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Add vector search API for raw 512-dim AdaFace embeddings

ec7950d 3 months ago

27 kB

import hashlib
from collections import OrderedDict

import gradio as gr
import numpy as np

from models.data_manager import DataManager
from models.face_recognition import _dist_to_confidence
from models.image_processor import image_search_performers
from models.sprite_processor import identify_sprite_performers


class _LRUCache:
    def __init__(self, maxsize=3):
        self.maxsize = maxsize
        self._cache = OrderedDict()

    @staticmethod
    def _hash_args(*args):
        h = hashlib.md5(usedforsecurity=False)
        for arg in args:
            if arg is None:
                h.update(b'\x00')
            elif isinstance(arg, bytes):
                h.update(arg)
            elif isinstance(arg, str):
                h.update(arg.encode('utf-8'))
            elif isinstance(arg, int):
                h.update(str(arg).encode())
            elif hasattr(arg, 'tobytes'):
                h.update(arg.tobytes())
            else:
                h.update(str(arg).encode())
        return h.digest()

    def get(self, *args):
        key = self._hash_args(*args)
        if key in self._cache:
            self._cache.move_to_end(key)
            return self._cache[key]
        return None

    def put(self, value, *args):
        key = self._hash_args(*args)
        self._cache[key] = value
        self._cache.move_to_end(key)
        while len(self._cache) > self.maxsize:
            self._cache.popitem(last=False)

_COUNTRY_FLAGS = {
    'AD': '🇦🇩', 'AE': '🇦🇪', 'AF': '🇦🇫', 'AG': '🇦🇬', 'AI': '🇦🇮', 'AL': '🇦🇱', 'AM': '🇦🇲', 'AO': '🇦🇴',
    'AQ': '🇦🇶', 'AR': '🇦🇷', 'AS': '🇦🇸', 'AT': '🇦🇹', 'AU': '🇦🇺', 'AW': '🇦🇼', 'AX': '🇦🇽', 'AZ': '🇦🇿',
    'BA': '🇧🇦', 'BB': '🇧🇧', 'BD': '🇧🇩', 'BE': '🇧🇪', 'BF': '🇧🇫', 'BG': '🇧🇬', 'BH': '🇧🇭', 'BI': '🇧🇮',
    'BJ': '🇧🇯', 'BL': '🇧🇱', 'BM': '🇧🇲', 'BN': '🇧🇳', 'BO': '🇧🇴', 'BQ': '🇧🇶', 'BR': '🇧🇷', 'BS': '🇧🇸',
    'BT': '🇧🇹', 'BV': '🇧🇻', 'BW': '🇧🇼', 'BY': '🇧🇾', 'BZ': '🇧🇿', 'CA': '🇨🇦', 'CC': '🇨🇨', 'CD': '🇨🇩',
    'CF': '🇨🇫', 'CG': '🇨🇬', 'CH': '🇨🇭', 'CI': '🇨🇮', 'CK': '🇨🇰', 'CL': '🇨🇱', 'CM': '🇨🇲', 'CN': '🇨🇳',
    'CO': '🇨🇴', 'CR': '🇨🇷', 'CU': '🇨🇺', 'CV': '🇨🇻', 'CW': '🇨🇼', 'CX': '🇨🇽', 'CY': '🇨🇾', 'CZ': '🇨🇿',
    'DE': '🇩🇪', 'DJ': '🇩🇯', 'DK': '🇩🇰', 'DM': '🇩🇲', 'DO': '🇩🇴', 'DZ': '🇩🇿', 'EC': '🇪🇨', 'EE': '🇪🇪',
    'EG': '🇪🇬', 'EH': '🇪🇭', 'ER': '🇪🇷', 'ES': '🇪🇸', 'ET': '🇪🇹', 'FI': '🇫🇮', 'FJ': '🇫🇯', 'FK': '🇫🇰',
    'FM': '🇫🇲', 'FO': '🇫🇴', 'FR': '🇫🇷', 'GA': '🇬🇦', 'GB': '🇬🇧', 'GD': '🇬🇩', 'GE': '🇬🇪', 'GF': '🇬🇫',
    'GG': '🇬🇬', 'GH': '🇬🇭', 'GI': '🇬🇮', 'GL': '🇬🇱', 'GM': '🇬🇲', 'GN': '🇬🇳', 'GP': '🇬🇵', 'GQ': '🇬🇶',
    'GR': '🇬🇷', 'GS': '🇬🇸', 'GT': '🇬🇹', 'GU': '🇬🇺', 'GW': '🇬🇼', 'GY': '🇬🇾', 'HK': '🇭🇰', 'HM': '🇭🇲',
    'HN': '🇭🇳', 'HR': '🇭🇷', 'HT': '🇭🇹', 'HU': '🇭🇺', 'ID': '🇮🇩', 'IE': '🇮🇪', 'IL': '🇮🇱', 'IM': '🇮🇲',
    'IN': '🇮🇳', 'IO': '🇮🇴', 'IQ': '🇮🇶', 'IR': '🇮🇷', 'IS': '🇮🇸', 'IT': '🇮🇹', 'JE': '🇯🇪', 'JM': '🇯🇲',
    'JO': '🇯🇴', 'JP': '🇯🇵', 'KE': '🇰🇪', 'KG': '🇰🇬', 'KH': '🇰🇭', 'KI': '🇰🇮', 'KM': '🇰🇲', 'KN': '🇰🇳',
    'KP': '🇰🇵', 'KR': '🇰🇷', 'KW': '🇰🇼', 'KY': '🇰🇾', 'KZ': '🇰🇿', 'LA': '🇱🇦', 'LB': '🇱🇧', 'LC': '🇱🇨',
    'LI': '🇱🇮', 'LK': '🇱🇰', 'LR': '🇱🇷', 'LS': '🇱🇸', 'LT': '🇱🇹', 'LU': '🇱🇺', 'LV': '🇱🇻', 'LY': '🇱🇾',
    'MA': '🇲🇦', 'MC': '🇲🇨', 'MD': '🇲🇩', 'ME': '🇲🇪', 'MF': '🇲🇫', 'MG': '🇲🇬', 'MH': '🇲🇭', 'MK': '🇲🇰',
    'ML': '🇲🇱', 'MM': '🇲🇲', 'MN': '🇲🇳', 'MO': '🇲🇴', 'MP': '🇲🇵', 'MQ': '🇲🇶', 'MR': '🇲🇷', 'MS': '🇲🇸',
    'MT': '🇲🇹', 'MU': '🇲🇺', 'MV': '🇲🇻', 'MW': '🇲🇼', 'MX': '🇲🇽', 'MY': '🇲🇾', 'MZ': '🇲🇿', 'NA': '🇳🇦',
    'NC': '🇳🇨', 'NE': '🇳🇪', 'NF': '🇳🇫', 'NG': '🇳🇬', 'NI': '🇳🇮', 'NL': '🇳🇱', 'NO': '🇳🇴', 'NP': '🇳🇵',
    'NR': '🇳🇷', 'NU': '🇳🇺', 'NZ': '🇳🇿', 'OM': '🇴🇲', 'PA': '🇵🇦', 'PE': '🇵🇪', 'PF': '🇵🇫', 'PG': '🇵🇬',
    'PH': '🇵🇭', 'PK': '🇵🇰', 'PL': '🇵🇱', 'PM': '🇵🇲', 'PN': '🇵🇳', 'PR': '🇵🇷', 'PS': '🇵🇸', 'PT': '🇵🇹',
    'PW': '🇵🇼', 'PY': '🇵🇾', 'QA': '🇶🇦', 'RE': '🇷🇪', 'RO': '🇷🇴', 'RS': '🇷🇸', 'RU': '🇷🇺', 'RW': '🇷🇼',
    'SA': '🇸🇦', 'SB': '🇸🇧', 'SC': '🇸🇨', 'SD': '🇸🇩', 'SE': '🇸🇪', 'SG': '🇸🇬', 'SH': '🇸🇭', 'SI': '🇸🇮',
    'SJ': '🇸🇯', 'SK': '🇸🇰', 'SL': '🇸🇱', 'SM': '🇸🇲', 'SN': '🇸🇳', 'SO': '🇸🇴', 'SR': '🇸🇷', 'SS': '🇸🇸',
    'ST': '🇸🇹', 'SV': '🇸🇻', 'SX': '🇸🇽', 'SY': '🇸🇾', 'SZ': '🇸🇿', 'TC': '🇹🇨', 'TD': '🇹🇩', 'TF': '🇹🇫',
    'TG': '🇹🇬', 'TH': '🇹🇭', 'TJ': '🇹🇯', 'TK': '🇹🇰', 'TL': '🇹🇱', 'TM': '🇹🇲', 'TN': '🇹🇳', 'TO': '🇹🇴',
    'TR': '🇹🇷', 'TT': '🇹🇹', 'TV': '🇹🇻', 'TW': '🇹🇼', 'TZ': '🇹🇿', 'UA': '🇺🇦', 'UG': '🇺🇬', 'UM': '🇺🇲',
    'US': '🇺🇸', 'UY': '🇺🇾', 'UZ': '🇺🇿', 'VA': '🇻🇦', 'VC': '🇻🇨', 'VE': '🇻🇪', 'VG': '🇻🇬', 'VI': '🇻🇮',
    'VN': '🇻🇳', 'VU': '🇻🇺', 'WF': '🇼🇫', 'WS': '🇼🇸', 'YE': '🇾🇪', 'YT': '🇾🇹', 'ZA': '🇿🇦', 'ZM': '🇿🇲',
    'ZW': '🇿🇼',
}

