File size: 46,536 Bytes
2f0e115 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 | #!/usr/bin/env python
"""Local web GUI for rich CMGUI screenshot summarization."""
from __future__ import annotations
import argparse
import json
import mimetypes
import re
import sys
import threading
import time
import webbrowser
from http import HTTPStatus
from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer
from io import BytesIO
from pathlib import Path
from typing import Any, Dict, List, Optional, Tuple
from urllib.parse import unquote, urlparse
import torch
from PIL import Image
SCRIPT_DIR = Path(__file__).resolve().parent
if str(SCRIPT_DIR) not in sys.path:
sys.path.insert(0, str(SCRIPT_DIR))
from enrich_rich_ocr_evidence import build_ocr_ui_items, filter_ocr_items # noqa: E402
from infer_rich import find_latest_rich_checkpoint, row_result, template_prediction # noqa: E402
from prepare_rich_data import load_ocr_items, safe_text, sha256_file # noqa: E402
from train_rich import ( # noqa: E402
RichCollator,
apply_structured_evidence_predictions,
apply_structured_function_predictions,
load_rich_checkpoint,
move_batch,
natural_prediction_from_text,
prediction_from_summary,
repair_prediction_with_context,
safe_json_loads,
target_schema_is_natural_text,
target_schema_is_summary,
)
DEFAULT_CHECKPOINT = ""
DEFAULT_FUNCTION_THRESHOLD = 0.20
DEFAULT_SEARCH_THRESHOLD = 0.20
DEFAULT_EVIDENCE_THRESHOLD = 0.50
DEFAULT_MAX_STRUCTURED_ITEMS = 8
ALLOWED_IMAGE_EXTS = {".png", ".jpg", ".jpeg", ".webp", ".bmp"}
GENERIC_APP_NAMES = {"", "移动应用", "手机应用", "应用", "App", "APP", "app"}
SUMMARY_SKIP_TEXTS = {
"ADB Keyboard {ON}",
"反馈",
"共享中",
"分享中",
}
GUI_FUNCTION_KEYWORDS = ["确定", "取消", "删除", "分享", "评论", "收藏", "点赞", "关注", "返回", "首页", "消息", "购物车", "购买", "下单", "关闭"]
PASSIVE_SEARCH_TERMS = ["历史搜索", "搜索历史", "热门搜索", "热搜", "猜你想搜", "搜索推荐", "搜索记录"]
HTML_PAGE = r"""
<!doctype html>
<html lang="zh-CN">
<head>
<meta charset="utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1" />
<title>Rich Screenshot Summarizer</title>
<style>
:root {
color-scheme: light;
--bg: #f6f7f9;
--panel: #ffffff;
--line: #d8dde6;
--text: #1d2433;
--muted: #5e6a7d;
--accent: #166a5a;
--accent-2: #b35c16;
--danger: #b3261e;
--shadow: 0 10px 30px rgba(29, 36, 51, 0.08);
}
* { box-sizing: border-box; }
body {
margin: 0;
min-height: 100vh;
background: var(--bg);
color: var(--text);
font-family: "Segoe UI", "Microsoft YaHei", Arial, sans-serif;
letter-spacing: 0;
}
header {
border-bottom: 1px solid var(--line);
background: rgba(255, 255, 255, 0.92);
position: sticky;
top: 0;
z-index: 10;
backdrop-filter: blur(10px);
}
.bar {
max-width: 1180px;
margin: 0 auto;
padding: 14px 20px;
display: flex;
align-items: center;
justify-content: space-between;
gap: 16px;
}
h1 {
font-size: 18px;
line-height: 1.25;
margin: 0;
font-weight: 650;
}
.status {
color: var(--muted);
font-size: 13px;
white-space: nowrap;
}
main {
max-width: 1180px;
margin: 0 auto;
padding: 22px 20px 32px;
display: grid;
grid-template-columns: 360px minmax(0, 1fr);
gap: 18px;
}
section {
background: var(--panel);
border: 1px solid var(--line);
border-radius: 8px;
box-shadow: var(--shadow);
}
.controls { padding: 18px; }
.preview { padding: 14px; }
label {
display: block;
color: var(--muted);
font-size: 13px;
margin: 0 0 7px;
}
input[type="file"], input[type="text"], textarea, select {
width: 100%;
border: 1px solid var(--line);
border-radius: 6px;
padding: 10px 11px;
font: inherit;
font-size: 14px;
background: #fff;
color: var(--text);
min-height: 40px;
}
textarea { resize: vertical; min-height: 76px; }
.field { margin-bottom: 14px; }
.row { display: grid; grid-template-columns: 1fr 1fr; gap: 10px; }
button {
width: 100%;
border: 1px solid #10584a;
border-radius: 6px;
background: var(--accent);
color: #fff;
padding: 11px 14px;
font: inherit;
font-size: 14px;
font-weight: 650;
cursor: pointer;
min-height: 42px;
}
button:disabled { cursor: wait; opacity: 0.72; }
.image-box {
border: 1px solid var(--line);
border-radius: 8px;
background: #eef1f5;
overflow: hidden;
min-height: 260px;
display: grid;
place-items: center;
}
.image-box img {
max-width: 100%;
max-height: 72vh;
display: block;
object-fit: contain;
}
.placeholder { color: var(--muted); font-size: 14px; padding: 30px; text-align: center; }
.result { padding: 18px; display: grid; gap: 16px; }
.summary {
font-size: 17px;
line-height: 1.65;
padding-bottom: 8px;
border-bottom: 1px solid var(--line);
}
.grid {
display: grid;
grid-template-columns: repeat(2, minmax(0, 1fr));
gap: 14px;
}
.block h2 {
margin: 0 0 8px;
font-size: 14px;
