app.py

import torch
import torch.nn.functional as F
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import re
import pandas as pd
import gradio as gr

# -----------------------------
# MODEL (Fakespot 2025)
# -----------------------------
MODEL_NAME = "fakespot-ai/roberta-base-ai-text-detection-v1"
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
dtype = torch.bfloat16 if (device.type == "cuda" and torch.cuda.is_bf16_supported()) else torch.float32
model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME, dtype=dtype).to(device).eval()

THRESHOLD = 0.80

# -----------------------------
# ABBREVIATION PROTECTION
# -----------------------------
ABBR = [
    "e.g", "i.e", "mr", "mrs", "ms", "dr", "prof", "vs", "etc",
    "fig", "al", "jr", "sr", "st", "no", "vol", "pp", "mt",
    "inc", "ltd", "co", "u.s", "u.k", "a.m", "p.m"
]

ABBR_REGEX = re.compile(r"\b(" + "|".join(map(re.escape, ABBR)) + r")\.", re.IGNORECASE)


def _protect(text):
    text = text.replace("...", "⟨ELLIPSIS⟩")
    text = re.sub(r"(?<=\d)\.(?=\d)", "⟨DECIMAL⟩", text)
    text = ABBR_REGEX.sub(r"\1⟨ABBRDOT⟩", text)
    return text


def _restore(text):
    return (
        text.replace("⟨ABBRDOT⟩", ".")
            .replace("⟨DECIMAL⟩", ".")
            .replace("⟨ELLIPSIS⟩", "...")
    )


# -----------------------------
# PERFECT PARAGRAPH-PRESERVING SPLITTER
# -----------------------------
def split_preserving_structure(text):
    blocks = re.split(r"(\n+)", text)  # keep newline separators
    final_blocks = []

    for block in blocks:
        if block.startswith("\n"):
            final_blocks.append(block)
        else:
            protected = _protect(block)
            parts = re.split(r"([.?!])(\s+)", protected)

            for i in range(0, len(parts), 3):
                sentence = parts[i]
                punct = parts[i + 1] if i + 1 < len(parts) else ""
                space = parts[i + 2] if i + 2 < len(parts) else ""

                whole = sentence + punct
                if whole.strip():
                    final_blocks.append(_restore(whole))
                if space:
                    final_blocks.append(space)

    return final_blocks


def extract_sentences_only(blocks):
    return [
        b for b in blocks
        if b.strip() != "" and not b.startswith("\n") and not b.isspace()
    ]


# -----------------------------
# GROUPING
# -----------------------------
def group_sentences(sents, size=3):
    return [" ".join(sents[i:i + size]) for i in range(0, len(sents), size)]


# -----------------------------
# ANALYSIS LOGIC
# -----------------------------
def analyze(text, max_len=512):

    blocks = split_preserving_structure(text)
    pure_sentences = extract_sentences_only(blocks)

    if not pure_sentences:
        return "—", "—", "<em>Paste text to analyze.</em>", None

    # Group into 3-sentence windows
    grouped = group_sentences(pure_sentences, 3)
    clean_grouped = [re.sub(r"\s+", " ", g).strip() for g in grouped]

    # Model forward pass
    inputs = tokenizer(clean_grouped, return_tensors="pt",
                       padding=True, truncation=True,
                       max_length=max_len).to(device)

    with torch.no_grad():
        logits = model(**inputs).logits

    chunk_probs = F.softmax(logits, dim=-1)[:, 1].cpu().tolist()

    # expand back
    ai_scores = []
    for idx, prob in enumerate(chunk_probs):
        start = idx * 3
        end = min(start + 3, len(pure_sentences))
        for _ in range(start, end):
            ai_scores.append(prob)

    # -----------------------------
    # RECONSTRUCTION WITH HIGHLIGHT
    # -----------------------------
    highlighted = ""
    sentence_index = 0

    for block in blocks:
        if block.startswith("\n"):
            highlighted += block
            continue

        if block.isspace():
            highlighted += block
            continue

        # safety
        if sentence_index >= len(ai_scores):
            ai_p = ai_scores[-1]
        else:
            ai_p = ai_scores[sentence_index]
        sentence_index += 1

        pct = f"{ai_p * 100:.1f}%"

        if ai_p < 0.30:
            color = "#11823b"
        elif ai_p < 0.70:
            color = "#b8860b"
        else:
            color = "#b80d0d"

        highlighted += (
            f"<span style='background:rgba(0,0,0,0.03); padding:3px 4px; "
            f"border-radius:4px;'><strong style='color:{color}'>[{pct}]</strong> "
            f"{block.strip()}</span> "
        )

    # -----------------------------
    # OVERALL SCORE
    # -----------------------------
    overall = sum(ai_scores) / len(ai_scores)
    overall_pct = f"{overall * 100:.1f}%"
    overall_label = "🤖 Likely AI Written" if overall >= THRESHOLD else "🧒 Likely Human Written"

    df = pd.DataFrame(
        [[i + 1, s, ai_scores[i]] for i, s in enumerate(pure_sentences)],
        columns=["#", "Sentence", "AI_Prob"]
    )

    return overall_label, overall_pct, highlighted, df


# -----------------------------
# UI
# -----------------------------
with gr.Blocks() as demo:
    gr.Markdown("### 🕵️ AI Sentence-Level Detector — Exact Structure Highlighting")

    text_input = gr.Textbox(label="Paste text", lines=14, placeholder="Your text…")
    btn = gr.Button("Analyze")

    verdict = gr.Label(label="Verdict (Overall)")
    score = gr.Label(label="AI Score")
    highlights = gr.HTML(label="Highlighted Text (Exact Structure)")
    table = gr.Dataframe(headers=["#", "Sentence", "AI_Prob"], wrap=True)

    btn.click(analyze, inputs=[text_input], outputs=[verdict, score, highlights, table])

if __name__ == "__main__":
    demo.launch()