captcha-cloudrun/index.html at main · GitHub30/captcha-cloudrun · GitHub

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<!DOCTYPE html>
<html lang="ja">
  <head>
    <meta charset="UTF-8" />
    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
    <title>Captcha Decoder</title>
    <script src="https://unpkg.com/react@18/umd/react.development.js"></script>
    <script src="https://unpkg.com/react-dom@18/umd/react-dom.development.js"></script>
    <script src="https://unpkg.com/@babel/standalone/babel.min.js"></script>
    <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@4.22.0/dist/tf.min.js"></script>
    <style>
      :root {
        color-scheme: light;
        font-family: Inter, "Segoe UI", sans-serif;
      }

      * {
        box-sizing: border-box;
      }

      body {
        margin: 0;
        min-height: 100vh;
        background: linear-gradient(135deg, #eef4ff 0%, #f9fbff 100%);
        color: #172033;
      }

      .container {
        max-width: 860px;
        margin: 0 auto;
        padding: 32px 16px 56px;
      }

      .card {
        background: #fff;
        border-radius: 18px;
        box-shadow: 0 18px 40px rgba(33, 66, 131, 0.12);
        padding: 24px;
      }

      h1 {
        margin: 0 0 8px;
        font-size: 2rem;
      }

      p {
        line-height: 1.6;
      }

      .uploader {
        border: 2px dashed #a9b9de;
        border-radius: 16px;
        padding: 20px;
        background: #f8fbff;
      }

      .file-input {
        width: 100%;
        padding: 10px;
        border-radius: 10px;
        border: 1px solid #cad5ef;
        background: #fff;
      }

      .toolbar {
        display: flex;
        justify-content: flex-end;
        margin-bottom: 12px;
      }

      .language-select {
        display: inline-flex;
        align-items: center;
        gap: 8px;
        font-size: 0.95rem;
        font-weight: 600;
        color: #214283;
      }

      .language-select select {
        border: 1px solid #cad5ef;
        border-radius: 10px;
        padding: 8px 10px;
        background: #fff;
        color: #172033;
      }

      .sample-list {
        display: flex;
        flex-wrap: wrap;
        gap: 10px;
        margin: 12px 0 14px;
      }

      .sample-chip {
        background: #fff;
        border: 2px solid #cad5ef;
        padding: 4px;
        border-radius: 12px;
        line-height: 0;
      }

      .sample-chip img {
        width: 120px;
        height: 40px;
        object-fit: cover;
        display: block;
        border-radius: 8px;
        background: #f4f7ff;
      }

      .sample-chip.active {
        border-color: #2563eb;
        box-shadow: 0 0 0 3px rgba(37, 99, 235, 0.15);
      }

      .preview {
        margin-top: 16px;
        display: grid;
        gap: 16px;
        grid-template-columns: repeat(auto-fit, minmax(220px, 1fr));
        align-items: start;
      }

      .preview-box,
      .result-box {
        border: 1px solid #e1e8f7;
        border-radius: 14px;
        padding: 16px;
        background: #fff;
      }

      .preview-box img {
        width: 100%;
        max-height: 180px;
        object-fit: contain;
        border-radius: 10px;
        background: #f4f7ff;
      }

      .actions {
        display: flex;
        flex-wrap: wrap;
        gap: 12px;
        margin-top: 18px;
      }

      button {
        border: none;
        border-radius: 10px;
        padding: 12px 18px;
        font-size: 0.95rem;
        font-weight: 700;
        cursor: pointer;
      }

      .primary {
        background: #2563eb;
        color: #fff;
      }

      .secondary {
        background: #e8eefc;
        color: #1e3a8a;
      }

      button:disabled {
        opacity: 0.6;
        cursor: not-allowed;
      }

      .status {
        margin-top: 12px;
        padding: 12px 14px;
        border-radius: 10px;
        background: #eef4ff;
        color: #214283;
      }