class WebInterface:
    def __init__(self, data_manager: DataManager):
        self.data_manager = data_manager
        self._img_cache = _LRUCache(maxsize=3)
        self._sprite_cache = _LRUCache(maxsize=3)

    def get_country_flag(self, country_code):
        if not country_code or len(country_code) != 2:
            return ""
        return _COUNTRY_FLAGS.get(country_code.upper(), "")

    def multiple_image_search(self, img, results):
        cached = self._img_cache.get(img, results)
        if cached is not None:
            return cached
        try:
            result = image_search_performers(img, self.data_manager, results)
            self._img_cache.put(result, img, results)
            return result
        except ValueError as e:
            if "No faces found" in str(e):
                return {"error": "No faces detected in the uploaded image. Please try uploading an image with visible faces."}
            else:
                raise e

    def format_results_for_visual_display(self, json_results):
        if not json_results:
            return "<p>No faces detected or no matches found.</p>"

        if isinstance(json_results, dict) and "error" in json_results:
            return f"""
            <div class="performer-card">
                <div class="face-info">
                    <h3 style="color: #ff6b6b;">Error</h3>
                    <p>{json_results['error']}</p>
                </div>
            </div>
            """

        html_parts = []

        html_parts.append("""
        <style>
        body, .gradio-container {
            background-color: #1e1e1e !important;
            color: #d4d4d4 !important;
        }
        .performer-card {
            border: 1px solid #404040;
            border-radius: 16px;
            padding: 24px;
            margin: 16px 0;
            background: #2d2d2d;
            box-shadow: 0 4px 12px rgba(0,0,0,0.3);
            color: #d4d4d4;
        }
        .face-info {
            background: #3c3c3c;
            padding: 12px 16px;
            border-radius: 10px;
            margin-bottom: 24px;
            border: 1px solid #4a4a4a;
            display: flex;
            align-items: center;
            gap: 16px;
        }
        .face-info-content {
            flex: 1;
        }
        .face-info h3 {
            color: #ffffff;
            margin: 0 0 4px 0;
            font-size: 1.1em;
        }
        .face-info p {
            color: #cccccc;
            margin: 2px 0;
            font-size: 0.9em;
        }
        .face-info p strong {
            color: #9cdcfe;
        }
        .detected-face img {
            width: 72px;
            height: 72px;
            border-radius: 8px;
            object-fit: cover;
            border: 2px solid #569cd6;
            box-shadow: 0 2px 8px rgba(0,0,0,0.3);
            flex-shrink: 0;
        }
        .performer-grid {
            display: grid;
            grid-template-columns: repeat(auto-fit, minmax(280px, 1fr));
            gap: 24px;
            margin-top: 8px;
        }
        .performer-item {
            border: 1px solid #4a4a4a;
            border-radius: 16px;
            padding: 0;
            background: #333333;
            transition: all 0.3s ease;
            box-shadow: 0 2px 8px rgba(0,0,0,0.2);
            display: flex;
            flex-direction: column;
            overflow: hidden;
        }
        .performer-item:hover {
            border-color: #569cd6;
            box-shadow: 0 8px 24px rgba(0,0,0,0.5);
            transform: translateY(-4px);
        }
        .performer-image-wrap {
            width: 100%;
            background: #2a2a2a;
            display: flex;
            justify-content: center;
            align-items: center;
            overflow: hidden;
        }
        .performer-image {
            width: 100%;
            max-height: 280px;
            object-fit: cover;
            object-position: top;
            display: block;
            border-bottom: 1px solid #4a4a4a;
            transition: transform 0.4s ease;
        }
        .performer-image:hover {
            transform: scale(1.04);
        }
        .performer-body {
            padding: 16px;
            display: flex;
            flex-direction: column;
            gap: 8px;
        }
        .performer-header {
            display: flex;
            align-items: center;
            flex-wrap: wrap;
            gap: 8px;
        }
        .performer-name {
            color: #ffffff;
            margin: 0;
            font-size: 1.15em;
            font-weight: 600;
            line-height: 1.3;
        }
        .performer-name a {
            color: #569cd6;
            text-decoration: none;
            transition: color 0.3s ease;
        }
        .performer-name a:hover {
            color: #9cdcfe;
            text-decoration: underline;
        }
        .performer-country {
            color: #999999;
            font-size: 0.85em;
            margin-left: auto;
        }
        .country-flag {
            font-size: 1.1em;
            margin-right: 4px;
            vertical-align: middle;
        }
        .confidence-bar {
            background: #404040;
            border-radius: 8px;
            overflow: hidden;
            height: 24px;
            margin: 4px 0 0 0;
            border: 1px solid #4a4a4a;
            width: 100%;
        }
        .confidence-fill {
            height: 100%;
            transition: width 0.5s ease;
            text-align: center;
            line-height: 24px;
            color: white;
            font-size: 12px;
            font-weight: bold;
            text-shadow: 0 1px 2px rgba(0,0,0,0.5);
        }
        .high-confidence {
            background: linear-gradient(135deg, #4caf50, #66bb6a);
        }
        .medium-confidence {
            background: linear-gradient(135deg, #ff9800, #ffb74d);
        }
        .low-confidence {
            background: linear-gradient(135deg, #f44336, #ef5350);
        }
        .source-badge {
            display: inline-block;
            width: 18px;
            height: 18px;