line-height: 1.35;
color: var(--accent);
}
ul { margin: 0; padding-left: 18px; }
li { margin: 4px 0; line-height: 1.45; overflow-wrap: anywhere; }
.meta {
display: flex;
flex-wrap: wrap;
gap: 8px;
color: var(--muted);
font-size: 12px;
}
.pill {
border: 1px solid var(--line);
border-radius: 999px;
padding: 3px 8px;
background: #fafbfc;
}
details {
border-top: 1px solid var(--line);
padding-top: 12px;
}
summary { cursor: pointer; color: var(--accent-2); font-size: 13px; }
pre {
white-space: pre-wrap;
overflow-wrap: anywhere;
background: #f3f5f7;
border: 1px solid var(--line);
border-radius: 6px;
padding: 12px;
max-height: 360px;
overflow: auto;
font-size: 12px;
}
.error {
color: var(--danger);
border: 1px solid rgba(179, 38, 30, 0.35);
background: rgba(179, 38, 30, 0.06);
border-radius: 6px;
padding: 11px;
line-height: 1.45;
}
@media (max-width: 860px) {
main { grid-template-columns: 1fr; }
.grid { grid-template-columns: 1fr; }
.bar { align-items: flex-start; flex-direction: column; }
.status { white-space: normal; }
}
</style>
</head>
<body>
<header>
<div class="bar">
<h1>Rich Screenshot Summarizer</h1>
<div id="status" class="status">就绪</div>
</div>
</header>
<main>
<section class="controls">
<form id="form">
<div class="field">
<label for="image">图片</label>
<input id="image" name="image" type="file" accept="image/*" required />
</div>
<div class="row">
<div class="field">
<label for="app">应用名/类型（可选）</label>
<input id="app" name="app" type="text" placeholder="可留空" />
</div>
<div class="field">
<label for="ocr_engine">OCR</label>
<select id="ocr_engine" name="ocr_engine">
<option value="paddleocr" selected>paddleocr</option>
<option value="none">none</option>
</select>
</div>
</div>
<div class="field">
<label for="focus">关注点（可选）</label>
<textarea id="focus" name="focus" placeholder="可留空，例如：搜索结果、价格信息、按钮入口"></textarea>
</div>
<button id="submit" type="submit">生成总结</button>
</form>
</section>
<section class="preview">
<div class="image-box" id="imageBox"><div class="placeholder">未选择图片</div></div>
</section>
<section class="result" style="grid-column: 1 / -1;">
<div id="output" class="placeholder">结果会显示在这里</div>
</section>
</main>
<script>
const form = document.getElementById('form');
const imageInput = document.getElementById('image');
const imageBox = document.getElementById('imageBox');
const output = document.getElementById('output');
const statusEl = document.getElementById('status');
const submit = document.getElementById('submit');
imageInput.addEventListener('change', () => {
const file = imageInput.files && imageInput.files[0];
if (!file) {
imageBox.innerHTML = '<div class="placeholder">未选择图片</div>';
return;
}
const url = URL.createObjectURL(file);
imageBox.innerHTML = '';
const img = document.createElement('img');
img.src = url;
img.onload = () => URL.revokeObjectURL(url);
imageBox.appendChild(img);
});
function escapeHtml(value) {
return String(value ?? '').replace(/[&<>'"]/g, ch => ({
'&': '&', '<': '<', '>': '>', "'": ''', '"': '"'
}[ch]));
}
function listItems(items, mapFn) {
if (!Array.isArray(items) || items.length === 0) return '<span class="placeholder">无</span>';
return '<ul>' + items.map(item => '<li>' + mapFn(item) + '</li>').join('') + '</ul>';
}
function renderResult(data) {
const pred = data.prediction || {};
const outputMode = data.model_output_mode || 'json';
const parseLabel = outputMode === 'summary' ? '输出 summary' : (outputMode === 'natural_text' ? ('自然文本 ' + (data.json_valid ? 'parsed' : 'fallback')) : (outputMode === 'template' ? '模板输出' : ('JSON ' + (data.json_valid ? 'valid' : 'fallback'))));
const visible = listItems(pred['可见文字'], x => escapeHtml(x));
const funcs = listItems(pred['功能入口'], item => {
const evidence = Array.isArray(item.evidence_ids) ? item.evidence_ids.join(', ') : '';
return escapeHtml(item.name || '') + (evidence ? ' <span class="pill">' + escapeHtml(evidence) + '</span>' : '');
});
const interactions = listItems(pred['互动数据'], item => {
const value = item.value ? '：' + item.value : '';
return escapeHtml((item.name || '') + value);
});
const clues = listItems(data.key_ui_clues, item => {
const score = typeof item.score === 'number' ? ' score=' + item.score.toFixed(3) : '';
const text = item.text ? ' ' + item.text : '';
return '<span class="pill">' + escapeHtml(item.element_id || '') + '</span>' + escapeHtml(text + score);
});
output.className = '';
output.innerHTML = `
<div class="summary">${escapeHtml(data.summary || pred['画面总结'] || '')}</div>
<div class="meta">
<span class="pill">${escapeHtml(data.source || '')}</span>
