "data science").replace("| \u200b","").\ replace("m.s. in data science at lincoln center ","").\ translate(str.maketrans('', '', string.punctuation)).strip().lstrip() # Define the function to classify text def nb_lr(model, text): # Clean and format the input text text = format_text(text) # Predict using either LR or NB and get prediction probability prediction = model.predict([text]).item() predict_proba = round(model.predict_proba([text]).squeeze()[prediction].item(),4) return prediction, predict_proba def torch_pred(tokenizer, model, text): # DL models (BERT/DistilBERT based models) cleaned_text_tokens = tokenizer([text], padding='max_length', max_length=512, truncation=True) with torch.inference_mode(): input_ids, att = cleaned_text_tokens["input_ids"], cleaned_text_tokens["attention_mask"] input_ids = torch.tensor(input_ids).to(device) attention_mask = torch.tensor(att).to(device) logits = model(input_ids=input_ids, attention_mask=attention_mask) _, prediction = torch.max(logits, 1) prediction = prediction.item() predict_proba = round(torch.softmax(logits, 1).cpu().squeeze().tolist()[prediction],4) return prediction, predict_proba def pred_str(prediction): # Map the predicted class to string output if prediction == 0: return "Human-made 🤷‍♂️🤷‍♀️" else: return "Generated with AI 🦾" @st.cache(allow_output_mutation=True, suppress_st_warning=True) def load_tokenizer(option): if option == "BERT-based model": tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased", padding='max_length', max_length=512, truncation=True) else: tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased", padding='max_length', max_length=512, truncation=True) return tokenizer @st.cache(allow_output_mutation=True, suppress_st_warning=True) def load_model(option): if option == "BERT-based model": model = HF_BertBasedModelAppDocs.from_pretrained("ferdmartin/HF_BertBasedModelAppDocs").to(device) else: model = HF_DistilBertBasedModelAppDocs.from_pretrained("ferdmartin/HF_DistilBertBasedModelAppDocs").to(device) return model # Streamlit app: models_available = {"Logistic Regression":"models/baseline_model_lr2.joblib", "Naive Bayes": "models/baseline_model_nb2.joblib", "DistilBERT-based model (BERT light)": "ferdmartin/HF_DistilBertBasedModelAppDocs", "BERT-based model": "ferdmartin/HF_BertBasedModelAppDocs" } st.set_page_config(page_title="AI/Human GradAppDocs", page_icon="📝🦾", layout="wide") st.title("Academic Application Document Classifier") st.header("Is it human-made 📝 or Generated with AI 🤖 ? ") # Check the model to use def restore_prediction_state(): if "prediction" in st.session_state: del st.session_state.prediction option = st.selectbox("Select a model to use:", models_available, on_change=restore_prediction_state) # Load the selected trained model if option in ("BERT-based model", "DistilBERT-based model (BERT light)"): tokenizer = load_tokenizer(option) model = load_model(option) else: model = joblib.load(models_available[option]) text = st.text_area("Enter either a statement of intent or a letter of recommendation:") #Hide footer "made with streamlit" hide_st_style = """ """ st.markdown(hide_st_style, unsafe_allow_html=True) # Use model if st.button("Let's check this text!"): if text.strip() == "": st.error("Please enter some text") else: with st.spinner("Wait for the magic 🪄🔮"): # Use model if option in ("Naive Bayes", "Logistic Regression"): prediction, predict_proba = nb_lr(model, text) st.session_state["sklearn"] = True else: prediction, predict_proba = torch_pred(tokenizer, model, text) st.session_state["torch"] = True # Store the result in session state st.session_state["color_pred"] = "blue" if prediction == 0 else "red" prediction = pred_str(prediction) st.session_state["prediction"] = prediction st.session_state["predict_proba"] = predict_proba st.session_state["text"] = text # Print result st.markdown(f"I think this text is: **:{st.session_state['color_pred']}[{st.session_state['prediction']}]** (Prediction probability: {st.session_state['predict_proba'] * 100}%)") elif "prediction" in st.session_state: # Display the stored result if available st.markdown(f"I think this text is: **:{st.session_state['color_pred']}[{st.session_state['prediction']}]** (Prediction probability: {st.session_state['predict_proba'] * 100}%)") if st.button("Model Explanation"): # Check if there's text in the session state if "text" in st.session_state and "prediction" in st.session_state: if option in ("Naive Bayes", "Logistic Regression"): with st.spinner('Wait for it 💭...'): explainer = TextExplainer(sampler=MaskingTextSampler()) explainer.fit(st.session_state["text"], model.predict_proba) html = eli5.format_as_html(explainer.explain_prediction(target_names=["Human", "AI"])) else: with st.spinner('Wait for it 💭... BERT-based model explanations take around 4-10 minutes. In case you want to abort, refresh the page.'): # TORCH EXPLAINER PRED FUNC (USES logits) def f(x): tv = torch.tensor([tokenizer.encode(v, padding='max_length', max_length=512, truncation=True) for v in x])#.cuda() outputs = model(tv).detach().cpu().numpy() scores = (np.exp(outputs).T / np.exp(outputs).sum(-1)).T val = scipy.special.logit(scores[:,1]) # use one vs rest logit units return val # build an explainer using a token masker explainer = shap.Explainer(f, tokenizer) shap_values = explainer([st.session_state["text"]], fixed_context=1) html = shap.plots.text(shap_values, display=False) # Render HTML st.components.v1.html(html, height=500, scrolling = True) else: st.error("Please enter some text and click 'Let's check!' before requesting an explanation.") if __name__ == "__main__": main()