src/app.py

import os
from pathlib import Path

# -----------------
# Get the directory where app.py is located
# -----------------
APP_DIR = Path(__file__).parent.resolve()

account_name = 'mamba413'

# -----------------
# Fix Streamlit Permission Issues
# -----------------
# 在 HF Space 中，将 Streamlit 配置目录设置到可写位置
if os.environ.get('SPACE_ID'):
    os.environ['STREAMLIT_SERVER_FILE_WATCHER_TYPE'] = 'none'
    os.environ['STREAMLIT_BROWSER_GATHER_USAGE_STATS'] = 'false'
    os.environ['STREAMLIT_SERVER_ENABLE_CORS'] = 'false'
    
    # 设置 HuggingFace 缓存到可写目录
    CACHE_DIR = '/tmp/huggingface_cache'
    os.makedirs(CACHE_DIR, exist_ok=True)

    os.environ['HF_HOME'] = CACHE_DIR
    os.environ['TRANSFORMERS_CACHE'] = CACHE_DIR
    os.environ['HF_DATASETS_CACHE'] = CACHE_DIR
    os.environ['HUGGINGFACE_HUB_CACHE'] = CACHE_DIR
    
    # 设置可写的配置目录
    streamlit_dir = Path('/tmp/.streamlit')
    streamlit_dir.mkdir(exist_ok=True, parents=True)
    # os.environ['STREAMLIT_HOME'] = '/tmp/.streamlit'


import streamlit as st
from FineTune.model import ComputeStat
import time

st.markdown(
    """
    <style>
    /* Text area & text input */
    textarea, input[type="text"] {
        background-color: #f8fafc !important;
        border: 1px solid #e5e7eb !important;
        color: #111827 !important;
    }

    textarea::placeholder {
        color: #9ca3af !important;
    }

    /* Selectbox */
    div[data-testid="stSelectbox"] > div {
        background-color: #f8fafc !important;
        border: 1px solid #e5e7eb !important;
    }
    </style>
    """,
    unsafe_allow_html=True
)

st.markdown(
    """
    <style>
    /* Detect button */
    div.stButton > button[kind="primary"] {
        background-color: #fdae6b;
        border: white;
        color: black;
        font-weight: 600;
        height: 4.3rem;

        font-size: 1.1rem;

        display: flex;
        align-items: center;
        justify-content: center;
        gap: 0.55rem;
    }

    /* Icon inside Detect button */
    div.stButton > button[kind="primary"] span {
        font-size: 1.25rem;
        line-height: 1;
    }

    div.stButton > button[kind="primary"]:hover {
        background-color: #fd8d3c;
        border-color: white;
    }

    div.stButton > button[kind="primary"]:active {
        background-color: #fd8d3c;
        border-color: white;
    }
    </style>
    """,
    unsafe_allow_html=True
)

# -----------------
# Page Configuration
# -----------------
st.set_page_config(
    page_title="DetectGPTPro",
    page_icon="🕵️",
)

# -----------------
# Model Loading (Cached)
# -----------------
@st.cache_resource
def load_model(from_pretrained, base_model, cache_dir, device):
    """
    Load and cache the model to avoid reloading on every user interaction.
    This function runs only once when the app starts or when parameters change.
    """
    # is_hf_space = os.environ.get('SPACE_ID') is not None
    is_hf_space = False
    if is_hf_space:
        cache_dir = '/tmp/huggingface_cache'
        os.makedirs(cache_dir, exist_ok=True)

        device = 'cpu'
        print("Using **CPU** now!")

        # 获取 HF Token（用于访问 gated 模型）
        hf_token = os.environ.get('HF_TOKEN', None)
        if hf_token:
            # 也可以用 login 方式
            try:
                from huggingface_hub import login
                login(token=hf_token)
                print("✅ Successfully authenticated with HF token")
            except Exception as e:
                print(f"⚠️ HF login warning: {e}")
        