<span class="pill">${escapeHtml(outputMode)}</span>
<span class="pill">${escapeHtml(data.display_mode || 'model')}</span>
<span class="pill">${escapeHtml(parseLabel)}</span>
<span class="pill">OCR ${data.ocr_count ?? 0}</span>
<span class="pill">${Number(data.elapsed_sec || 0).toFixed(2)}s</span>
</div>
<div class="grid">
<div class="block"><h2>可见文字</h2>${visible}</div>
<div class="block"><h2>功能入口</h2>${funcs}</div>
<div class="block"><h2>互动数据</h2>${interactions}</div>
<div class="block"><h2>关键证据</h2>${clues}</div>
</div>
<details><summary>原始 JSON</summary><pre>${escapeHtml(JSON.stringify(data, null, 2))}</pre></details>
`;
if (data.image_url) {
imageBox.innerHTML = `<img src="${escapeHtml(data.image_url)}" alt="uploaded screenshot" />`;
}
}
form.addEventListener('submit', async event => {
event.preventDefault();
const formData = new FormData(form);
submit.disabled = true;
statusEl.textContent = '生成中';
output.className = 'placeholder';
output.textContent = '正在处理图片';
try {
const response = await fetch('/api/summarize', { method: 'POST', body: formData });
const data = await response.json();
if (!response.ok) throw new Error(data.error || '请求失败');
renderResult(data);
statusEl.textContent = '完成';
} catch (error) {
output.className = 'error';
output.textContent = error.message || String(error);
statusEl.textContent = '出错';
} finally {
submit.disabled = false;
}
});
</script>
</body>
</html>
"""
def str_to_bool(value: Any) -> bool:
return str(value).lower() in {"1", "true", "yes", "y"}
def json_bytes(obj: Dict[str, Any], status: int = 200) -> Tuple[int, bytes, str]:
return status, json.dumps(obj, ensure_ascii=False).encode("utf-8"), "application/json; charset=utf-8"
def safe_upload_name(filename: str) -> str:
name = Path(filename or "upload").name
name = re.sub(r"[^A-Za-z0-9._-]+", "_", name).strip("._")
return name or "upload"
def display_app_name(app: str) -> str:
text = safe_text(app)
return "" if text in GENERIC_APP_NAMES else text
def text_bbox(item: Dict[str, Any]) -> Tuple[float, float]:
bbox = item.get("bbox") or []
if isinstance(bbox, list) and len(bbox) >= 4:
try:
return float(bbox[1]), float(bbox[0])
except (TypeError, ValueError):
return 0.0, 0.0
return 0.0, 0.0
def skip_summary_text(text: str) -> bool:
if not text:
return True
if text in SUMMARY_SKIP_TEXTS:
return True
if re.fullmatch(r"[0-9:：/ ._\-]+", text):
return True
if re.fullmatch(r"\d{1,2}:\d{2}", text):
return True
if re.fullmatch(r"[A-Za-z0-9%+\- ]{1,5}", text):
return True
if len(text) == 1 and text not in {"搜"}:
return True
if len(text) == 1 and not re.search(r"[\u4e00-\u9fffA-Za-z]", text):
return True
return False
def center_dialog_texts(row: Dict[str, Any]) -> List[str]:
items: List[Tuple[float, float, str]] = []
for item in row.get("ocr_items", []) or []:
text = safe_text(item.get("text"))[:80]
if not text or skip_summary_text(text):
continue
bbox = item.get("bbox") or []
if not (isinstance(bbox, list) and len(bbox) == 4):
continue
try:
x1, y1, x2, y2 = [float(value) for value in bbox]
except (TypeError, ValueError):
continue
cx = (x1 + x2) / 2.0
cy = (y1 + y2) / 2.0
if 0.18 <= cx <= 0.82 and 0.32 <= cy <= 0.68:
items.append((y1, x1, text))
texts = []
seen = set()
for _, _, text in sorted(items, key=lambda value: (value[0], value[1])):
if text not in seen:
texts.append(text)
seen.add(text)
joined = " ".join(texts)
has_dialog_action = "取消" in joined and "确定" in joined
has_dialog_prompt = any(term in joined for term in ["确认", "是否", "删除", "提示", "商品吗"])
return texts if has_dialog_action and has_dialog_prompt else []
def ordered_ocr_texts(row: Dict[str, Any], max_items: int = 14) -> List[str]:
dialog_texts = center_dialog_texts(row)
candidates: List[Tuple[float, float, str]] = []
for item in row.get("ocr_items", []) or []:
text = safe_text(item.get("text"))[:80]
if skip_summary_text(text):
continue
conf = float(item.get("conf", item.get("ocr_conf", 1.0)) or 1.0)
if conf < 0.5:
continue
y_pos, x_pos = text_bbox(item)
candidates.append((y_pos, x_pos, text))
seen = set()
texts: List[str] = []
for text in dialog_texts:
if text not in seen:
texts.append(text)
seen.add(text)
for _, _, text in sorted(candidates, key=lambda value: (value[0], value[1])):
if text in seen:
continue
seen.add(text)
texts.append(text)
if len(texts) >= max_items:
break
return texts
def gui_item_index(row: Dict[str, Any]) -> Dict[str, Dict[str, Any]]:
index: Dict[str, Dict[str, Any]] = {}
for item in list(row.get("ui_items", []) or []) + list(row.get("ocr_items", []) or []):
for key in ["id", "ocr_id"]:
item_id = safe_text(item.get(key))
if item_id and item_id not in index:
