diff --git a/csvc.html b/csvc.html
new file mode 100644
index 0000000..f86451b
--- /dev/null
+++ b/csvc.html
@@ -0,0 +1,295 @@
+<!doctype html>
+<html lang="en">
+<head>
+  <meta charset="utf-8" />
+  <meta name="viewport" content="width=device-width,initial-scale=1" />
+  <title>CSV File Search</title>
+  <style>
+    body { font-family: system-ui, Arial, sans-serif; max-width: 900px; margin: 24px auto; padding: 0 12px; }
+    .row { display: flex; gap: 10px; align-items: center; flex-wrap: wrap; }
+    input[type="text"] { flex: 1; min-width: 260px; padding: 10px; }
+    button { padding: 10px 14px; cursor: pointer; }
+    pre { background: #f6f7f9; padding: 12px; overflow: auto; border-radius: 8px; }
+    .meta { color: #555; margin-top: 6px; font-size: 0.95em; }
+  </style>
+</head>
+<body>
+  <h1>CSV File Search</h1>
+
+  <div class="row">
+    <label>
+      <div style="margin-bottom:6px;">Upload CSV files</div>
+      <input id="csvFiles" type="file" accept=".csv,text/csv" multiple />
+    </label>
+
+    <button id="loadBtn" disabled>Load CSVs</button>
+  </div>
+
+  <div class="meta" id="status">Status: waiting for files.</div>
+
+  <hr style="margin: 18px 0;" />
+
+  <div class="row">
+    <input id="queryBox" type="text" placeholder="Type a search term (e.g., john)..." disabled />
+    <button id="searchBtn" disabled>Search</button>
+    <button id="listColumnsBtn" disabled>List columns</button>
+  </div>
+
+  <p class="meta" id="hint">
+    Searches all “text-like” columns in all loaded CSVs using a substring match.
+  </p>
+
+  <h2 style="margin-top: 18px;">Results</h2>
+  <pre id="out">No results yet.</pre>
+
+  <script src="https://cdn.jsdelivr.net/npm/papaparse@5.4.1/papaparse.min.js"></script>
+  <script>
+    // datasets: array of { file, fileName, headers, textTargets:Set(headerName) }
+    let datasets = [];
+
+    const statusEl = document.getElementById('status');
+    const outEl = document.getElementById('out');
+    const csvFilesEl = document.getElementById('csvFiles');
+    const loadBtn = document.getElementById('loadBtn');
+    const queryBox = document.getElementById('queryBox');
+    const searchBtn = document.getElementById('searchBtn');
+    const listColumnsBtn = document.getElementById('listColumnsBtn');
+
+    function setStatus(msg) { statusEl.textContent = "Status: " + msg; }
+
+    function detectTextLikeColumnsFromSample(sampleRows, headers, sampleSize = 200) {
+      const limit = Math.min(sampleRows.length, sampleSize);
+
+      // Stats indexed by column index (so duplicates/empties don't break alignment)
+      const colStats = new Array(headers.length).fill(null).map(() => ({ nonEmpty: 0, numericLike: 0 }));
+
+      const numericRegex = /^-?\d+(\.\d+)?$/;
+
+      for (let r = 0; r < limit; r++) {
+        const row = sampleRows[r];
+
+        for (let c = 0; c < headers.length; c++) {
+          const h = headers[c];
+          if (!h) continue; // ignore empty header names for "textTargets" selection
+
+          const v = (row[c] ?? '').toString().trim();
+          if (!v) continue;
+
+          colStats[c].nonEmpty++;
+          if (numericRegex.test(v)) colStats[c].numericLike++;
+        }
+      }
+
+      // Keep a Set of header names for UI and matching selection (matches may include duplicate names)
+      const targets = new Set();
+
+      for (let c = 0; c < headers.length; c++) {
+        const h = headers[c];
+        if (!h) continue;
+
+        const s = colStats[c];
+        if (s.nonEmpty === 0) continue;
+
+        const numericRatio = s.numericLike / Math.max(1, s.nonEmpty);
+        // numericRatio < 0.8 => not mostly-numeric => treat as text-like
+        if (numericRatio < 0.8) targets.add(h);
+      }
+
+      return targets;
+    }
+
+    csvFilesEl.addEventListener('change', () => {
+      loadBtn.disabled = !csvFilesEl.files?.length;
+    });
+
+    loadBtn.addEventListener('click', async () => {
+      const files = [...(csvFilesEl.files || [])];
+      if (!files.length) return;
+
+      try {
