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import streamlit as st
import tensorflow as tf
import numpy as np
import nltk
import os
from nltk.tokenize import sent_tokenize
from transformers import DistilBertTokenizerFast, TFDistilBertForSequenceClassification
# 📁 Use safe cache directory inside Hugging Face or Docker
os.environ["TRANSFORMERS_CACHE"] = "/tmp/huggingface"
# 📥 Download NLTK tokenizer
nltk_data_path = "/tmp/nltk_data"
nltk.download("punkt_tab", download_dir=nltk_data_path)
nltk.data.path.append(nltk_data_path)
# 🔄 Load tokenizer and model from Hugging Face
tokenizer = DistilBertTokenizerFast.from_pretrained(
"distilbert-base-uncased", cache_dir="/tmp/huggingface"
)
model = TFDistilBertForSequenceClassification.from_pretrained(
"sundaram07/distilbert-sentence-classifier", cache_dir="/tmp/huggingface"
)
# 🔮 Predict AI probability for a sentence
def predict_sentence_ai_probability(sentence):
inputs = tokenizer(sentence, return_tensors="tf", truncation=True, padding=True)
outputs = model(inputs)
logits = outputs.logits
prob_ai = tf.sigmoid(logits)[0][0].numpy()
return prob_ai
# 📊 Analyze all sentences
def predict_ai_generated_percentage(text, threshold=0.15):
text = text.strip()
sentences = sent_tokenize(text)
ai_sentence_count = 0
results = []
for sentence in sentences:
prob = predict_sentence_ai_probability(sentence)
is_ai = prob <= threshold
results.append((sentence, prob, is_ai))
if is_ai:
ai_sentence_count += 1
total_sentences = len(sentences)
ai_percentage = (ai_sentence_count / total_sentences) * 100 if total_sentences > 0 else 0.0
return ai_percentage, results
# 🌐 Streamlit Web App
st.set_page_config(page_title="AI Detector", layout="wide")
st.title("🧠 AI Content Detector")
st.markdown("This app detects the percentage of **AI-generated content** based on sentence-level analysis using DistilBERT.")
# Initialize session state to avoid duplicates
if "last_input" not in st.session_state:
st.session_state.last_input = ""
st.session_state.results = None
st.session_state.percentage = None
# 📋 User Input Area
user_input = st.text_area("📋 Paste your text below to check for AI-generated sentences:", height=300)
# 🔘 Analyze Button
if st.button("🔍 Analyze"):
if not user_input.strip():
st.warning("⚠️ Please enter some text to analyze.")
else:
# Store in session_state to avoid duplicates
st.session_state.last_input = user_input
ai_percentage, analysis_results = predict_ai_generated_percentage(user_input)
st.session_state.results = analysis_results
st.session_state.percentage = ai_percentage
# Display only if results are present
if st.session_state.results is not None:
st.subheader("🔍 Sentence-level Analysis")
for i, (sentence, prob, is_ai) in enumerate(st.session_state.results, start=1):
label = "🟢 Human" if not is_ai else "🔴 AI"
st.markdown(f"**{i}.** _{sentence}_\n\n → {label}")
st.subheader("📊 Final Result")
st.success(f"Estimated **AI-generated content**: **{st.session_state.percentage:.2f}%**")
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