        # 🔥 新增：从 HF Hub 下载模型
        # 检查是否是 HF Hub 路径（格式：username/repo-name）
        is_hf_hub = '/' in from_pretrained and not from_pretrained.startswith('.')
        if is_hf_hub:
            from huggingface_hub import snapshot_download
            print(f"📥 Downloading model from HuggingFace Hub: {from_pretrained}")
            try:
                # 下载整个仓库到本地
                local_model_path = snapshot_download(
                    repo_id=from_pretrained,
                    cache_dir=cache_dir,
                    token=hf_token,
                    repo_type="model"
                )
                print(f"✅ Model downloaded to: {local_model_path}")
                # 使用下载后的本地路径
                from_pretrained = local_model_path
            except Exception as e:
                print(f"❌ Failed to download model: {e}")
                raise
    else:
        cache_dir = cache_dir
    
    with st.spinner("🔄 Loading model... This may take a moment on first launch."):
        model = ComputeStat.from_pretrained(
            from_pretrained, 
            base_model, 
            device=device,
            cache_dir=cache_dir
        )
        model.set_criterion_fn('mean')
    return model

# -----------------
# Result Feedback Module Import
# -----------------
from feedback import FeedbackManager
from stats import StatsManager

# Initialize Feedback Manager with HF dataset
# make sure HF_TOKEN is set to visit private repository
FEEDBACK_DATASET_ID = os.environ.get('FEEDBACK_DATASET_ID', f'{account_name}/user-feedback')
feedback_manager = FeedbackManager(
    dataset_repo_id=FEEDBACK_DATASET_ID,
    hf_token=os.environ.get('HF_TOKEN'),
    local_backup=False if os.environ.get('SPACE_ID') else True # 保留本地备份
)

@st.cache_resource
def get_stats_manager():
    return StatsManager(
        dataset_repo_id=FEEDBACK_DATASET_ID,
        hf_token=os.environ.get('HF_TOKEN'),
        local_backup=False if os.environ.get('SPACE_ID') else True,
    )

stats_manager = get_stats_manager()

# -----------------
# Configuration
# -----------------
MODEL_CONFIG = {
    'from_pretrained': './src/FineTune/ckpt/',
    'base_model': 'gemma-1b',
    'cache_dir': '../cache', 
    'device': 'cpu' if os.environ.get('SPACE_ID') else 'mps',
    # 'device': 'cuda',
}

DOMAINS = [
    "General",
    "Academia", 
    "Finance", 
    "Government", 
    "Knowledge", 
    "Legislation", 
    "Medicine", 
    "News", 
    "UserReview"
]

# Load model once at startup
try:
    model = load_model(
        MODEL_CONFIG['from_pretrained'],
        MODEL_CONFIG['base_model'],
        MODEL_CONFIG['cache_dir'], 
        MODEL_CONFIG['device']
    )
    model_loaded = True
except Exception as e:
    model_loaded = False
    error_message = str(e)

# =========== 🆕 session_state ===========
if 'last_detection' not in st.session_state:
    st.session_state.last_detection = None
if 'feedback_given' not in st.session_state:
    st.session_state.feedback_given = False
if 'pending_toast' not in st.session_state:
    st.session_state.pending_toast = None
# ========================================

# Show any pending toast (set by feedback buttons before st.rerun())
if st.session_state.pending_toast:
    _msg, _icon = st.session_state.pending_toast
    st.toast(_msg, icon=_icon)
    st.session_state.pending_toast = None

# ----- Visit Counter -----
# session_state resets on F5 / new tab, so this runs exactly once per browser session
if 'visit_counted' not in st.session_state:
    st.session_state.visit_counted = True
    stats_manager.increment_visit()
# -------------------------

# -----------------
# Streamlit Layout
# -----------------
st.markdown(
    "<h1 style='text-align: center;'> Detect AI-Generated Texts 🕵️ </h1>",
    unsafe_allow_html=True,
)

# st.markdown(
#     """Pasted the text to be detected below and click the 'Detect' button to get the p-value. Use a better option may improve detection."""
# )

# Display model loading status
if not model_loaded:
    st.error(f"❌ Failed to load model: {error_message}")
    st.stop()

# -----------------
# Main Interface
# -----------------
# --- Two columns: Input text & button | Result displays ---
text_input = st.text_area(
    label="📝 Input Text to be Detected",
    placeholder="Paste your text here",
    height=240,
    label_visibility="hidden",
)

subcol11, subcol12, subcol13 = st.columns((1, 1, 1))

selected_domain = subcol11.selectbox(
    label="💡 Domain that matches your text",
    options=DOMAINS,
    index=0,  # Default to General
    # label_visibility="collapsed",
    # label_visibility="hidden",
)

detect_clicked = subcol12.button("🔍 Detect", type="primary", use_container_width=True)

selected_level = subcol13.slider(
    label="Significance level (α)",
    min_value=0.01,
    max_value=0.2,
    value=0.05,
    step=0.005,
    # label_visibility="collapsed",
)