+        outEl.textContent = "Loading CSVs...";
+        setStatus(`loading ${files.length} file(s)...`);
+        datasets = [];
+
+        // Load sequentially so you don't spawn multiple heavy parses at once
+        const SAMPLE_SIZE = 200;
+
+        for (let fi = 0; fi < files.length; fi++) {
+          const file = files[fi];
+          setStatus(`sampling ${fi + 1}/${files.length}: ${file.name}`);
+          outEl.textContent = `Sampling: ${file.name}`;
+
+          // Pass 1: stream to get headers + first N data rows
+          const sampleRows = [];
+          let headers = null;
+          let headerSet = false;
+
+          await new Promise((resolve, reject) => {
+            Papa.parse(file, {
+              header: false,
+              skipEmptyLines: true,
+              worker: true,
+              chunkSize: 1024 * 1024,
+              step: function(results, parser) {
+                if (!headerSet) {
+                  // First row is header row
+                  headers = results.data.map(h => (h ?? '').toString().trim());
+                  // IMPORTANT: do NOT filter empty header names; keep positions stable
+                  headerSet = true;
+                  return;
+                }
+
+                if (headers && sampleRows.length < SAMPLE_SIZE) {
+                  sampleRows.push(results.data);
+                }
+
+                if (sampleRows.length >= SAMPLE_SIZE) {
+                  parser.abort(); // only need a sample for column detection
+                }
+              },
+              complete: function() {
+                if (!headers || !headers.length) return resolve();
+                const textTargets = detectTextLikeColumnsFromSample(sampleRows, headers, SAMPLE_SIZE);
+                datasets.push({ file, fileName: file.name, headers, textTargets });
+                resolve();
+              },
+              error: function(err) {
+                reject(err);
+              }
+            });
+          });
+        }
+
+        queryBox.disabled = false;
+        searchBtn.disabled = false;
+        listColumnsBtn.disabled = false;
+
+        const totalCols = datasets.reduce((acc, d) => acc + d.textTargets.size, 0);
+        setStatus("loaded.");
+        outEl.textContent =
+          `Loaded ${datasets.length} CSV(s). Total text-like columns (sum of unique names): ${totalCols}.`;
+      } catch (e) {
+        console.error(e);
+        setStatus("failed.");
+        outEl.textContent = "Error:\n" + e.message;
+      }
+    });
+
+    listColumnsBtn.addEventListener('click', () => {
+      if (!datasets.length) return;
+
+      const lines = [];
+      for (const d of datasets) {
+        const cols = [...d.textTargets];
+        if (!cols.length) continue;
+        lines.push(`${d.fileName} (${cols.length}):\n- ${cols.join("\n- ")}`);
+      }
+
+      if (!lines.length) {
+        outEl.textContent = "No text-like columns detected in any loaded CSV.";
+        return;
+      }
+
+      outEl.textContent = "Text-like columns:\n\n" + lines.join("\n\n");
+    });
+
+    searchBtn.addEventListener('click', async () => {
+      const term = (queryBox.value || '').trim();
+      if (!term) {
+        outEl.textContent = "Type a search term first.";
+        return;
+      }
+
+      if (!datasets.length) return;
+
+      const termLower = term.toLowerCase();
+      const LIMIT = 1000;
+      const results = [];
+
+      try {
+        setStatus(`searching for "${term}"...`);
+        outEl.textContent = `Searching: "${term}"`;
+
+        // Search each dataset by streaming the entire file (second pass per file)
+        for (let di = 0; di < datasets.length; di++) {
+          const d = datasets[di];
+          if (!d.textTargets.size) continue;
+
+          setStatus(`searching ${di + 1}/${datasets.length}: ${d.fileName}`);
+
+          // Build candidate indices by header position (supports duplicate header names)
+          const targetIndices = [];
+          for (let i = 0; i < d.headers.length; i++) {
+            const h = d.headers[i];
