app.py

import gradio as gr
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
import re
import matplotlib.pyplot as plt
from tokenizers.normalizers import Sequence, Replace, Strip
from tokenizers import Regex

# ---- Device setup ----
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

# ---- Model and Tokenizer Setup ----
model1_path = "modernbert.bin"
model2_path = "https://huggingface.co/mihalykiss/modernbert_2/resolve/main/Model_groups_3class_seed12"
model3_path = "https://huggingface.co/mihalykiss/modernbert_2/resolve/main/Model_groups_3class_seed22"

tokenizer = AutoTokenizer.from_pretrained("answerdotai/ModernBERT-base")

# Load models
model_1 = AutoModelForSequenceClassification.from_pretrained("answerdotai/ModernBERT-base", num_labels=41)
model_1.load_state_dict(torch.load(model1_path, map_location=device))
model_1.to(device).eval()

model_2 = AutoModelForSequenceClassification.from_pretrained("answerdotai/ModernBERT-base", num_labels=41)
model_2.load_state_dict(torch.hub.load_state_dict_from_url(model2_path, map_location=device))
model_2.to(device).eval()

model_3 = AutoModelForSequenceClassification.from_pretrained("answerdotai/ModernBERT-base", num_labels=41)
model_3.load_state_dict(torch.hub.load_state_dict_from_url(model3_path, map_location=device))
model_3.to(device).eval()


# ---- Label Mapping ----
label_mapping = {
    0: '13B', 1: '30B', 2: '65B', 3: '7B', 4: 'GLM130B', 5: 'bloom_7b',
    6: 'bloomz', 7: 'cohere', 8: 'davinci', 9: 'dolly', 10: 'dolly-v2-12b',
    11: 'flan_t5_base', 12: 'flan_t5_large', 13: 'flan_t5_small',
    14: 'flan_t5_xl', 15: 'flan_t5_xxl', 16: 'gemma-7b-it', 17: 'gemma2-9b-it',
    18: 'gpt-3.5-turbo', 19: 'gpt-35', 20: 'gpt4', 21: 'gpt4o',
    22: 'gpt_j', 23: 'gpt_neox', 24: 'human', 25: 'llama3-70b', 26: 'llama3-8b',
    27: 'mixtral-8x7b', 28: 'opt_1.3b', 29: 'opt_125m', 30: 'opt_13b',
    31: 'opt_2.7b', 32: 'opt_30b', 33: 'opt_350m', 34: 'opt_6.7b',
    35: 'opt_iml_30b', 36: 'opt_iml_max_1.3b', 37: 't0_11b', 38: 't0_3b',
    39: 'text-davinci-002', 40: 'text-davinci-003'
}

# ---- Text Cleaning ----
def clean_text(text: str) -> str:
    text = re.sub(r'\s{2,}', ' ', text)
    text = re.sub(r'\s+([,.;:?!])', r'\1', text)
    return text

newline_to_space = Replace(Regex(r'\s*\n\s*'), " ")
tokenizer.backend_tokenizer.normalizer = Sequence([
    tokenizer.backend_tokenizer.normalizer,
    newline_to_space,
    Strip()
])


# ---- Classification Function ----
def classify_text(text):
    cleaned_text = clean_text(text)
    if not cleaned_text.strip():
        return "**Error:** Please enter some text to analyze.", None

    # Split into paragraphs
    paragraphs = [p.strip() for p in re.split(r'\n{2,}', cleaned_text) if p.strip()]
    chunk_scores = []
    all_probabilities = []

    for i, paragraph in enumerate(paragraphs):
        inputs = tokenizer(paragraph, return_tensors="pt", truncation=True, padding=True).to(device)
        with torch.no_grad():
            logits_1 = model_1(**inputs).logits
            logits_2 = model_2(**inputs).logits
            logits_3 = model_3(**inputs).logits

            softmax_1 = torch.softmax(logits_1, dim=1)
            softmax_2 = torch.softmax(logits_2, dim=1)
            softmax_3 = torch.softmax(logits_3, dim=1)

            avg_probs = (softmax_1 + softmax_2 + softmax_3) / 3
            probs = avg_probs[0]
            all_probabilities.append(probs.cpu())

        human_prob = probs[24].item()
        ai_probs_clone = probs.clone()
        ai_probs_clone[24] = 0
        ai_total = ai_probs_clone.sum().item()
        total = human_prob + ai_total
        human_pct = (human_prob / total) * 100
        ai_pct = (ai_total / total) * 100
        ai_model = label_mapping[torch.argmax(ai_probs_clone).item()]

        chunk_scores.append({
            "human": human_pct,
            "ai": ai_pct,
            "model": ai_model,
            "text": paragraph[:200].replace('\n', ' ') + ("..." if len(paragraph) > 200 else "")
        })