index[item_id] = item
return index
def is_passive_search_text(text: str) -> bool:
return any(term in text for term in PASSIVE_SEARCH_TERMS)
def clean_function_list(row: Dict[str, Any], funcs: List[Any], max_items: int) -> List[Dict[str, Any]]:
index = gui_item_index(row)
cleaned: List[Dict[str, Any]] = []
seen = set()
for item in funcs or []:
if not isinstance(item, dict):
name = safe_text(item)
evidence_ids: List[str] = []
else:
name = safe_text(item.get("name"))
evidence_ids = [safe_text(value) for value in item.get("evidence_ids", []) if safe_text(value)]
if not name or name in seen:
continue
evidence_texts = [safe_text(index.get(value, {}).get("text")) for value in evidence_ids]
if any(skip_summary_text(text) for text in evidence_texts if text):
continue
if "搜索" in name and evidence_texts and all(is_passive_search_text(text) for text in evidence_texts):
continue
cleaned.append({"name": name, "evidence_ids": evidence_ids})
seen.add(name)
if len(cleaned) >= max_items:
break
return cleaned
def collect_gui_functions(row: Dict[str, Any], max_items: int) -> List[Dict[str, Any]]:
funcs: List[Dict[str, Any]] = []
seen = set()
for item in row.get("ui_items", []) or []:
text = safe_text(item.get("text"))
evidence_id = safe_text(item.get("id") or item.get("ocr_id"))
if not text or not evidence_id or skip_summary_text(text):
continue
name = ""
if (text in {"搜索", "搜"} or text.startswith("搜索")) and not is_passive_search_text(text):
name = "搜索"
else:
for keyword in GUI_FUNCTION_KEYWORDS:
if keyword in text:
name = keyword
break
if not name or name in seen:
continue
funcs.append({"name": name, "evidence_ids": [evidence_id]})
seen.add(name)
if len(funcs) >= max_items:
break
return funcs
def clean_evidence_ids(row: Dict[str, Any], evidence_ids: List[Any], max_items: int) -> List[str]:
index = gui_item_index(row)
cleaned: List[str] = []
seen = set()
for value in evidence_ids or []:
evidence_id = safe_text(value)
if not evidence_id or evidence_id in seen:
continue
text = safe_text(index.get(evidence_id, {}).get("text"))
if text and skip_summary_text(text):
continue
cleaned.append(evidence_id)
seen.add(evidence_id)
if len(cleaned) >= max_items:
break
return cleaned
def infer_page_type(texts: List[str]) -> str:
joined = " ".join(texts)
if "历史搜索" in joined or "搜索" in joined:
if "热榜" in joined or "热搜" in joined or "猜你喜欢" in joined:
return "搜索和推荐内容页面"
return "搜索相关页面"
if any(term in joined for term in ["购物车", "下单", "购买", "商品", "价格", "优惠"]):
return "购物页面"
if any(term in joined for term in ["热榜", "榜单", "猜你喜欢", "推荐"]):
return "推荐内容页面"
if any(term in joined for term in ["评论", "点赞", "收藏", "分享"]):
return "内容互动页面"
if any(term in joined for term in ["我的", "订单", "设置", "账号", "会员"]):
return "个人或设置页面"
return "移动应用页面"
def quoted_join(values: List[str], limit: int) -> str:
return "、".join(f"“{value}”" for value in values[:limit])
def build_grounded_summary(row: Dict[str, Any]) -> str:
texts = ordered_ocr_texts(row, max_items=14)
dialog_texts = center_dialog_texts(row)
app = display_app_name(str(row.get("app") or ""))
page_type = infer_page_type(texts)
subject = f"{app}的{page_type}" if app else page_type
if not texts:
return f"这张截图展示的是{subject}，但当前没有识别到足够清晰的屏幕文字。"
if dialog_texts:
prompt = next((text for text in dialog_texts if any(term in text for term in ["确认", "是否", "删除", "商品吗"])), dialog_texts[0])
actions = [text for text in dialog_texts if text in {"取消", "确定", "删除", "关闭"}]
action_text = f"，提供{quoted_join(actions, 4)}等按钮" if actions else ""
return f"这张截图显示{subject}上弹出确认对话框，提示“{prompt}”{action_text}；背景是购物车商品列表。"
primary = quoted_join(texts, 5)
summary = f"这张截图主要是{subject}，屏幕上能看到{primary}等文字。"
extra = texts[5:10]
if extra:
summary += f" 下方还出现{quoted_join(extra, 5)}等条目。"
return summary
def merge_items(primary: List[Any], secondary: List[Any], max_items: int) -> List[Any]:
merged: List[Any] = []
seen = set()
for item in list(primary or []) + list(secondary or []):
if isinstance(item, dict):
key = json.dumps(item, ensure_ascii=False, sort_keys=True)
else:
key = safe_text(item)
if not key or key in seen:
continue
seen.add(key)
merged.append(item)
if len(merged) >= max_items:
break
return merged
def gui_ground_prediction(row: Dict[str, Any], pred_obj: Optional[Dict[str, Any]], args: argparse.Namespace) -> Dict[str, Any]:
pred = pred_obj if isinstance(pred_obj, dict) else {}
template = template_prediction(row, max_visible=args.max_visible_text)
grounded = {
"画面总结": safe_text(pred.get("画面总结") or pred.get("summary_zh") or pred.get("summary")),
"可见文字": pred.get("可见文字") or pred.get("visible_text") or [],