            border-radius: 50%;
            text-align: center;
            line-height: 18px;
            font-size: 10px;
            font-weight: 700;
            color: #fff;
            vertical-align: middle;
            cursor: help;
        }
        .gender-badge {
            display: inline-block;
            padding: 2px 10px;
            border-radius: 10px;
            font-size: 0.72em;
            font-weight: 600;
            letter-spacing: 0.02em;
            vertical-align: middle;
            border: 1px solid transparent;
        }
        .gender-female {
            background: rgba(220, 120, 200, 0.18);
            color: #e8a8d8;
            border-color: rgba(220, 120, 200, 0.35);
        }
        .gender-male {
            background: rgba(100, 160, 230, 0.18);
            color: #9cc2ee;
            border-color: rgba(100, 160, 230, 0.35);
        }
        .gender-non-binary {
            background: rgba(120, 210, 130, 0.18);
            color: #a8e0b0;
            border-color: rgba(120, 210, 130, 0.35);
        }
        .gender-transgender-female {
            background: rgba(220, 140, 200, 0.12);
            color: #d8a8c8;
            border-color: rgba(220, 140, 200, 0.28);
        }
        .gender-transgender-male {
            background: rgba(100, 160, 230, 0.12);
            color: #a8c0e0;
            border-color: rgba(100, 160, 230, 0.28);
        }
        .source-stashdb { background: #4a6fa5; }
        .source-javstash { background: #c0392b; }
        .source-fansdb { background: #27ae60; }
        </style>
        """)

        for i, face_result in enumerate(json_results):
            face_confidence = face_result['confidence']
            performers = face_result['performers']
            face_image_b64 = f"data:image/jpeg;base64,{face_result['image']}"

            html_parts.append(f"""
            <div class="performer-card">
                <div class="face-info">
                    <div class="detected-face">
                        <img src="{face_image_b64}" alt="Detected Face {i+1}">
                    </div>
                    <div class="face-info-content">
                        <h3>Face {i+1}</h3>
                        <p><strong>Detection:</strong> {face_confidence:.1%} &nbsp;|&nbsp; <strong>Matches:</strong> {len(performers)}</p>
                    </div>
                </div>
            """)

            if performers:
                html_parts.append('<div class="performer-grid">')
                for performer in performers:
                    confidence_class = "high-confidence" if performer['confidence'] >= 80 else "medium-confidence" if performer['confidence'] >= 60 else "low-confidence"
                    country_code = performer.get('country', '')
                    country_flag = self.get_country_flag(country_code)
                    country_display = f"{country_flag} {country_code}" if country_flag else (country_code if country_code else 'Unknown')

                    source = performer.get('source', '')
                    source_badge = ''
                    if source:
                        initial = source[0].upper()
                        source_badge = f'<span class="source-badge source-{source}" title="{source}">{initial}</span>'

                    gender_raw = performer.get('gender') or ''
                    gender_badge = ''
                    if gender_raw:
                        gender_key = gender_raw.lower().replace('_', '-')
                        gender_display = gender_raw.lower().replace('_', ' ').title()
                        if gender_key == 'non-binary':
                            gender_display = 'Non-binary'
                        gender_badge = f'<span class="gender-badge gender-{gender_key}">{gender_display}</span>'

                    html_parts.append(f"""
                    <div class="performer-item">
                        <div class="performer-image-wrap">
                            <img src="{performer['image']}" alt="{performer['name']}" class="performer-image" onerror="this.style.display='none'">
                        </div>
                        <div class="performer-body">
                            <div class="performer-header">
                                <h4 class="performer-name"><a href="{performer['performer_url']}" target="_blank">{performer['name']}</a></h4>
                                {source_badge}
                                {gender_badge}
                                <span class="performer-country"><span class="country-flag">{country_flag}</span>{country_code if country_code else 'Unknown'}</span>
                            </div>
                            <div class="confidence-bar">
                                <div class="confidence-fill {confidence_class}" style="width: {performer['confidence']}%">
                                    {performer['confidence']}%
                                </div>
                            </div>
                        </div>
                    </div>
                    """)
                html_parts.append('</div>')
            else:
                html_parts.append('<p><em>No performer matches found for this face.</em></p>')

            html_parts.append('</div>')

        return ''.join(html_parts)

    def multiple_image_search_with_visual(self, img, results):
        """Run face search and return HTML for visual display."""
        try:
            json_results = self.multiple_image_search(img, results)
            return self.format_results_for_visual_display(json_results)
        except Exception as e:
            return f"<div class='performer-card'><h3>Error</h3><p>{str(e)}</p></div>"

    def search_by_vector(self, payload):
        """Search the AdaFace index directly with a raw 512-dim embedding.
        Expects payload = {"embedding": [512 floats], "top_k": 5}.
        Skips detection and embedding generation entirely.
        """
        if not isinstance(payload, dict):
            return {"error": "Payload must be a JSON object: {\"embedding\": [512 floats], \"top_k\": 5}"}

