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

# 🧠 Ensure sentence tokenizer works inside Hugging Face (use /tmp/)
nltk_data_path = "/tmp/nltk_data"
nltk.download("punkt", download_dir=nltk_data_path)
nltk.data.path.append(nltk_data_path)

# 📦 Load tokenizer and model
tokenizer = DistilBertTokenizerFast.from_pretrained("distilbert-base-uncased")
model = TFDistilBertForSequenceClassification.from_pretrained("sundaram07/distilbert-sentence-classifier")

# 🧠 Predict probability for one 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()  # Assuming binary classification (single neuron)
    return prob_ai

# 📊 Analyze full text
def predict_ai_generated_percentage(text, threshold=0.75):
    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 UI
st.title("🧠 AI Content Detector")
st.markdown("This tool detects the percentage of **AI-generated content** based on sentence-level analysis.")

user_input = st.text_area("📋 Paste your text here:", height=300)

if st.button("🔍 Analyze"):
    if user_input.strip() == "":
        st.warning("⚠️ Please enter some text to analyze.")
    else:
        ai_percentage, analysis_results = predict_ai_generated_percentage(user_input)
        
        st.subheader("🔎 Sentence-level Analysis")
        for i, (sentence, prob, is_ai) in enumerate(analysis_results, start=1):
            label = "🟢 Human" if not is_ai else "🔴 AI"
            st.markdown(f"**{i}.** _{sentence}_\n\n→ **Probability AI:** `{prob:.2%}` → {label}")

        st.subheader("📊 Final Result")
        st.success(f"Estimated **AI-generated content**: **{ai_percentage:.2f}%**")