"互动数据": pred.get("互动数据") or pred.get("interaction_data") or [],
"功能入口": pred.get("功能入口") or pred.get("ui_functions") or [],
"关键证据": pred.get("关键证据") or pred.get("key_ui_clues") or pred.get("evidence") or [],
}
model_summary = safe_text(grounded.get("画面总结"))
if row.get("ocr_items") and (args.gui_summary_mode == "ocr" or (args.gui_summary_mode == "auto" and not model_summary)):
grounded["画面总结"] = build_grounded_summary(row)
if row.get("ocr_items"):
grounded["可见文字"] = ordered_ocr_texts(row, max_items=args.max_visible_text)
max_functions = int(getattr(args, "structured_max_functions", None) or 12)
gui_functions = collect_gui_functions(row, max_functions)
model_functions = clean_function_list(row, grounded.get("功能入口", []), max_functions)
if args.merge_ocr_functions:
grounded["功能入口"] = merge_items(
model_functions,
gui_functions,
max_functions,
)
elif not grounded.get("功能入口"):
grounded["功能入口"] = gui_functions
else:
grounded["功能入口"] = model_functions
if not grounded.get("互动数据"):
grounded["互动数据"] = template.get("互动数据", [])
function_evidence = []
for function in grounded.get("功能入口", []) or []:
if isinstance(function, dict):
function_evidence.extend(function.get("evidence_ids", []) or [])
evidence_candidates = function_evidence + list(grounded.get("关键证据", []) or [])
if not evidence_candidates:
evidence_candidates = list(template.get("关键证据", []) or [])
grounded["关键证据"] = clean_evidence_ids(row, evidence_candidates, 8)
if not grounded.get("画面总结"):
grounded["画面总结"] = template.get("画面总结", "")
return grounded
def parse_multipart(headers: Any, body: bytes) -> Dict[str, Any]:
content_type = headers.get("Content-Type", "")
match = re.search(r"boundary=(?P<q>\"?)([^\";]+)(?P=q)", content_type)
if not match:
raise ValueError("Missing multipart boundary.")
boundary = ("--" + match.group(2)).encode("utf-8")
fields: Dict[str, Any] = {}
for part in body.split(boundary):
part = part.strip(b"\r\n")
if not part or part == b"--":
continue
header_blob, _, value = part.partition(b"\r\n\r\n")
if not header_blob:
continue
header_text = header_blob.decode("utf-8", errors="replace")
disposition = ""
for line in header_text.split("\r\n"):
if line.lower().startswith("content-disposition:"):
disposition = line.split(":", 1)[1]
break
name_match = re.search(r'name="([^"]+)"', disposition)
if not name_match:
continue
field_name = name_match.group(1)
filename_match = re.search(r'filename="([^"]*)"', disposition)
if filename_match:
fields[field_name] = {
"filename": filename_match.group(1),
"content": value.rstrip(b"\r\n"),
}
else:
fields[field_name] = value.rstrip(b"\r\n").decode("utf-8", errors="replace")
return fields
class RichGuiPredictor:
def __init__(self, args: argparse.Namespace):
self.args = args
self.device = torch.device(args.device if args.device else ("cuda" if torch.cuda.is_available() else "cpu"))
self.model = None
self.tokenizer = None
self.image_processor = None
self.ckpt_args = None
self.collator = None
self.lock = threading.Lock()
def load_model(self) -> None:
if self.args.template_only or self.model is not None:
return
if not self.args.checkpoint:
raise FileNotFoundError("Checkpoint not set. Train a natural multimodal checkpoint first or pass --checkpoint.")
checkpoint = Path(self.args.checkpoint)
if not checkpoint.exists():
raise FileNotFoundError(f"Checkpoint not found: {checkpoint}")
self.model, self.tokenizer, self.image_processor, self.ckpt_args = load_rich_checkpoint(str(checkpoint), self.device)
self.apply_runtime_args(self.ckpt_args)
self.collator = RichCollator(self.tokenizer, self.image_processor, self.ckpt_args)
def apply_runtime_args(self, ckpt_args: argparse.Namespace) -> None:
optional_names = [
"generation_no_repeat_ngram_size",
"generation_repetition_penalty",
"generation_block_extra_ids",
"generation_block_title_prefix",
"generation_force_json_start",
"context_summary_repair",
"canonicalize_targets",
"drop_bare_search_functions",
"structured_function_threshold",
"structured_search_threshold",
"structured_max_functions",
"structured_strict_search_candidates",
"structured_evidence_threshold",
"structured_max_evidence",
"structured_evidence_fallback_top1",
]
for name in optional_names:
value = getattr(self.args, name, None)
if value is not None and value != "":
setattr(ckpt_args, name, value)
for name in ["structured_function_mode", "structured_evidence_mode"]:
value = getattr(self.args, name, "")
if value:
setattr(ckpt_args, name, value)
ckpt_args.num_workers = 0
def save_image(self, upload: Dict[str, Any]) -> Path:
raw = upload.get("content") or b""
if not raw:
raise ValueError("Uploaded image is empty.")