      .result {
        font-size: 2rem;
        font-weight: 800;
        letter-spacing: 0.18em;
        margin: 8px 0 0;
        color: #0f172a;
        word-break: break-all;
      }

      .muted {
        color: #5b6780;
      }

      code {
        background: #f1f5ff;
        padding: 2px 6px;
        border-radius: 6px;
      }
    </style>
  </head>
  <body>
    <div id="root"></div>

    <script type="text/babel">
      const { useEffect, useMemo, useRef, useState } = React;
      const SAMPLE_IMAGES = ["captcha1.png", "captcha2.jpg", "captcha3.jpg", "captcha4.jpg"];
      const LANGUAGE_OPTIONS = [
        { value: "en", label: "English" },
        { value: "ja", label: "日本語" },
        { value: "zh-CN", label: "简体中文" },
        { value: "zh-TW", label: "繁體中文" },
        { value: "ko", label: "한국어" },
        { value: "vi", label: "Tiếng Việt" },
      ];

      function detectBrowserLanguage() {
        const languages = navigator.languages?.length ? navigator.languages : [navigator.language || "ja"];

        for (const lang of languages) {
          const code = String(lang).toLowerCase();
          if (code.startsWith("ja")) return "ja";
          if (code.startsWith("en")) return "en";
          if (code.startsWith("zh-tw") || code.startsWith("zh-hk") || code.startsWith("zh-mo")) return "zh-TW";
          if (code.startsWith("zh")) return "zh-CN";
          if (code.startsWith("ko")) return "ko";
          if (code.startsWith("vi")) return "vi";
        }