    # ---- Overall Averages ----
    avg_human = sum(c["human"] for c in chunk_scores) / len(chunk_scores)
    avg_ai = sum(c["ai"] for c in chunk_scores) / len(chunk_scores)

    if avg_human > avg_ai:
        result_message = f"**Overall Result:** <span class='highlight-human'>{avg_human:.2f}% Human-written</span>"
    else:
        top_model = max(chunk_scores, key=lambda c: c['ai'])['model']
        result_message = f"**Overall Result:** <span class='highlight-ai'>{avg_ai:.2f}% AI-generated (likely {top_model})</span>"

    # ---- Paragraph Analysis (Markdown Clean) ----
    paragraph_text = "\n\n**Paragraph Analysis:**\n"
    for i, c in enumerate(chunk_scores, 1):
        paragraph_text += (
            f"**Paragraph {i}:** {c['human']:.2f}% Human | {c['ai']:.2f}% AI → *{c['model']}*\n"
            f"{c['text']}\n\n"
        )

    # ---- Top 5 Models Plot ----
    mean_probs = torch.mean(torch.stack(all_probabilities), dim=0)
    top_5_probs, top_5_idx = torch.topk(mean_probs, 5)
    top_5_probs = top_5_probs.cpu().numpy()
    top_5_labels = [label_mapping[i.item()] for i in top_5_idx]

    fig, ax = plt.subplots(figsize=(10, 5))
    bars = ax.barh(top_5_labels, top_5_probs, color='#4CAF50')
    ax.set_xlabel('Probability')
    ax.set_title('Top 5 Model Predictions')
    ax.invert_yaxis()
    for bar in bars:
        width = bar.get_width()
        ax.text(width + 0.005, bar.get_y() + bar.get_height() / 2, f'{width:.2%}', va='center')
    plt.tight_layout()

    return result_message + "\n\n" + paragraph_text, fig


# ---- UI Setup ----
title = "AI Text Detector"
description = """
This tool uses **ModernBERT** to detect AI-generated text.  
Each paragraph is analyzed separately to show which parts are likely AI-generated.
"""
bottom_text = "**Developed by SzegedAI – Extended by Saber**"

AI_texts = [
"Artificial intelligence (AI) is reshaping industries by automating tasks, enhancing decision-making, and driving innovation. From predictive analytics in finance to autonomous vehicles in transportation, AI technologies are becoming integral to daily operations. The future of AI lies not only in technological advancement but also in ensuring ethical use, transparency, and accountability."
]

Human_texts = [
"Mathematics has always been a cornerstone of scientific discovery. It provides a precise language for describing natural phenomena, from the orbit of planets to the behavior of subatomic particles. The beauty of mathematics lies in its universality—its principles hold true regardless of context or culture."
]

iface = gr.Blocks(css="""
    @import url('https://fonts.googleapis.com/css2?family=Roboto+Mono:wght@400;700&display=swap');
    #text_input_box { border-radius: 10px; border: 2px solid #4CAF50; font-size: 18px; padding: 15px; margin-bottom: 20px; width: 60%; margin: auto; }
    #result_output_box { border-radius: 10px; border: 2px solid #4CAF50; font-size: 16px; padding: 15px; margin-top: 20px; width: 80%; margin: auto; }
    body { font-family: 'Roboto Mono', sans-serif !important; padding: 20px; }
    .gradio-container { border: 1px solid #4CAF50; border-radius: 15px; padding: 30px; box-shadow: 0px 0px 10px rgba(0,255,0,0.4); max-width: 900px; margin: auto; }
    .highlight-human { color: #4CAF50; font-weight: bold; }
    .highlight-ai { color: #FF5733; font-weight: bold; }
""")

with iface:
    gr.Markdown(f"# {title}")
    gr.Markdown(description)
    text_input = gr.Textbox(label="", placeholder="Paste your article here...", elem_id="text_input_box", lines=10)
    result_output = gr.HTML("", elem_id="result_output_box")
    plot_output = gr.Plot(label="Model Probability Distribution")
    text_input.change(classify_text, inputs=text_input, outputs=[result_output, plot_output])
    with gr.Tab("AI Examples"):
        gr.Examples(AI_texts, inputs=text_input)
    with gr.Tab("Human Examples"):
        gr.Examples(Human_texts, inputs=text_input)
    gr.Markdown(bottom_text)

iface.launch(share=True)