filename = safe_upload_name(upload.get("filename", "upload.png"))
suffix = Path(filename).suffix.lower()
if suffix and suffix not in ALLOWED_IMAGE_EXTS:
raise ValueError(f"Unsupported image type: {suffix}")
upload_dir = Path(self.args.upload_dir)
upload_dir.mkdir(parents=True, exist_ok=True)
with Image.open(BytesIO(raw)) as img:
image = img.convert("RGB")
stamp = time.strftime("%Y%m%d_%H%M%S")
upload_path = upload_dir / f"{stamp}_{int(time.time() * 1000) % 1000:03d}_{Path(filename).stem}.png"
image.save(upload_path)
return upload_path
def build_row(self, image_path: Path, app: str, focus: str, ocr_engine: str) -> Tuple[Dict[str, Any], Optional[str]]:
model_instruction = safe_text(focus) if self.args.use_focus_in_model else ""
row: Dict[str, Any] = {
"screen_id": image_path.stem,
"image_path": str(image_path),
"app": safe_text(app) or "移动应用",
"instruction": model_instruction,
"display_focus": safe_text(focus),
"target": {
"summary_zh": "",
"visible_text": [],
"interaction_data": [],
"ui_functions": [],
"key_ui_clues": [],
},
"ocr_items": [],
"ui_items": [],
"weak_evidence_ids": [],
}
ocr_error = None
if ocr_engine != "none":
try:
cache_args = argparse.Namespace(ocr_engine=ocr_engine, ocr_lang=self.args.ocr_lang)
image_sha = sha256_file(image_path)
ocr_items = load_ocr_items(image_path, image_sha, Path(self.args.ocr_cache_dir), cache_args)
ocr_items = filter_ocr_items(ocr_items, self.args.min_ocr_conf)[: self.args.max_ocr_items]
row["ocr_items"] = ocr_items
row["ui_items"] = build_ocr_ui_items(row, ocr_items, self.args.max_ui_items)
row["weak_evidence_ids"] = [item.get("id") for item in row["ui_items"][:8] if item.get("id")]
except Exception as exc: # OCR should not prevent image-only inference.
ocr_error = str(exc)
return row, ocr_error
@torch.no_grad()
def summarize(self, image_path: Path, app: str, focus: str, ocr_engine: str) -> Dict[str, Any]:
start = time.perf_counter()
row, ocr_error = self.build_row(image_path, app, focus, ocr_engine)
if self.args.template_only:
pred = template_prediction(row, max_visible=self.args.max_visible_text)
pred = gui_ground_prediction(row, pred, self.args)
result = row_result(
row=row,
raw_text=json.dumps(pred, ensure_ascii=False),
pred_obj=pred,
json_valid=True,
evidence_scores=None,
allow_template_fallback=False,
source="template",
)
else:
with self.lock:
self.load_model()
batch = self.collator([row])
batch = move_batch(batch, self.device)
text = self.model.generate_text(
batch,
self.tokenizer,
num_beams=self.args.num_beams,
max_new_tokens=self.args.max_new_tokens,
)[0]
_, _, elem_tokens, elem_key_padding = self.model.build_memory(batch)
masks = (~elem_key_padding)[0].detach().cpu().numpy()
evidence_head = torch.sigmoid(self.model.evidence_head(elem_tokens).squeeze(-1))[0].detach().cpu()
function_head = torch.sigmoid(self.model.ui_function_head(elem_tokens).squeeze(-1))[0].detach().cpu()
search_head = torch.sigmoid(self.model.search_function_head(elem_tokens).squeeze(-1))[0].detach().cpu()
output_is_summary = target_schema_is_summary(getattr(self.ckpt_args, "target_schema", "zh"))
output_is_natural = target_schema_is_natural_text(getattr(self.ckpt_args, "target_schema", "zh"))
if output_is_summary:
pred_obj = prediction_from_summary(row, text)
ok = True
elif output_is_natural:
pred_obj = natural_prediction_from_text(text)
ok = bool(pred_obj.get("画面总结"))
else:
pred_obj, ok = safe_json_loads(text)
if pred_obj is None:
elements = row.get("ui_items", []) or []
ranked = torch.argsort(evidence_head, descending=True).tolist()
top_ids = []
for idx in ranked:
if idx < len(elements) and idx < len(masks) and masks[idx]:
evidence_id = safe_text(elements[idx].get("id") or elements[idx].get("ocr_id"))
if evidence_id:
top_ids.append(evidence_id)
if len(top_ids) >= self.args.top_k_clues:
break
pred_obj = {"关键证据": top_ids}
if self.args.context_summary_repair:
pred_obj, _ = repair_prediction_with_context(row, pred_obj)
ok = True
pred_obj = apply_structured_function_predictions(row, pred_obj, function_head, search_head, self.ckpt_args)