+            if (h && d.textTargets.has(h)) targetIndices.push(i);
+          }
+
+          let dataRowIndex = 0;
+          let sawHeader = false;
+
+          await new Promise((resolve, reject) => {
+            Papa.parse(d.file, {
+              header: false,
+              skipEmptyLines: true,
+              worker: true,
+              chunkSize: 1024 * 1024,
+              step: function(results2, parser) {
+                if (!sawHeader) {
+                  sawHeader = true;
+                  return; // skip header row
+                }
+
+                // This is a data row (since we skipped the header row above)
+                dataRowIndex++;
+
+                // IMPORTANT: collect ALL matches across candidate columns for this row
+                for (const ci of targetIndices) {
+                  const v = (results2.data?.[ci] ?? '').toString().trim();
+                  if (v && v.toLowerCase().includes(termLower)) {
+                    results.push({
+                      file_name: d.fileName,
+                      row_index: dataRowIndex + 1, // header row is row 1; data starts at 2
+                      column_name: d.headers[ci], // may be "" if header was empty
+                      match_value: v
+                    });
+
+                    if (results.length >= LIMIT) {
+                      parser.abort();
+                      break;
+                    }
+                  }
+                }
+              },
+              complete: function() { resolve(); },
+              error: function(err) { reject(err); }
+            });
+          });
+
+          if (results.length >= LIMIT) break;
+
+          // Update UI occasionally between files
+          outEl.textContent =
+            `Searching: "${term}"\n\nCollected ${results.length} match(es) so far (up to ${LIMIT}).`;
+        }
+
+        if (!results.length) {
+          outEl.textContent = `No matches found for "${term}".`;
+        } else {
+          const lines = results.map(x =>
+            `${x.file_name} (row ${x.row_index}): ${x.column_name} = ${x.match_value}`
+          );
+          outEl.textContent =
+            `Found ${results.length} match(es) for "${term}" (showing up to ${LIMIT}):\n\n` + lines.join("\n");
+        }
+
+        setStatus("done.");
+      } catch (e) {
+        console.error(e);
+        setStatus("failed.");
+        outEl.textContent = "Error:\n" + e.message;
+      }
+    });
+  </script>
+</body>
+</html>
+
diff --git a/csvd.html b/csvd.html
new file mode 100644
index 0000000..94ffa39
--- /dev/null
+++ b/csvd.html
@@ -0,0 +1,311 @@
+<!doctype html>
+<html lang="en">
+<head>
+  <meta charset="utf-8" />
+  <meta name="viewport" content="width=device-width,initial-scale=1" />
+  <title>CSV File Search</title>
+  <style>
+    body { font-family: system-ui, Arial, sans-serif; max-width: 900px; margin: 24px auto; padding: 0 12px; }
+    .row { display: flex; gap: 10px; align-items: center; flex-wrap: wrap; }
+    input[type="text"] { flex: 1; min-width: 260px; padding: 10px; }
+    button { padding: 10px 14px; cursor: pointer; }
+    pre { background: #f6f7f9; padding: 12px; overflow: auto; border-radius: 8px; }
+    .meta { color: #555; margin-top: 6px; font-size: 0.95em; }
+  </style>
+</head>
+<body>
+  <h1>CSV File Search</h1>
+
+  <div class="row">
+    <label>
+      <div style="margin-bottom:6px;">Upload CSV files</div>
+      <input id="csvFiles" type="file" accept=".csv,text/csv" multiple />
+    </label>
+
+    <button id="loadBtn" disabled>Load CSVs</button>
+  </div>
+
+  <div class="meta" id="status">Status: waiting for files.</div>
+
+  <hr style="margin: 18px 0;" />
+
+  <div class="row">
+    <input id="queryBox" type="text" placeholder="Type a search term (e.g., john)..." disabled />
+    <button id="searchBtn" disabled>Search</button>
+    <button id="listColumnsBtn" disabled>List columns</button>
+  </div>
+
+  <p class="meta" id="hint">
+    Searches all “text-like” columns in all loaded CSVs using a substring match.