        return "ja";
      }

      const TEXT = {
        en: {
          appTitle: "Captcha Decoder",
          descriptionBefore: "converted into a TensorFlow.js model and",
          browserOnly: "runs entirely in the browser",
          descriptionAfter: "to display the code.",
          languageLabel: "Language",
          sampleImages: "Sample Images",
          predictButton: "Show code",
          predicting: "Inferring...",
          clear: "Clear",
          previewTitle: "Image Preview",
          previewPlaceholder: "Select an image to preview it here.",
          resultTitle: "Inference Result",
          resultHint: "Generate the code using CTC greedy decode.",
          resultPlaceholder: "----",
          modelLoading: "Loading model...",
          modelLoaded: "Model loaded successfully.",
          invalidImage: "Please select an image file.",
          inferenceFailed: "Inference failed. Please also check the browser console.",
          modelLoadFailed: (detail) => `Model load error: ${detail}. Also confirm the page is opened through a static server.`,
        },
        ja: {
          appTitle: "Captcha Decoder",
          descriptionBefore: "から変換した TensorFlow.js モデルを読み込み、",
          browserOnly: "ブラウザ内だけ",
          descriptionAfter: "で画像推論して code を表示します。",
          languageLabel: "言語",
          sampleImages: "サンプル画像",
          predictButton: "code を表示",
          predicting: "推論中...",
          clear: "クリア",
          previewTitle: "画像プレビュー",
          previewPlaceholder: "画像を選択するとここに表示されます。",
          resultTitle: "推論結果",
          resultHint: "CTC greedy decode で code を生成します。",
          resultPlaceholder: "----",
          modelLoading: "モデルを読み込み中です...",
          modelLoaded: "モデルの読み込みが完了しました。",
          invalidImage: "画像ファイルを選択してください。",
          inferenceFailed: "推論に失敗しました。ブラウザのコンソールも確認してください。",
          modelLoadFailed: (detail) => `モデル読込エラー: ${detail}. 静的サーバー経由で開いていることも確認してください。`,
        },
        "zh-CN": {
          appTitle: "验证码识别器",
          descriptionBefore: "转换得到的 TensorFlow.js 模型，并",
          browserOnly: "仅在浏览器内",
          descriptionAfter: "进行图像推理并显示 code。",
          languageLabel: "语言",
          sampleImages: "示例图片",
          predictButton: "显示 code",
          predicting: "推理中...",
          clear: "清除",
          previewTitle: "图片预览",
          previewPlaceholder: "选择图片后会显示在这里。",
          resultTitle: "推理结果",
          resultHint: "使用 CTC greedy decode 生成 code。",
          resultPlaceholder: "----",
          modelLoading: "正在加载模型...",
          modelLoaded: "模型加载完成。",
          invalidImage: "请选择图片文件。",
          inferenceFailed: "推理失败。请同时检查浏览器控制台。",
          modelLoadFailed: (detail) => `模型加载错误：${detail}。也请确认页面是通过静态服务器打开的。`,
        },
        "zh-TW": {
          appTitle: "驗證碼辨識器",
          descriptionBefore: "轉換得到的 TensorFlow.js 模型，並",
          browserOnly: "僅在瀏覽器內",
          descriptionAfter: "進行影像推論並顯示 code。",
          languageLabel: "語言",
          sampleImages: "範例圖片",
          predictButton: "顯示 code",
          predicting: "推論中...",
          clear: "清除",
          previewTitle: "圖片預覽",
          previewPlaceholder: "選擇圖片後會顯示在這裡。",
          resultTitle: "推論結果",
          resultHint: "使用 CTC greedy decode 產生 code。",
          resultPlaceholder: "----",
          modelLoading: "模型載入中...",
          modelLoaded: "模型載入完成。",
          invalidImage: "請選擇圖片檔案。",
          inferenceFailed: "推論失敗。也請檢查瀏覽器主控台。",
          modelLoadFailed: (detail) => `模型載入錯誤：${detail}。也請確認頁面是透過靜態伺服器開啟的。`,
        },
        ko: {
          appTitle: "캡차 디코더",
          descriptionBefore: "에서 변환한 TensorFlow.js 모델을 불러와",
          browserOnly: "브라우저 안에서만",
          descriptionAfter: "추론하여 code를 표시합니다.",
          languageLabel: "언어",
          sampleImages: "샘플 이미지",
          predictButton: "code 보기",
          predicting: "추론 중...",
          clear: "지우기",
          previewTitle: "이미지 미리보기",
          previewPlaceholder: "이미지를 선택하면 여기에 표시됩니다.",
          resultTitle: "추론 결과",
          resultHint: "CTC greedy decode로 code를 생성합니다.",
          resultPlaceholder: "----",
          modelLoading: "모델을 불러오는 중입니다...",
          modelLoaded: "모델 로딩이 완료되었습니다.",
          invalidImage: "이미지 파일을 선택하세요.",
          inferenceFailed: "추론에 실패했습니다. 브라우저 콘솔도 확인하세요.",
          modelLoadFailed: (detail) => `모델 로드 오류: ${detail}. 정적 서버를 통해 페이지를 열었는지도 확인하세요.`,
        },
        vi: {
          appTitle: "Trình giải Captcha",
          descriptionBefore: "đã chuyển đổi sang mô hình TensorFlow.js và",
          browserOnly: "chạy suy luận hoàn toàn trên trình duyệt",
          descriptionAfter: "để hiển thị code.",
          languageLabel: "Ngôn ngữ",
          sampleImages: "Ảnh mẫu",
          predictButton: "Hiển thị code",
          predicting: "Đang suy luận...",
          clear: "Xóa",
          previewTitle: "Xem trước ảnh",
          previewPlaceholder: "Chọn ảnh để hiển thị tại đây.",
          resultTitle: "Kết quả suy luận",
          resultHint: "Tạo code bằng CTC greedy decode.",
          resultPlaceholder: "----",
          modelLoading: "Đang tải mô hình...",
          modelLoaded: "Đã tải mô hình xong.",
          invalidImage: "Vui lòng chọn một tệp hình ảnh.",
          inferenceFailed: "Suy luận thất bại. Hãy kiểm tra thêm console của trình duyệt.",
          modelLoadFailed: (detail) => `Lỗi tải mô hình: ${detail}. Đồng thời hãy xác nhận trang được mở qua máy chủ tĩnh.`,
        },
      };