pred_obj = apply_structured_evidence_predictions(row, pred_obj, evidence_head, self.ckpt_args)
pred_obj = gui_ground_prediction(row, pred_obj, self.args)
evidence_scores: Dict[str, float] = {}
for idx, elem in enumerate(row.get("ui_items", []) or []):
if idx < len(masks) and masks[idx]:
evidence_id = safe_text(elem.get("id") or elem.get("ocr_id"))
if evidence_id:
evidence_scores[evidence_id] = float(evidence_head[idx])
result = row_result(
row=row,
raw_text=text,
pred_obj=pred_obj,
json_valid=ok,
evidence_scores=evidence_scores,
allow_template_fallback=self.args.allow_template_fallback,
source="model",
)
result["ocr_count"] = len(row.get("ocr_items", []) or [])
result["ui_item_count"] = len(row.get("ui_items", []) or [])
result["ocr_error"] = ocr_error
result["image_url"] = f"/uploads/{image_path.name}"
result["focus"] = safe_text(focus)
if self.args.template_only:
result["model_output_mode"] = "template"
elif self.ckpt_args is not None and target_schema_is_summary(getattr(self.ckpt_args, "target_schema", "zh")):
result["model_output_mode"] = "summary"
elif self.ckpt_args is not None and target_schema_is_natural_text(getattr(self.ckpt_args, "target_schema", "zh")):
result["model_output_mode"] = "natural_text"
else:
result["model_output_mode"] = "json"
result["display_mode"] = self.args.gui_summary_mode if row.get("ocr_items") else "model"
result["elapsed_sec"] = round(time.perf_counter() - start, 3)
return result
class RichGuiHandler(BaseHTTPRequestHandler):
server_version = "RichGui/1.0"
def send_payload(self, status: int, body: bytes, content_type: str) -> None:
self.send_response(status)
self.send_header("Content-Type", content_type)
self.send_header("Cache-Control", "no-store, no-cache, must-revalidate, max-age=0")
self.send_header("Pragma", "no-cache")
self.send_header("Content-Length", str(len(body)))
self.end_headers()
self.wfile.write(body)
def do_GET(self) -> None: # noqa: N802
parsed = urlparse(self.path)
if parsed.path in {"/", "/index.html"}:
self.send_payload(HTTPStatus.OK, HTML_PAGE.encode("utf-8"), "text/html; charset=utf-8")
return
if parsed.path == "/api/health":
status, body, content_type = json_bytes(
{
"ok": True,
"template_only": self.server.predictor.args.template_only,
"checkpoint": None if self.server.predictor.args.template_only else self.server.predictor.args.checkpoint,
"structured_function_mode": self.server.predictor.args.structured_function_mode,
"structured_function_threshold": self.server.predictor.args.structured_function_threshold,
"structured_search_threshold": self.server.predictor.args.structured_search_threshold,
"structured_evidence_mode": self.server.predictor.args.structured_evidence_mode,
"structured_evidence_threshold": self.server.predictor.args.structured_evidence_threshold,
}
)
self.send_payload(status, body, content_type)
return
if parsed.path.startswith("/uploads/"):
filename = safe_upload_name(unquote(parsed.path.removeprefix("/uploads/")))
path = Path(self.server.predictor.args.upload_dir) / filename
if path.exists() and path.is_file():
content_type = mimetypes.guess_type(path.name)[0] or "application/octet-stream"
self.send_payload(HTTPStatus.OK, path.read_bytes(), content_type)
else:
status, body, content_type = json_bytes({"error": "Not found"}, HTTPStatus.NOT_FOUND)
self.send_payload(status, body, content_type)
return
status, body, content_type = json_bytes({"error": "Not found"}, HTTPStatus.NOT_FOUND)
self.send_payload(status, body, content_type)
def do_POST(self) -> None: # noqa: N802
parsed = urlparse(self.path)
if parsed.path != "/api/summarize":
status, body, content_type = json_bytes({"error": "Not found"}, HTTPStatus.NOT_FOUND)
self.send_payload(status, body, content_type)
return
try:
length = int(self.headers.get("Content-Length", "0"))
if length > self.server.predictor.args.max_upload_mb * 1024 * 1024:
raise ValueError(f"Image is larger than {self.server.predictor.args.max_upload_mb} MB.")
fields = parse_multipart(self.headers, self.rfile.read(length))
upload = fields.get("image")
if not isinstance(upload, dict):
raise ValueError("Missing image field.")