+  </p>
+
+  <h2 style="margin-top: 18px;">Results</h2>
+  <pre id="out">No results yet.</pre>
+
+  <script src="https://cdn.jsdelivr.net/npm/papaparse@5.4.1/papaparse.min.js"></script>
+  <script>
+    // datasets: array of { file, fileName, headers, textTargets:Set(headerName) }
+    let datasets = [];
+
+    const statusEl = document.getElementById('status');
+    const outEl = document.getElementById('out');
+    const csvFilesEl = document.getElementById('csvFiles');
+    const loadBtn = document.getElementById('loadBtn');
+    const queryBox = document.getElementById('queryBox');
+    const searchBtn = document.getElementById('searchBtn');
+    const listColumnsBtn = document.getElementById('listColumnsBtn');
+
+    function setStatus(msg) { statusEl.textContent = "Status: " + msg; }
+
+    function detectTextLikeColumnsFromSample(sampleRows, headers, sampleSize = 200) {
+      const limit = Math.min(sampleRows.length, sampleSize);
+
+      // Stats indexed by column index (so duplicates/empties don't break alignment)
+      const colStats = new Array(headers.length).fill(null).map(() => ({ nonEmpty: 0, numericLike: 0 }));
+
+      const numericRegex = /^-?\d+(\.\d+)?$/;
+
+      for (let r = 0; r < limit; r++) {
+        const row = sampleRows[r];
+
+        for (let c = 0; c < headers.length; c++) {
+          const h = headers[c];
+          if (!h) continue; // ignore empty header names for "textTargets" selection
+
+          const v = (row[c] ?? '').toString().trim();
+          if (!v) continue;
+
+          colStats[c].nonEmpty++;
+          if (numericRegex.test(v)) colStats[c].numericLike++;
+        }
+      }
+
+      // Keep a Set of header names for UI and matching selection (matches may include duplicate names)
+      const targets = new Set();
+
+      for (let c = 0; c < headers.length; c++) {
+        const h = headers[c];
+        if (!h) continue;
+
+        const s = colStats[c];
+        if (s.nonEmpty === 0) continue;
+
+        const numericRatio = s.numericLike / Math.max(1, s.nonEmpty);
+        // numericRatio < 0.8 => not mostly-numeric => treat as text-like
+        if (numericRatio < 0.8) targets.add(h);
+      }
+
+      return targets;
+    }
+
+    csvFilesEl.addEventListener('change', () => {
+      loadBtn.disabled = !csvFilesEl.files?.length;
+    });
+
+    loadBtn.addEventListener('click', async () => {
+      const files = [...(csvFilesEl.files || [])];
+      if (!files.length) return;
+
+      try {
+        outEl.textContent = "Loading CSVs...";
+        setStatus(`loading ${files.length} file(s)...`);
+        datasets = [];
+
+        // Load sequentially so you don't spawn multiple heavy parses at once
+        const SAMPLE_SIZE = 200;
+
+        for (let fi = 0; fi < files.length; fi++) {
+          const file = files[fi];
+          setStatus(`sampling ${fi + 1}/${files.length}: ${file.name}`);
+          outEl.textContent = `Sampling: ${file.name}`;
+
+          // Pass 1: stream to get headers + first N data rows
+          const sampleRows = [];
+          let headers = null;
+          let headerSet = false;
+
+          await new Promise((resolve, reject) => {
+            Papa.parse(file, {
+              header: false,
+              skipEmptyLines: true,
+              worker: true,
+              chunkSize: 1024 * 1024,
+              step: function(results, parser) {
+                if (!headerSet) {
+                  // First row is header row
+                  headers = results.data.map(h => (h ?? '').toString().trim());
+                  // IMPORTANT: do NOT filter empty header names; keep positions stable
+                  headerSet = true;
+                  return;
+                }
+
+                if (headers && sampleRows.length < SAMPLE_SIZE) {
+                  sampleRows.push(results.data);
+                }
+
+                if (sampleRows.length >= SAMPLE_SIZE) {
+                  parser.abort(); // only need a sample for column detection
+                }
+              },
+              complete: function() {
+                if (!headers || !headers.length) return resolve();
+                const textTargets = detectTextLikeColumnsFromSample(sampleRows, headers, SAMPLE_SIZE);
+                datasets.push({ file, fileName: file.name, headers, textTargets });
+                resolve();