# -----------------
# Detection Logic
# -----------------
if detect_clicked:
    if not text_input.strip():
        st.warning("⚠️ Please enter some text before detecting.")
    else:
        # ========== Reset feedback state ==========
        st.session_state.feedback_given = False
        # ==========================================

        # Start timing to decide whether to show progress bar
        start_time = time.time()
        
        # Use a placeholder for dynamic updates
        status_placeholder = st.empty()
        result_placeholder = st.empty()
        
        try:
            # Show spinner for quick operations (< 2 seconds expected)
            with status_placeholder:
                with st.spinner(f"🔍 Analyzing text in {selected_domain} domain..."):
                    # Perform inference
                    crit, p_value = model.compute_p_value(text_input, selected_domain)
                    elapsed_time = time.time() - start_time
            
                    # Convert tensors to Python scalars if needed
                    if hasattr(crit, 'item'):
                        crit = crit.item()
                    if hasattr(p_value, 'item'):
                        p_value = p_value.item()

            # Clear status and show results
            status_placeholder.empty()

            # ========== 🆕 保存检测结果到 session_state ==========
            st.session_state.last_detection = {
                'text': text_input,
                'domain': selected_domain,
                'statistics': crit,
                'p_value': p_value,
                'elapsed_time': elapsed_time
            }

            # Count detection once per unique detect action
            _det_key = f'det_counted_{hash(text_input[:80])}'
            if _det_key not in st.session_state:
                st.session_state[_det_key] = True
                stats_manager.increment_detection()

            st.info(
                f"""
                **Conclusion**: 

                    {'Text is likely LLM-generated.' if p_value < selected_level else 'Fail to reject hypothesis that text is human-written.'}

                based on the observation that $p$-value {p_value:.3f} is {'less' if p_value < selected_level else 'greater'} than significance level {selected_level:.2f} 📊
                """,
                icon="💡"
            )
            st.markdown(
                """
                <style>
                /* Tighten spacing inside Clarification / Citation expanders */
                div[data-testid="stExpander"] {
                    margin-top: -1.3rem;
                }
                div[data-testid="stExpander"] p,
                div[data-testid="stExpander"] li {
                    line-height: 1.35;
                    margin-bottom: 0.1rem;
                }

                div[data-testid="stExpander"] ul {
                    margin-top: 0.1rem;
                }
                </style>
                """,
                unsafe_allow_html=True
            )
            with st.expander("📋 Interpretation and Suggestions"):
                st.markdown(
                    """
                    + Interpretation:
                        - $p$-value: Lower $p$-value (closer to 0) indicates text is **more likely AI-generated**; Higher $p$-value (closer to 1) indicates text is **more likely human-written**.
                        - Significance Level (α): a threshold set by the user to determine the sensitivity of the detection. Lower α means stricter criteria for claiming the text is AI-generated.

                    + Suggestions for better detection:
                        - Provide longer text inputs for more reliable detection results.
                        - Select the domain that best matches the content of your text to improve detection accuracy.
                    """
                )


            # Show detailed results
            with result_placeholder:
                st.caption(f"⏱️ Processing time: {elapsed_time:.2f} seconds")
        
        except Exception as e:
            status_placeholder.empty()
            st.error(f"❌ Error during detection: {str(e)}")
            st.exception(e)