        embedding = payload.get("embedding")
        if embedding is None:
            return {"error": "Missing 'embedding' field."}
        try:
            emb = np.asarray(embedding, dtype=np.float32)
        except (TypeError, ValueError):
            return {"error": "'embedding' must be an array of 512 numbers."}
        if emb.ndim != 1 or emb.shape[0] != 512:
            return {"error": f"'embedding' must have exactly 512 dimensions, got {emb.size}."}

        norm = float(np.linalg.norm(emb))
        if norm < 1e-12:
            return {"error": "'embedding' must be a non-zero vector."}
        emb = emb / norm

        try:
            top_k = int(payload.get("top_k", 5))
        except (TypeError, ValueError):
            return {"error": "'top_k' must be an integer."}
        top_k = max(1, min(top_k, 50))

        ids, dists = self.data_manager.query_adaface_index(emb, top_k)

        performers = []
        for pid, dist in zip(ids, dists):
            info = self.data_manager.get_performer_info(pid, _dist_to_confidence(dist), dist)
            if info is not None:
                performers.append(info)

        return {"performers": performers, "count": len(performers)}

    def _create_vector_search_interface(self):
        """Create the raw-vector JSON API search interface"""
        with gr.Blocks() as interface:
            gr.Markdown("# Vector Search API")
            gr.Markdown("Search with a raw 512-dim AdaFace embedding (no image upload).")

            with gr.Row():
                with gr.Column():
                    payload_input = gr.JSON(
                        label="Request",
                        value={"embedding": [0.0] * 512, "top_k": 5},
                    )
                    search_btn = gr.Button("Search")

                with gr.Column():
                    json_output = gr.JSON(label="JSON Results")

            search_btn.click(
                fn=self.search_by_vector,
                inputs=[payload_input],
                outputs=json_output,
                api_name="search_by_vector"
            )

        return interface

    def _create_json_search_interface(self):
        """Create the JSON API search interface"""
        with gr.Blocks() as interface:
            gr.Markdown("# Face Recognition API")
            gr.Markdown("Upload an image and get JSON results - perfect for API integration.")

            with gr.Row():
                with gr.Column():
                    img_input = gr.Image(type="pil")
                    results_count = gr.Slider(
                        label="Top matches",
                        minimum=3,
                        maximum=50,
                        value=5,
                        step=1
                    )
                    search_btn = gr.Button("Search")

                with gr.Column():
                    json_output = gr.JSON(label="JSON Results")

            search_btn.click(
                fn=self.multiple_image_search,
                inputs=[img_input, results_count],
                outputs=json_output,
                api_name="multiple_image_search"
            )

        return interface

    def identify_sprite_performers_wrapper(self, sprite_img, vtt_file, threshold):
        if sprite_img is None:
            yield {"error": "Please upload a sprite image."}
            return
        if vtt_file is None:
            yield {"error": "Please upload a VTT file."}
            return
        try:
            if hasattr(vtt_file, 'read'):
                vtt_text = vtt_file.read().decode('utf-8')
            else:
                with open(vtt_file, 'r', encoding='utf-8') as f:
                    vtt_text = f.read()
        except Exception as e:
            yield {"error": f"Failed to read VTT file: {e}"}
            return

        cached = self._sprite_cache.get(sprite_img, vtt_text, threshold)
        if cached is not None:
            yield cached
            return

        gen = identify_sprite_performers(
            sprite_img, vtt_text, self.data_manager,
            detection_threshold=threshold / 100.0,
        )
        try:
            final = None
            for item in gen:
                if isinstance(item, tuple) and len(item) == 2:
                    progress_val, desc = item
                    yield {"progress": progress_val, "desc": desc}
                else:
                    final = item
                    yield item
                    return
        except ValueError as e:
            yield {"error": str(e)}
        finally:
            if final is not None:
                self._sprite_cache.put(final, sprite_img, vtt_text, threshold)

    def _create_sprite_search_interface(self):
        with gr.Blocks() as interface:
            gr.Markdown("# Sprite/VTT Batch Face Identification")
            gr.Markdown("Upload a scene sprite image and its VTT file to identify all performers across the entire scene.")

            with gr.Row():
                with gr.Column():
                    sprite_input = gr.Image(type="pil")
                    vtt_input = gr.File(label="VTT File", file_types=[".vtt"])
                    threshold = gr.Slider(
                        label="Detection threshold",
                        minimum=1,
                        maximum=100,
                        value=50,
                        step=1,
                    )
                    search_btn = gr.Button("Identify Performers")

                with gr.Column():
                    json_output = gr.JSON(label="Identification Results")

            search_btn.click(
                fn=self.identify_sprite_performers_wrapper,
                inputs=[sprite_input, vtt_input, threshold],
                outputs=json_output,
                api_name="identify_sprite_performers",
            )

        return interface

    def _create_visual_search_interface(self):
        with gr.Blocks() as interface:
            gr.Markdown("# Who is in the photo?")
            gr.Markdown("Upload an image of a person(s) and we'll show you who it is with photos and details.")

            with gr.Row():
                with gr.Column():
                    img_input = gr.Image(type="pil")
                    results_count = gr.Slider(
                        label="Top matches",
                        minimum=3,
                        maximum=50,
                        value=5,
                        step=1
                    )
                    search_btn = gr.Button("Search")

                with gr.Column():
                    performer_info = gr.HTML(
                        label="Performer Information",
                        value="<p>Upload an image and click search to see results.</p>"
                    )

            search_btn.click(
                fn=self.multiple_image_search_with_visual,
                inputs=[img_input, results_count],
                outputs=[performer_info],
                api_name="multiple_image_search_with_visual"
            )

        return interface

    def launch(self, server_name="0.0.0.0", server_port=7860, share=True):
        with gr.Blocks(
            css="""
            .gradio-container {
                background-color: #1e1e1e !important;
                color: #d4d4d4 !important;
            }
            .dark {
                --background-fill-primary: #2d2d2d;
                --background-fill-secondary: #3c3c3c;
                --border-color-primary: #404040;
                --block-title-text-color: #ffffff;
                --body-text-color: #d4d4d4;
            }
            """
        ) as demo:
            with gr.Tabs():
                with gr.TabItem("Visual Search"):
                    self._create_visual_search_interface()
                with gr.TabItem("JSON API"):
                    self._create_json_search_interface()
                with gr.TabItem("Sprite Search"):
                    self._create_sprite_search_interface()
                with gr.TabItem("Vector Search"):
                    self._create_vector_search_interface()