      function App() {
        const [language, setLanguage] = useState(detectBrowserLanguage());
        const t = TEXT[language];
        const [model, setModel] = useState(null);
        const [modelStatusKey, setModelStatusKey] = useState("modelLoading");
        const [previewUrl, setPreviewUrl] = useState("");
        const [selectedSample, setSelectedSample] = useState("");
        const [result, setResult] = useState("");
        const [busy, setBusy] = useState(false);
        const [errorKey, setErrorKey] = useState("");
        const [errorDetail, setErrorDetail] = useState("");
        const imageRef = useRef(null);
        const fileInputRef = useRef(null);

        useEffect(() => {
          let cancelled = false;

          async function loadModel() {
            try {
              if (tf.getBackend() !== "webgl") {
                try {
                  await tf.setBackend("webgl");
                } catch {
                  await tf.setBackend("cpu");
                }
              }
              await tf.ready();
              const loadedModel = await tf.loadLayersModel("./web_model/model.json?v=20260407-2");
              if (!cancelled) {
                setModel(loadedModel);
                setModelStatusKey("modelLoaded");
              }
            } catch (err) {
              if (!cancelled) {
                console.error(err);
                setModelStatusKey("modelLoading");
                setErrorKey("modelLoadFailed");
                setErrorDetail(err?.message || String(err));
              }
            }
          }

          loadModel();
          return () => {
            cancelled = true;
          };
        }, []);

        useEffect(() => {
          document.documentElement.lang = language;
        }, [language]);

        useEffect(() => {
          return () => {
            if (previewUrl && previewUrl.startsWith("blob:")) {
              URL.revokeObjectURL(previewUrl);
            }
          };
        }, [previewUrl]);

        const canPredict = useMemo(() => Boolean(model && previewUrl && !busy), [model, previewUrl, busy]);
        const modelStatus = t[modelStatusKey] || "";
        const errorMessage = errorKey === "modelLoadFailed" ? t.modelLoadFailed(errorDetail) : errorKey ? t[errorKey] : "";

        function handleFileChange(event) {
          const file = event.target.files?.[0];
          setResult("");
          setErrorKey("");
          setErrorDetail("");
          setSelectedSample("");

          if (!file) {
            if (previewUrl && previewUrl.startsWith("blob:")) {
              URL.revokeObjectURL(previewUrl);
            }
            setPreviewUrl("");
            return;
          }

          if (!file.type.startsWith("image/")) {
            setErrorKey("invalidImage");
            return;
          }

          if (previewUrl && previewUrl.startsWith("blob:")) {
            URL.revokeObjectURL(previewUrl);
          }
          setPreviewUrl(URL.createObjectURL(file));
        }

        function chooseSample(sampleName) {
          setResult("");
          setErrorKey("");
          setErrorDetail("");
          setSelectedSample(sampleName);
          if (fileInputRef.current) {
            fileInputRef.current.value = "";
          }
          if (previewUrl && previewUrl.startsWith("blob:")) {
            URL.revokeObjectURL(previewUrl);
          }
          setPreviewUrl(`./${sampleName}?v=20260407-2`);
        }

        function handlePreviewLoad() {
          if (selectedSample && model && !busy) {
            inferFromCurrentImage();
          }
        }

        function decodeCaptcha(predictionTensor) {
          const blankIndex = predictionTensor.shape[2] - 1;
          const bestPath = predictionTensor.argMax(-1).dataSync();
          const digits = [];
          let previous = blankIndex;

          for (const index of bestPath) {
            if (index !== blankIndex && index !== previous) {
              digits.push(String(index));
            }
            previous = index;
          }