+              },
+              error: function(err) {
+                reject(err);
+              }
+            });
+          });
+        }
+
+        queryBox.disabled = false;
+        searchBtn.disabled = false;
+        listColumnsBtn.disabled = false;
+
+        const totalCols = datasets.reduce((acc, d) => acc + d.textTargets.size, 0);
+        setStatus("loaded.");
+        outEl.textContent =
+          `Loaded ${datasets.length} CSV(s). Total text-like columns (sum of unique names): ${totalCols}.`;
+      } catch (e) {
+        console.error(e);
+        setStatus("failed.");
+        outEl.textContent = "Error:\n" + e.message;
+      }
+    });
+
+    listColumnsBtn.addEventListener('click', () => {
+      if (!datasets.length) return;
+
+      const lines = [];
+      for (const d of datasets) {
+        const cols = [...d.textTargets];
+        if (!cols.length) continue;
+        lines.push(`${d.fileName} (${cols.length}):\n- ${cols.join("\n- ")}`);
+      }
+
+      if (!lines.length) {
+        outEl.textContent = "No text-like columns detected in any loaded CSV.";
+        return;
+      }
+
+      outEl.textContent = "Text-like columns:\n\n" + lines.join("\n\n");
+    });
+
+    searchBtn.addEventListener('click', async () => {
+      const term = (queryBox.value || '').trim();
+      if (!term) {
+        outEl.textContent = "Type a search term first.";
+        return;
+      }
+
+      if (!datasets.length) return;
+
+      const termLower = term.toLowerCase();
+      const LIMIT = 1000;
+      const results = [];
+
+      // Build a quick lookup for headers by file name
+      const headersByFile = new Map(datasets.map(d => [d.fileName, d.headers]));
+
+      try {
+        setStatus(`searching for "${term}"...`);
+        outEl.textContent = `Searching: "${term}"`;
+
+        // Search each dataset by streaming the entire file (second pass per file)
+        for (let di = 0; di < datasets.length; di++) {
+          const d = datasets[di];
+          if (!d.textTargets.size) continue;
+
+          setStatus(`searching ${di + 1}/${datasets.length}: ${d.fileName}`);
+
+          // Build candidate indices by header position (supports duplicate header names)
+          const targetIndices = [];
+          for (let i = 0; i < d.headers.length; i++) {
+            const h = d.headers[i];
+            if (h && d.textTargets.has(h)) targetIndices.push(i);
+          }
+
+          let dataRowIndex = 0;
+          let sawHeader = false;
+
+          await new Promise((resolve, reject) => {
+            Papa.parse(d.file, {
+              header: false,
+              skipEmptyLines: true,
+              worker: true,
+              chunkSize: 1024 * 1024,
+              step: function(results2, parser) {
+                if (!sawHeader) {
+                  sawHeader = true;
+                  return; // skip header row
+                }
+
+                // This is a data row (since we skipped the header row above)
+                dataRowIndex++;
+
+                // If any target cell in this row matches, store the whole row once
+                let rowMatched = false;
+
+                for (const ci of targetIndices) {
+                  const v = (results2.data?.[ci] ?? '').toString().trim();
+                  if (v && v.toLowerCase().includes(termLower)) {
+                    rowMatched = true;
+                    break;
+                  }
+                }
+
+                if (rowMatched) {
+                  results.push({
+                    file_name: d.fileName,
+                    row_index: dataRowIndex + 1, // header row is row 1; data starts at 2
+                    row: results2.data
+                  });
+
+                  if (results.length >= LIMIT) {
+                    parser.abort();
+                  }
+                }
+              },
+              complete: function() { resolve(); },
+              error: function(err) { reject(err); }
+            });
+          });
+
+          if (results.length >= LIMIT) break;
+
+          // Update UI occasionally between files
+          outEl.textContent =
+            `Searching: "${term}"\n\nCollected ${results.length} matched row(s) so far (up to ${LIMIT}).`;
+        }
+
+        if (!results.length) {
+          outEl.textContent = `No matches found for "${term}".`;