image_path = self.server.predictor.save_image(upload)
app = safe_text(fields.get("app")) or "移动应用"
focus = safe_text(fields.get("focus")) or safe_text(fields.get("instruction"))
ocr_engine = safe_text(fields.get("ocr_engine")) or self.server.predictor.args.ocr_engine
if ocr_engine not in {"none", "paddleocr"}:
ocr_engine = self.server.predictor.args.ocr_engine
result = self.server.predictor.summarize(image_path, app, focus, ocr_engine)
status, body, content_type = json_bytes(result, HTTPStatus.OK)
except Exception as exc:
status, body, content_type = json_bytes({"error": str(exc)}, HTTPStatus.BAD_REQUEST)
self.send_payload(status, body, content_type)
def log_message(self, fmt: str, *args: Any) -> None:
if not self.server.predictor.args.quiet:
super().log_message(fmt, *args)
class RichGuiServer(ThreadingHTTPServer):
def __init__(self, server_address: Tuple[str, int], handler_class: Any, predictor: RichGuiPredictor):
super().__init__(server_address, handler_class)
self.predictor = predictor
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(
description="Start a local GUI for rich screenshot summarization.",
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
)
parser.add_argument("--checkpoint", default=DEFAULT_CHECKPOINT)
parser.add_argument("--template_only", action="store_true")
parser.add_argument("--host", default="127.0.0.1")
parser.add_argument("--port", type=int, default=7860)
parser.add_argument("--open_browser", type=str_to_bool, default=True)
parser.add_argument("--quiet", action="store_true")
parser.add_argument("--upload_dir", default="outputs/rich_gui/uploads")
parser.add_argument("--ocr_cache_dir", default="data/rich_cmgui/cache/gui_ocr")
parser.add_argument("--ocr_engine", choices=["none", "paddleocr"], default="paddleocr")
parser.add_argument("--ocr_lang", default="ch")
parser.add_argument("--min_ocr_conf", type=float, default=0.5)
parser.add_argument("--max_ocr_items", type=int, default=120)
parser.add_argument("--max_ui_items", type=int, default=48)
parser.add_argument("--max_upload_mb", type=int, default=20)
parser.add_argument("--device", default="")
parser.add_argument("--num_beams", type=int, default=1)
parser.add_argument("--max_new_tokens", type=int, default=256)
parser.add_argument("--top_k_clues", type=int, default=5)
parser.add_argument("--max_visible_text", type=int, default=12)
parser.add_argument("--gui_summary_mode", choices=["model", "ocr", "auto"], default="model")
parser.add_argument("--merge_ocr_functions", type=str_to_bool, default=True)
parser.add_argument("--use_focus_in_model", type=str_to_bool, default=False)
parser.add_argument("--generation_no_repeat_ngram_size", type=int, default=3)
parser.add_argument("--generation_repetition_penalty", type=float, default=1.1)
parser.add_argument("--generation_block_extra_ids", type=str_to_bool, default=True)
parser.add_argument("--generation_block_title_prefix", type=str_to_bool, default=True)
parser.add_argument("--generation_force_json_start", type=str_to_bool, default=False)
parser.add_argument("--context_summary_repair", type=str_to_bool, default=None)
parser.add_argument("--canonicalize_targets", type=str_to_bool, default=None)
parser.add_argument("--drop_bare_search_functions", type=str_to_bool, default=None)
parser.add_argument("--structured_function_mode", choices=["", "decoder", "heads"], default="heads")
parser.add_argument("--structured_function_threshold", type=float, default=DEFAULT_FUNCTION_THRESHOLD)
parser.add_argument("--structured_search_threshold", type=float, default=DEFAULT_SEARCH_THRESHOLD)
parser.add_argument("--structured_max_functions", type=int, default=DEFAULT_MAX_STRUCTURED_ITEMS)
parser.add_argument("--structured_strict_search_candidates", type=str_to_bool, default=None)
parser.add_argument("--structured_evidence_mode", choices=["", "decoder", "heads"], default="heads")
parser.add_argument("--structured_evidence_threshold", type=float, default=DEFAULT_EVIDENCE_THRESHOLD)
parser.add_argument("--structured_max_evidence", type=int, default=DEFAULT_MAX_STRUCTURED_ITEMS)
parser.add_argument("--structured_evidence_fallback_top1", type=str_to_bool, default=False)
parser.add_argument("--allow_template_fallback", type=str_to_bool, default=False)
args = parser.parse_args()
if not args.template_only and not args.checkpoint:
checkpoint = find_latest_rich_checkpoint()
if checkpoint is None:
raise FileNotFoundError("No stage3/stage4 rich checkpoint found. Train first or pass --checkpoint.")
args.checkpoint = str(checkpoint)
return args
def main() -> None:
args = parse_args()
predictor = RichGuiPredictor(args)
last_error: Optional[OSError] = None
server: Optional[RichGuiServer] = None
for port in range(args.port, args.port + 20):
try:
server = RichGuiServer((args.host, port), RichGuiHandler, predictor)
break
except OSError as exc:
last_error = exc
if server is None:
raise RuntimeError(f"Could not bind a local port starting at {args.port}: {last_error}")
actual_port = server.server_address[1]
url = f"http://{args.host}:{actual_port}/"
print(
json.dumps(
{
"url": url,
"template_only": args.template_only,
"checkpoint": None if args.template_only else args.checkpoint,
"num_beams": args.num_beams,
"max_new_tokens": args.max_new_tokens,
"structured_function_mode": args.structured_function_mode,
"structured_function_threshold": args.structured_function_threshold,
"structured_search_threshold": args.structured_search_threshold,
"structured_evidence_mode": args.structured_evidence_mode,
"structured_evidence_threshold": args.structured_evidence_threshold,
},
ensure_ascii=False,
)
)
if args.open_browser:
threading.Timer(0.5, lambda: webbrowser.open(url)).start()
server.serve_forever()
if __name__ == "__main__":
main()
|