        @demo.app.middleware("http")
        async def add_cors(request, call_next):
            response = await call_next(request)
            response.headers["Access-Control-Allow-Origin"] = "*"
            response.headers["Access-Control-Allow-Methods"] = "*"
            response.headers["Access-Control-Allow-Headers"] = "*"
            response.headers["Access-Control-Allow-Credentials"] = "true"
            return response

        demo.queue().launch(server_name=server_name, server_port=server_port, share=share, ssr_mode=False)

	import hashlib
	from collections import OrderedDict

	import gradio as gr
	import numpy as np

	from models.data_manager import DataManager
	from models.face_recognition import _dist_to_confidence
	from models.image_processor import image_search_performers
	from models.sprite_processor import identify_sprite_performers


	class _LRUCache:
	def __init__(self, maxsize=3):
	self.maxsize = maxsize
	self._cache = OrderedDict()

	@staticmethod
	def _hash_args(*args):
	h = hashlib.md5(usedforsecurity=False)
	for arg in args:
	if arg is None:
	h.update(b'\x00')
	elif isinstance(arg, bytes):
	h.update(arg)
	elif isinstance(arg, str):
	h.update(arg.encode('utf-8'))
	elif isinstance(arg, int):
	h.update(str(arg).encode())
	elif hasattr(arg, 'tobytes'):
	h.update(arg.tobytes())
	else:
	h.update(str(arg).encode())
	return h.digest()

	def get(self, *args):
	key = self._hash_args(*args)
	if key in self._cache:
	self._cache.move_to_end(key)
	return self._cache[key]
	return None

	def put(self, value, *args):
	key = self._hash_args(*args)
	self._cache[key] = value
	self._cache.move_to_end(key)
	while len(self._cache) > self.maxsize:
	self._cache.popitem(last=False)

	_COUNTRY_FLAGS = {
	'AD': '🇦🇩', 'AE': '🇦🇪', 'AF': '🇦🇫', 'AG': '🇦🇬', 'AI': '🇦🇮', 'AL': '🇦🇱', 'AM': '🇦🇲', 'AO': '🇦🇴',
	'AQ': '🇦🇶', 'AR': '🇦🇷', 'AS': '🇦🇸', 'AT': '🇦🇹', 'AU': '🇦🇺', 'AW': '🇦🇼', 'AX': '🇦🇽', 'AZ': '🇦🇿',
	'BA': '🇧🇦', 'BB': '🇧🇧', 'BD': '🇧🇩', 'BE': '🇧🇪', 'BF': '🇧🇫', 'BG': '🇧🇬', 'BH': '🇧🇭', 'BI': '🇧🇮',
	'BJ': '🇧🇯', 'BL': '🇧🇱', 'BM': '🇧🇲', 'BN': '🇧🇳', 'BO': '🇧🇴', 'BQ': '🇧🇶', 'BR': '🇧🇷', 'BS': '🇧🇸',
	'BT': '🇧🇹', 'BV': '🇧🇻', 'BW': '🇧🇼', 'BY': '🇧🇾', 'BZ': '🇧🇿', 'CA': '🇨🇦', 'CC': '🇨🇨', 'CD': '🇨🇩',
	'CF': '🇨🇫', 'CG': '🇨🇬', 'CH': '🇨🇭', 'CI': '🇨🇮', 'CK': '🇨🇰', 'CL': '🇨🇱', 'CM': '🇨🇲', 'CN': '🇨🇳',
	'CO': '🇨🇴', 'CR': '🇨🇷', 'CU': '🇨🇺', 'CV': '🇨🇻', 'CW': '🇨🇼', 'CX': '🇨🇽', 'CY': '🇨🇾', 'CZ': '🇨🇿',