          return digits.join("");
        }

        async function inferFromCurrentImage() {
          if (!model || !imageRef.current) return;

          setBusy(true);
          setErrorKey("");
          setErrorDetail("");
          setResult("");

          try {
            const code = tf.tidy(() => {
              const tensor = tf.browser
                .fromPixels(imageRef.current)
                .resizeBilinear([60, 300])
                .toFloat()
                .div(255)
                .expandDims(0);

              const prediction = model.predict(tensor);
              return decodeCaptcha(prediction);
            });

            setResult(code || "(空文字)");
          } catch (err) {
            console.error(err);
            setErrorKey("inferenceFailed");
          } finally {
            setBusy(false);
          }
        }

        async function runInference() {
          await inferFromCurrentImage();
        }

        function resetAll() {
          if (previewUrl && previewUrl.startsWith("blob:")) {
            URL.revokeObjectURL(previewUrl);
          }
          if (fileInputRef.current) {
            fileInputRef.current.value = "";
          }
          setPreviewUrl("");
          setSelectedSample("");
          setResult("");
          setErrorKey("");
          setErrorDetail("");
        }

        return (
          <div className="container">
            <div className="card">
              <h1>{t.appTitle}</h1>
              <p className="muted">
                <code>xserver_captcha.keras</code> {t.descriptionBefore}
                <strong>{t.browserOnly}</strong> {t.descriptionAfter}
              </p>

              <div className="uploader">
                <div className="toolbar">
                  <label className="language-select">
                    <span>{t.languageLabel}</span>
                    <select value={language} onChange={(event) => setLanguage(event.target.value)}>
                      {LANGUAGE_OPTIONS.map((option) => (
                        <option key={option.value} value={option.value}>
                          {option.label}
                        </option>
                      ))}
                    </select>
                  </label>
                </div>

                <div>
                  <strong>{t.sampleImages}</strong>
                  <div className="sample-list">
                    {SAMPLE_IMAGES.map((sampleName) => (
                      <button
                        key={sampleName}
                        type="button"
                        className={`sample-chip ${selectedSample === sampleName ? "active" : ""}`}
                        onClick={() => chooseSample(sampleName)}
                        aria-label={sampleName}
                        title={sampleName}
                      >
                        <img src={`./${sampleName}?v=20260407-2`} alt="sample captcha" />
                      </button>
                    ))}
                  </div>
                </div>

                <input
                  ref={fileInputRef}
                  className="file-input"
                  type="file"
                  accept="image/*"
                  onChange={handleFileChange}
                />
                <div className="status">{modelStatus}</div>

                {errorMessage && (
                  <div className="status" style={{ background: "#fff1f2", color: "#be123c" }}>
                    {errorMessage}
                  </div>
                )}

                <div className="actions">
                  <button className="primary" onClick={runInference} disabled={!canPredict}>
                    {busy ? t.predicting : t.predictButton}
                  </button>
                  <button className="secondary" onClick={resetAll} disabled={busy && !previewUrl}>
                    {t.clear}
                  </button>
                </div>

                <div className="preview">
                  <div className="preview-box">
                    <strong>{t.previewTitle}</strong>
                    <div style={{ marginTop: 12 }}>
                      {previewUrl ? (
                        <img ref={imageRef} src={previewUrl} alt="preview" onLoad={handlePreviewLoad} />
                      ) : (
                        <p className="muted">{t.previewPlaceholder}</p>
                      )}
                    </div>
                  </div>

                  <div className="result-box">
                    <strong>{t.resultTitle}</strong>
                    <p className="muted">{t.resultHint}</p>
                    <div className="result">{result || t.resultPlaceholder}</div>
                  </div>
                </div>
              </div>
            </div>
          </div>
        );
      }

      ReactDOM.createRoot(document.getElementById("root")).render(<App />);
    </script>
  </body>
</html>