+        } else {
+          const lines = results.map(x => {
+            const headers = headersByFile.get(x.file_name) || [];
+            const pairs = x.row.map((val, i) => {
+              const key = headers[i] ?? `col_${i + 1}`;
+              const safeVal = (val ?? '').toString();
+              return `${key}: ${safeVal}`;
+            });
+            return `${x.file_name} (row ${x.row_index}):\n  ${pairs.join("\n  ")}`;
+          });
+
+          outEl.textContent =
+            `Found ${results.length} matched row(s) for "${term}" (showing up to ${LIMIT}):\n\n` +
+            lines.join("\n\n");
+        }
+
+        setStatus("done.");
+      } catch (e) {
+        console.error(e);
+        setStatus("failed.");
+        outEl.textContent = "Error:\n" + e.message;
+      }
+    });
+  </script>
+</body>
+</html>
+
diff --git a/csvp.html b/csvp.html
new file mode 100644
index 0000000..89c33c3
--- /dev/null
+++ b/csvp.html
@@ -0,0 +1,368 @@
+<!doctype html>
+<html lang="en">
+<head>
+  <meta charset="utf-8" />
+  <meta name="viewport" content="width=device-width,initial-scale=1" />
+  <title>CSV File Search</title>
+  <style>
+    body { font-family: system-ui, Arial, sans-serif; max-width: 900px; margin: 24px auto; padding: 0 12px; }
+    .row { display: flex; gap: 10px; align-items: center; flex-wrap: wrap; }
+    input[type="text"] { flex: 1; min-width: 260px; padding: 10px; }
+    button { padding: 10px 14px; cursor: pointer; }
+    pre { background: #f6f7f9; padding: 12px; overflow: auto; border-radius: 8px; }
+    .meta { color: #555; margin-top: 6px; font-size: 0.95em; }
+  </style>
+</head>
+<body>
+  <h1>CSV File Search</h1>
+
+  <div class="row">
+    <label>
+      <div style="margin-bottom:6px;">Upload CSV files</div>
+      <input id="csvFiles" type="file" accept=".csv,text/csv" multiple />
+    </label>
+
+    <button id="loadBtn" disabled>Load CSVs</button>
+  </div>
+
+  <div class="meta" id="status">Status: waiting for files.</div>
+
+  <hr style="margin: 18px 0;" />
+
+  <div class="row">
+    <input id="queryBox" type="text" placeholder="Type a search term (e.g., john)..." disabled />
+    <button id="searchBtn" disabled>Search</button>
+    <button id="listColumnsBtn" disabled>List columns</button>
+  </div>
+
+  <p class="meta" id="hint">
+    Searches all “text-like” columns in all loaded CSVs using a substring match.
+  </p>
+
+  <h2 style="margin-top: 18px;">Results</h2>
+  <pre id="out">No results yet.</pre>
+
+  <script src="https://cdn.jsdelivr.net/npm/papaparse@5.4.1/papaparse.min.js"></script>
+  <script>
+    // datasets: array of { file, fileName, headers, textTargets:Set(headerName) }
+    let datasets = [];
+
+    const statusEl = document.getElementById('status');
+    const outEl = document.getElementById('out');
+    const csvFilesEl = document.getElementById('csvFiles');
+    const loadBtn = document.getElementById('loadBtn');
+    const queryBox = document.getElementById('queryBox');
+    const searchBtn = document.getElementById('searchBtn');
+    const listColumnsBtn = document.getElementById('listColumnsBtn');
+
+    function setStatus(msg) { statusEl.textContent = "Status: " + msg; }
+
+    function detectTextLikeColumnsFromSample(sampleRows, headers, sampleSize = 200) {
+      const limit = Math.min(sampleRows.length, sampleSize);
+
+      // Stats indexed by column index (so duplicates/empties don't break alignment)
+      const colStats = new Array(headers.length).fill(null).map(() => ({ nonEmpty: 0, numericLike: 0 }));
+
+      const numericRegex = /^-?\d+(\.\d+)?$/;
+
+      for (let r = 0; r < limit; r++) {
+        const row = sampleRows[r];
+
+        for (let c = 0; c < headers.length; c++) {
+          const h = headers[c];
+          if (!h) continue; // ignore empty header names for "textTargets" selection
+
+          const v = (row[c] ?? '').toString().trim();
+          if (!v) continue;
+
+          colStats[c].nonEmpty++;
+          if (numericRegex.test(v)) colStats[c].numericLike++;
+        }
+      }
+
+      // Keep a Set of header names for UI and matching selection (matches may include duplicate names)
+      const targets = new Set();
+
+      for (let c = 0; c < headers.length; c++) {
+        const h = headers[c];
+        if (!h) continue;
+
+        const s = colStats[c];