	'DE': '🇩🇪', 'DJ': '🇩🇯', 'DK': '🇩🇰', 'DM': '🇩🇲', 'DO': '🇩🇴', 'DZ': '🇩🇿', 'EC': '🇪🇨', 'EE': '🇪🇪',
	'EG': '🇪🇬', 'EH': '🇪🇭', 'ER': '🇪🇷', 'ES': '🇪🇸', 'ET': '🇪🇹', 'FI': '🇫🇮', 'FJ': '🇫🇯', 'FK': '🇫🇰',
	'FM': '🇫🇲', 'FO': '🇫🇴', 'FR': '🇫🇷', 'GA': '🇬🇦', 'GB': '🇬🇧', 'GD': '🇬🇩', 'GE': '🇬🇪', 'GF': '🇬🇫',
	'GG': '🇬🇬', 'GH': '🇬🇭', 'GI': '🇬🇮', 'GL': '🇬🇱', 'GM': '🇬🇲', 'GN': '🇬🇳', 'GP': '🇬🇵', 'GQ': '🇬🇶',
	'GR': '🇬🇷', 'GS': '🇬🇸', 'GT': '🇬🇹', 'GU': '🇬🇺', 'GW': '🇬🇼', 'GY': '🇬🇾', 'HK': '🇭🇰', 'HM': '🇭🇲',
	'HN': '🇭🇳', 'HR': '🇭🇷', 'HT': '🇭🇹', 'HU': '🇭🇺', 'ID': '🇮🇩', 'IE': '🇮🇪', 'IL': '🇮🇱', 'IM': '🇮🇲',
	'IN': '🇮🇳', 'IO': '🇮🇴', 'IQ': '🇮🇶', 'IR': '🇮🇷', 'IS': '🇮🇸', 'IT': '🇮🇹', 'JE': '🇯🇪', 'JM': '🇯🇲',
	'JO': '🇯🇴', 'JP': '🇯🇵', 'KE': '🇰🇪', 'KG': '🇰🇬', 'KH': '🇰🇭', 'KI': '🇰🇮', 'KM': '🇰🇲', 'KN': '🇰🇳',
	'KP': '🇰🇵', 'KR': '🇰🇷', 'KW': '🇰🇼', 'KY': '🇰🇾', 'KZ': '🇰🇿', 'LA': '🇱🇦', 'LB': '🇱🇧', 'LC': '🇱🇨',
	'LI': '🇱🇮', 'LK': '🇱🇰', 'LR': '🇱🇷', 'LS': '🇱🇸', 'LT': '🇱🇹', 'LU': '🇱🇺', 'LV': '🇱🇻', 'LY': '🇱🇾',
	'MA': '🇲🇦', 'MC': '🇲🇨', 'MD': '🇲🇩', 'ME': '🇲🇪', 'MF': '🇲🇫', 'MG': '🇲🇬', 'MH': '🇲🇭', 'MK': '🇲🇰',
	'ML': '🇲🇱', 'MM': '🇲🇲', 'MN': '🇲🇳', 'MO': '🇲🇴', 'MP': '🇲🇵', 'MQ': '🇲🇶', 'MR': '🇲🇷', 'MS': '🇲🇸',
	'MT': '🇲🇹', 'MU': '🇲🇺', 'MV': '🇲🇻', 'MW': '🇲🇼', 'MX': '🇲🇽', 'MY': '🇲🇾', 'MZ': '🇲🇿', 'NA': '🇳🇦',
	'NC': '🇳🇨', 'NE': '🇳🇪', 'NF': '🇳🇫', 'NG': '🇳🇬', 'NI': '🇳🇮', 'NL': '🇳🇱', 'NO': '🇳🇴', 'NP': '🇳🇵',
	'NR': '🇳🇷', 'NU': '🇳🇺', 'NZ': '🇳🇿', 'OM': '🇴🇲', 'PA': '🇵🇦', 'PE': '🇵🇪', 'PF': '🇵🇫', 'PG': '🇵🇬',
	'PH': '🇵🇭', 'PK': '🇵🇰', 'PL': '🇵🇱', 'PM': '🇵🇲', 'PN': '🇵🇳', 'PR': '🇵🇷', 'PS': '🇵🇸', 'PT': '🇵🇹',
	'PW': '🇵🇼', 'PY': '🇵🇾', 'QA': '🇶🇦', 'RE': '🇷🇪', 'RO': '🇷🇴', 'RS': '🇷🇸', 'RU': '🇷🇺', 'RW': '🇷🇼',
	'SA': '🇸🇦', 'SB': '🇸🇧', 'SC': '🇸🇨', 'SD': '🇸🇩', 'SE': '🇸🇪', 'SG': '🇸🇬', 'SH': '🇸🇭', 'SI': '🇸🇮',
	'SJ': '🇸🇯', 'SK': '🇸🇰', 'SL': '🇸🇱', 'SM': '🇸🇲', 'SN': '🇸🇳', 'SO': '🇸🇴', 'SR': '🇸🇷', 'SS': '🇸🇸',
	'ST': '🇸🇹', 'SV': '🇸🇻', 'SX': '🇸🇽', 'SY': '🇸🇾', 'SZ': '🇸🇿', 'TC': '🇹🇨', 'TD': '🇹🇩', 'TF': '🇹🇫',
	'TG': '🇹🇬', 'TH': '🇹🇭', 'TJ': '🇹🇯', 'TK': '🇹🇰', 'TL': '🇹🇱', 'TM': '🇹🇲', 'TN': '🇹🇳', 'TO': '🇹🇴',
	'TR': '🇹🇷', 'TT': '🇹🇹', 'TV': '🇹🇻', 'TW': '🇹🇼', 'TZ': '🇹🇿', 'UA': '🇺🇦', 'UG': '🇺🇬', 'UM': '🇺🇲',
	'US': '🇺🇸', 'UY': '🇺🇾', 'UZ': '🇺🇿', 'VA': '🇻🇦', 'VC': '🇻🇨', 'VE': '🇻🇪', 'VG': '🇻🇬', 'VI': '🇻🇮',
	'VN': '🇻🇳', 'VU': '🇻🇺', 'WF': '🇼🇫', 'WS': '🇼🇸', 'YE': '🇾🇪', 'YT': '🇾🇹', 'ZA': '🇿🇦', 'ZM': '🇿🇲',
	'ZW': '🇿🇼',
	}