# -----------------
# Feedback UI (outside if detect_clicked — persists across all reruns via session_state)
# -----------------
if st.session_state.last_detection is not None and not st.session_state.feedback_given:
    _ld = st.session_state.last_detection
    st.markdown(
        """
        <style>
        .fb-header { display: flex; align-items: center; gap: 0.4rem; margin-bottom: 0.3rem; }
        .privacy-tip {
            position: relative; display: inline-block;
            cursor: help; color: #9ca3af; font-size: 0.9rem;
        }
        .privacy-tip .tip-text {
            visibility: hidden; opacity: 0;
            width: 240px; background-color: #374151; color: #f9fafb;
            text-align: left; border-radius: 6px;
            padding: 0.5rem 0.7rem; font-size: 0.78rem; line-height: 1.4;
            position: absolute; z-index: 100;
            bottom: 130%; left: 50%; transform: translateX(-50%);
            transition: opacity 0.25s ease; pointer-events: none;
        }
        .privacy-tip:hover .tip-text { visibility: visible; opacity: 1; }
        </style>
        <div class="fb-header">
          <strong>📝 Result Feedback</strong>: Does this detection result meet your expectations?
          <span class="privacy-tip">🔒
            <span class="tip-text">🔒 Your feedback is stored privately and will never be shared with third parties. It is used solely to improve detection accuracy.</span>
          </span>
        </div>
        """,
        unsafe_allow_html=True
    )
    feedback_col1, feedback_col2 = st.columns(2)
    with feedback_col1:
        if st.button("✅ Expected", use_container_width=True, type="secondary",
                     key="expected_btn"):
            try:
                fb_success, fb_message = feedback_manager.save_feedback(
                    _ld['text'], _ld['domain'], _ld['statistics'], _ld['p_value'], 'expected'
                )
                if fb_success:
                    st.session_state.feedback_given = True
                    st.session_state.pending_toast = ("Thank you for your feedback!", "✅")
                    st.rerun()
                else:
                    st.error(f"Failed to save feedback: {fb_message}")
            except Exception as e:
                st.error(f"Failed to save feedback: {str(e)}")
    with feedback_col2:
        if st.button("❌ Unexpected", use_container_width=True, type="secondary",
                     key="unexpected_btn"):
            try:
                fb_success, fb_message = feedback_manager.save_feedback(
                    _ld['text'], _ld['domain'], _ld['statistics'], _ld['p_value'], 'unexpected'
                )
                if fb_success:
                    st.session_state.feedback_given = True
                    st.session_state.pending_toast = ("Feedback recorded! This will help us improve.", "📝")
                    st.rerun()
                else:
                    st.error(f"Failed to save feedback: {fb_message}")
            except Exception as e:
                st.error(f"Failed to save feedback: {str(e)}")

# with st.expander("📋 Citation"):
#     st.markdown(
#         """
#         If you find this tool useful for you, please cite our paper: **[AdaDetectGPT: Adaptive Detection of LLM-Generated Text with Statistical Guarantees](https://arxiv.org/abs/2510.01268)**
#         """
#     )
#     st.code(
#         """
# @inproceedings{zhou2024adadetectgpt,
#   title={AdaDetectGPT: Adaptive Detection of LLM-Generated Text with Statistical Guarantees},
#   author={Hongyi Zhou and Jin Zhu and Pingfan Su and Kai Ye and Ying Yang and Shakeel A O B Gavioli-Akilagun and Chengchun Shi},
#   booktitle={The Thirty-Ninth Annual Conference on Neural Information Processing Systems},
#   year={2025},
# }
#         """,
#         language="bibtex"
#     )

# -----------------
# Statistics Chip (fixed top-right)
# -----------------
st.markdown(
    f"""
    <style>
    .stats-chip {{
        position: fixed;
        top: 3.6rem;
        right: 1rem;
        display: flex;
        align-items: center;
        gap: 0.35rem;
        font-size: 0.78rem;
        color: #9ca3af;
        z-index: 999;
        pointer-events: none;
    }}
    </style>
    <div class="stats-chip">
      <span>{stats_manager.visit_count:,} visits</span>
    </div>
    """,
    unsafe_allow_html=True
)

# -----------------
# Footer
# -----------------
st.markdown(
    """
    <style>
    .footer {
        position: fixed;
        left: 0;
        bottom: 0;
        width: 100%;
        background-color: white;
        color: gray;
        text-align: center;
        padding: 1px;
        border-top: 1px solid #e0e0e0;
        z-index: 999;
    }
    
    /* Add padding to main content to prevent overlap with fixed footer */
    .main .block-container {
        padding-bottom: 1px;
    }
    </style>
    <div class='footer'>
        <small> This tool is developed for research purposes only. The detection results are not 100% accurate and should not be used as the sole basis for any critical decisions. Users are advised to use this tool responsibly and ethically. </small>
    </div>
    """,
    unsafe_allow_html=True
)