	class WebInterface:
	def __init__(self, data_manager: DataManager):
	self.data_manager = data_manager
	self._img_cache = _LRUCache(maxsize=3)
	self._sprite_cache = _LRUCache(maxsize=3)

	def get_country_flag(self, country_code):
	if not country_code or len(country_code) != 2:
	return ""
	return _COUNTRY_FLAGS.get(country_code.upper(), "")

	def multiple_image_search(self, img, results):
	cached = self._img_cache.get(img, results)
	if cached is not None:
	return cached
	try:
	result = image_search_performers(img, self.data_manager, results)
	self._img_cache.put(result, img, results)
	return result
	except ValueError as e:
	if "No faces found" in str(e):
	return {"error": "No faces detected in the uploaded image. Please try uploading an image with visible faces."}
	else:
	raise e

	def format_results_for_visual_display(self, json_results):
	if not json_results:
	return "<p>No faces detected or no matches found.</p>"

	if isinstance(json_results, dict) and "error" in json_results:
	return f"""
	<div class="performer-card">
	<div class="face-info">
	<h3 style="color: #ff6b6b;">Error</h3>
	<p>{json_results['error']}</p>
	</div>
	</div>
	"""

	html_parts = []

	html_parts.append("""
	<style>
	body, .gradio-container {
	background-color: #1e1e1e !important;
	color: #d4d4d4 !important;
	}
	.performer-card {
	border: 1px solid #404040;
	border-radius: 16px;
	padding: 24px;
	margin: 16px 0;
	background: #2d2d2d;
	box-shadow: 0 4px 12px rgba(0,0,0,0.3);
	color: #d4d4d4;
	}
	.face-info {
	background: #3c3c3c;
	padding: 12px 16px;
	border-radius: 10px;
	margin-bottom: 24px;
	border: 1px solid #4a4a4a;
	display: flex;
	align-items: center;
	gap: 16px;
	}
	.face-info-content {
	flex: 1;
	}
	.face-info h3 {
	color: #ffffff;
	margin: 0 0 4px 0;
	font-size: 1.1em;
	}
	.face-info p {
	color: #cccccc;
	margin: 2px 0;
	font-size: 0.9em;
	}
	.face-info p strong {
	color: #9cdcfe;
	}
	.detected-face img {
	width: 72px;
	height: 72px;
	border-radius: 8px;
	object-fit: cover;
	border: 2px solid #569cd6;
	box-shadow: 0 2px 8px rgba(0,0,0,0.3);
	flex-shrink: 0;
	}
	.performer-grid {
	display: grid;
	grid-template-columns: repeat(auto-fit, minmax(280px, 1fr));
	gap: 24px;
	margin-top: 8px;
	}
	.performer-item {
	border: 1px solid #4a4a4a;
	border-radius: 16px;
	padding: 0;
	background: #333333;
	transition: all 0.3s ease;
	box-shadow: 0 2px 8px rgba(0,0,0,0.2);
	display: flex;
	flex-direction: column;
	overflow: hidden;
	}
	.performer-item:hover {
	border-color: #569cd6;
	box-shadow: 0 8px 24px rgba(0,0,0,0.5);
	transform: translateY(-4px);
	}
	.performer-image-wrap {
	width: 100%;
	background: #2a2a2a;
	display: flex;
	justify-content: center;
	align-items: center;
	overflow: hidden;
	}
	.performer-image {
	width: 100%;
	max-height: 280px;
	object-fit: cover;
	object-position: top;
	display: block;
	border-bottom: 1px solid #4a4a4a;
	transition: transform 0.4s ease;
	}
	.performer-image:hover {
	transform: scale(1.04);
	}
	.performer-body {
	padding: 16px;
	display: flex;
	flex-direction: column;
	gap: 8px;
	}
	.performer-header {
	display: flex;
	align-items: center;
	flex-wrap: wrap;
	gap: 8px;
	}
	.performer-name {
	color: #ffffff;
	margin: 0;
	font-size: 1.15em;
	font-weight: 600;
	line-height: 1.3;
	}
	.performer-name a {
	color: #569cd6;
	text-decoration: none;
	transition: color 0.3s ease;
	}
	.performer-name a:hover {
	color: #9cdcfe;
	text-decoration: underline;
	}
	.performer-country {
	color: #999999;
	font-size: 0.85em;
	margin-left: auto;
	}
	.country-flag {
	font-size: 1.1em;
	margin-right: 4px;
	vertical-align: middle;
	}
	.confidence-bar {
	background: #404040;
	border-radius: 8px;
	overflow: hidden;
	height: 24px;
	margin: 4px 0 0 0;
	border: 1px solid #4a4a4a;
	width: 100%;
	}
	.confidence-fill {
	height: 100%;
	transition: width 0.5s ease;
	text-align: center;
	line-height: 24px;
	color: white;
	font-size: 12px;
	font-weight: bold;
	text-shadow: 0 1px 2px rgba(0,0,0,0.5);
	}
	.high-confidence {
	background: linear-gradient(135deg, #4caf50, #66bb6a);
	}
	.medium-confidence {
	background: linear-gradient(135deg, #ff9800, #ffb74d);
	}
	.low-confidence {
	background: linear-gradient(135deg, #f44336, #ef5350);
	}
	.source-badge {
	display: inline-block;
	width: 18px;
	height: 18px;
	border-radius: 50%;
	text-align: center;
	line-height: 18px;
	font-size: 10px;
	font-weight: 700;
	color: #fff;
	vertical-align: middle;
	cursor: help;
	}
	.gender-badge {
	display: inline-block;
	padding: 2px 10px;
	border-radius: 10px;
	font-size: 0.72em;
	font-weight: 600;
	letter-spacing: 0.02em;
	vertical-align: middle;
	border: 1px solid transparent;
	}
	.gender-female {
	background: rgba(220, 120, 200, 0.18);
	color: #e8a8d8;
	border-color: rgba(220, 120, 200, 0.35);
	}
	.gender-male {
	background: rgba(100, 160, 230, 0.18);
	color: #9cc2ee;
	border-color: rgba(100, 160, 230, 0.35);
	}
	.gender-non-binary {
	background: rgba(120, 210, 130, 0.18);
	color: #a8e0b0;
	border-color: rgba(120, 210, 130, 0.35);
	}
	.gender-transgender-female {
	background: rgba(220, 140, 200, 0.12);
	color: #d8a8c8;
	border-color: rgba(220, 140, 200, 0.28);
	}
	.gender-transgender-male {
	background: rgba(100, 160, 230, 0.12);
	color: #a8c0e0;
	border-color: rgba(100, 160, 230, 0.28);
	}
	.source-stashdb { background: #4a6fa5; }
	.source-javstash { background: #c0392b; }
	.source-fansdb { background: #27ae60; }
	</style>
	""")

	for i, face_result in enumerate(json_results):
	face_confidence = face_result['confidence']
	performers = face_result['performers']
	face_image_b64 = f"data:image/jpeg;base64,{face_result['image']}"

	html_parts.append(f"""
	<div class="performer-card">
	<div class="face-info">
	<div class="detected-face">
	<img src="{face_image_b64}" alt="Detected Face {i+1}">
	</div>
	<div class="face-info-content">
	<h3>Face {i+1}</h3>
	<p><strong>Detection:</strong> {face_confidence:.1%}  \|  <strong>Matches:</strong> {len(performers)}</p>
	</div>
	</div>
	""")

	if performers:
	html_parts.append('<div class="performer-grid">')
	for performer in performers:
	confidence_class = "high-confidence" if performer['confidence'] >= 80 else "medium-confidence" if performer['confidence'] >= 60 else "low-confidence"
	country_code = performer.get('country', '')
	country_flag = self.get_country_flag(country_code)
	country_display = f"{country_flag} {country_code}" if country_flag else (country_code if country_code else 'Unknown')

	source = performer.get('source', '')
	source_badge = ''
	if source:
	initial = source[0].upper()
	source_badge = f'<span class="source-badge source-{source}" title="{source}">{initial}</span>'

	gender_raw = performer.get('gender') or ''
	gender_badge = ''
	if gender_raw:
	gender_key = gender_raw.lower().replace('_', '-')
	gender_display = gender_raw.lower().replace('_', ' ').title()
	if gender_key == 'non-binary':
	gender_display = 'Non-binary'
	gender_badge = f'<span class="gender-badge gender-{gender_key}">{gender_display}</span>'

	html_parts.append(f"""
	<div class="performer-item">
	<div class="performer-image-wrap">
	<img src="{performer['image']}" alt="{performer['name']}" class="performer-image" onerror="this.style.display='none'">
	</div>
	<div class="performer-body">
	<div class="performer-header">
	<h4 class="performer-name"><a href="{performer['performer_url']}" target="_blank">{performer['name']}</a></h4>
	{source_badge}
	{gender_badge}
	<span class="performer-country"><span class="country-flag">{country_flag}</span>{country_code if country_code else 'Unknown'}</span>
	</div>
	<div class="confidence-bar">
	<div class="confidence-fill {confidence_class}" style="width: {performer['confidence']}%">
	{performer['confidence']}%
	</div>
	</div>
	</div>
	</div>
	""")
	html_parts.append('</div>')
	else:
	html_parts.append('<p><em>No performer matches found for this face.</em></p>')

	html_parts.append('</div>')

	return ''.join(html_parts)

	def multiple_image_search_with_visual(self, img, results):
	"""Run face search and return HTML for visual display."""
	try:
	json_results = self.multiple_image_search(img, results)
	return self.format_results_for_visual_display(json_results)
	except Exception as e:
	return f"<div class='performer-card'><h3>Error</h3><p>{str(e)}</p></div>"

	def search_by_vector(self, payload):
	"""Search the AdaFace index directly with a raw 512-dim embedding.

	Expects payload = {"embedding": [512 floats], "top_k": 5}.
	Skips detection and embedding generation entirely.
	"""
	if not isinstance(payload, dict):
	return {"error": "Payload must be a JSON object: {\"embedding\": [512 floats], \"top_k\": 5}"}

	embedding = payload.get("embedding")
	if embedding is None:
	return {"error": "Missing 'embedding' field."}
	try:
	emb = np.asarray(embedding, dtype=np.float32)
	except (TypeError, ValueError):
	return {"error": "'embedding' must be an array of 512 numbers."}
	if emb.ndim != 1 or emb.shape[0] != 512:
	return {"error": f"'embedding' must have exactly 512 dimensions, got {emb.size}."}

	norm = float(np.linalg.norm(emb))
	if norm < 1e-12:
	return {"error": "'embedding' must be a non-zero vector."}
	emb = emb / norm

	try:
	top_k = int(payload.get("top_k", 5))
	except (TypeError, ValueError):
	return {"error": "'top_k' must be an integer."}
	top_k = max(1, min(top_k, 50))

	ids, dists = self.data_manager.query_adaface_index(emb, top_k)

	performers = []
	for pid, dist in zip(ids, dists):
	info = self.data_manager.get_performer_info(pid, _dist_to_confidence(dist), dist)
	if info is not None:
	performers.append(info)

	return {"performers": performers, "count": len(performers)}

	def _create_vector_search_interface(self):
	"""Create the raw-vector JSON API search interface"""
	with gr.Blocks() as interface:
	gr.Markdown("# Vector Search API")
	gr.Markdown("Search with a raw 512-dim AdaFace embedding (no image upload).")

	with gr.Row():
	with gr.Column():
	payload_input = gr.JSON(
	label="Request",
	value={"embedding": [0.0] * 512, "top_k": 5},
	)
	search_btn = gr.Button("Search")

	with gr.Column():
	json_output = gr.JSON(label="JSON Results")

	search_btn.click(
	fn=self.search_by_vector,
	inputs=[payload_input],
	outputs=json_output,
	api_name="search_by_vector"
	)

	return interface

	def _create_json_search_interface(self):
	"""Create the JSON API search interface"""
	with gr.Blocks() as interface:
	gr.Markdown("# Face Recognition API")
	gr.Markdown("Upload an image and get JSON results - perfect for API integration.")

	with gr.Row():
	with gr.Column():
	img_input = gr.Image(type="pil")
	results_count = gr.Slider(
	label="Top matches",
	minimum=3,
	maximum=50,
	value=5,
	step=1
	)
	search_btn = gr.Button("Search")

	with gr.Column():
	json_output = gr.JSON(label="JSON Results")

	search_btn.click(
	fn=self.multiple_image_search,
	inputs=[img_input, results_count],
	outputs=json_output,
	api_name="multiple_image_search"
	)

	return interface

	def identify_sprite_performers_wrapper(self, sprite_img, vtt_file, threshold):
	if sprite_img is None:
	yield {"error": "Please upload a sprite image."}
	return
	if vtt_file is None:
	yield {"error": "Please upload a VTT file."}
	return
	try:
	if hasattr(vtt_file, 'read'):
	vtt_text = vtt_file.read().decode('utf-8')
	else:
	with open(vtt_file, 'r', encoding='utf-8') as f:
	vtt_text = f.read()
	except Exception as e:
	yield {"error": f"Failed to read VTT file: {e}"}
	return

	cached = self._sprite_cache.get(sprite_img, vtt_text, threshold)
	if cached is not None:
	yield cached
	return

	gen = identify_sprite_performers(
	sprite_img, vtt_text, self.data_manager,
	detection_threshold=threshold / 100.0,
	)
	try:
	final = None
	for item in gen:
	if isinstance(item, tuple) and len(item) == 2:
	progress_val, desc = item
	yield {"progress": progress_val, "desc": desc}
	else:
	final = item
	yield item
	return
	except ValueError as e:
	yield {"error": str(e)}
	finally:
	if final is not None:
	self._sprite_cache.put(final, sprite_img, vtt_text, threshold)

	def _create_sprite_search_interface(self):
	with gr.Blocks() as interface:
	gr.Markdown("# Sprite/VTT Batch Face Identification")
	gr.Markdown("Upload a scene sprite image and its VTT file to identify all performers across the entire scene.")

	with gr.Row():
	with gr.Column():
	sprite_input = gr.Image(type="pil")
	vtt_input = gr.File(label="VTT File", file_types=[".vtt"])
	threshold = gr.Slider(
	label="Detection threshold",
	minimum=1,
	maximum=100,
	value=50,
	step=1,
	)
	search_btn = gr.Button("Identify Performers")

	with gr.Column():
	json_output = gr.JSON(label="Identification Results")

	search_btn.click(
	fn=self.identify_sprite_performers_wrapper,
	inputs=[sprite_input, vtt_input, threshold],
	outputs=json_output,
	api_name="identify_sprite_performers",
	)

	return interface

	def _create_visual_search_interface(self):
	with gr.Blocks() as interface:
	gr.Markdown("# Who is in the photo?")
	gr.Markdown("Upload an image of a person(s) and we'll show you who it is with photos and details.")

	with gr.Row():
	with gr.Column():
	img_input = gr.Image(type="pil")
	results_count = gr.Slider(
	label="Top matches",
	minimum=3,
	maximum=50,
	value=5,
	step=1
	)
	search_btn = gr.Button("Search")

	with gr.Column():
	performer_info = gr.HTML(
	label="Performer Information",
	value="<p>Upload an image and click search to see results.</p>"
	)

	search_btn.click(
	fn=self.multiple_image_search_with_visual,
	inputs=[img_input, results_count],
	outputs=[performer_info],
	api_name="multiple_image_search_with_visual"
	)

	return interface

	def launch(self, server_name="0.0.0.0", server_port=7860, share=True):
	with gr.Blocks(
	css="""
	.gradio-container {
	background-color: #1e1e1e !important;
	color: #d4d4d4 !important;
	}
	.dark {
	--background-fill-primary: #2d2d2d;
	--background-fill-secondary: #3c3c3c;
	--border-color-primary: #404040;
	--block-title-text-color: #ffffff;
	--body-text-color: #d4d4d4;
	}
	"""
	) as demo:
	with gr.Tabs():
	with gr.TabItem("Visual Search"):
	self._create_visual_search_interface()
	with gr.TabItem("JSON API"):
	self._create_json_search_interface()
	with gr.TabItem("Sprite Search"):
	self._create_sprite_search_interface()
	with gr.TabItem("Vector Search"):
	self._create_vector_search_interface()

	@demo.app.middleware("http")
	async def add_cors(request, call_next):
	response = await call_next(request)
	response.headers["Access-Control-Allow-Origin"] = "*"
	response.headers["Access-Control-Allow-Methods"] = "*"
	response.headers["Access-Control-Allow-Headers"] = "*"
	response.headers["Access-Control-Allow-Credentials"] = "true"
	return response

	demo.queue().launch(server_name=server_name, server_port=server_port, share=share, ssr_mode=False)