+        if (s.nonEmpty === 0) continue;
+
+        const numericRatio = s.numericLike / Math.max(1, s.nonEmpty);
+        // numericRatio < 0.8 => not mostly-numeric => treat as text-like
+        if (numericRatio < 0.8) targets.add(h);
+      }
+
+      return targets;
+    }
+
+    csvFilesEl.addEventListener('change', () => {
+      loadBtn.disabled = !csvFilesEl.files?.length;
+    });
+
+    loadBtn.addEventListener('click', async () => {
+      const files = [...(csvFilesEl.files || [])];
+      if (!files.length) return;
+
+      try {
+        outEl.textContent = "Loading CSVs...";
+        setStatus(`loading ${files.length} file(s)...`);
+        datasets = [];
+
+        // Load sequentially so you don't spawn multiple heavy parses at once
+        const SAMPLE_SIZE = 200;
+
+        for (let fi = 0; fi < files.length; fi++) {
+          const file = files[fi];
+          setStatus(`sampling ${fi + 1}/${files.length}: ${file.name}`);
+          outEl.textContent = `Sampling: ${file.name}`;
+
+          // Pass 1: stream to get headers + first N data rows
+          const sampleRows = [];
+          let headers = null;
+          let headerSet = false;
+
+          await new Promise((resolve, reject) => {
+            Papa.parse(file, {
+              header: false,
+              skipEmptyLines: true,
+              worker: true,
+              chunkSize: 1024 * 1024,
+              step: function(results, parser) {
+                if (!headerSet) {
+                  // First row is header row
+                  headers = results.data.map(h => (h ?? '').toString().trim());
+                  // IMPORTANT: do NOT filter empty header names; keep positions stable
+                  headerSet = true;
+                  return;
+                }
+
+                if (headers && sampleRows.length < SAMPLE_SIZE) {
+                  sampleRows.push(results.data);
+                }
+
+                if (sampleRows.length >= SAMPLE_SIZE) {
+                  parser.abort(); // only need a sample for column detection
+                }
+              },
+              complete: function() {
+                if (!headers || !headers.length) return resolve();
+                const textTargets = detectTextLikeColumnsFromSample(sampleRows, headers, SAMPLE_SIZE);
+                datasets.push({ file, fileName: file.name, headers, textTargets });
+                resolve();
+              },
+              error: function(err) {
+                reject(err);
+              }
+            });
+          });
+        }
+
+        queryBox.disabled = false;
+        searchBtn.disabled = false;
+        listColumnsBtn.disabled = false;
+
+        const totalCols = datasets.reduce((acc, d) => acc + d.textTargets.size, 0);
+        setStatus("loaded.");
+        outEl.textContent =
+          `Loaded ${datasets.length} CSV(s). Total text-like columns (sum of unique names): ${totalCols}.`;
+      } catch (e) {
+        console.error(e);
+        setStatus("failed.");
+        outEl.textContent = "Error:\n" + e.message;
+      }
+    });
+
+    listColumnsBtn.addEventListener('click', () => {
+      if (!datasets.length) return;
+
+      const lines = [];
+      for (const d of datasets) {
+        const cols = [...d.textTargets];
+        if (!cols.length) continue;
+        lines.push(`${d.fileName} (${cols.length}):\n- ${cols.join("\n- ")}`);
+      }
+
+      if (!lines.length) {
+        outEl.textContent = "No text-like columns detected in any loaded CSVs.";
+        return;
+      }
+
+      outEl.textContent = "Text-like columns:\n\n" + lines.join("\n\n");
+    });
+
+    searchBtn.addEventListener('click', async () => {
+      const term = (queryBox.value || '').trim();
+      if (!term) {
+        outEl.textContent = "Type a search term first.";
+        return;
+      }
+
+      if (!datasets.length) return;
+
+      const termLower = term.toLowerCase();
+      const LIMIT = 1000;
+      const results = [];
+
+      // Build a quick lookup for headers by file name
+      const headersByFile = new Map(datasets.map(d => [d.fileName, d.headers]));
+
+      // Helper: count data rows (excluding header) for progress.
+      // Uses parsing steps to count rows precisely.
+      async function countDataRows(file) {
+        return await new Promise((resolve, reject) => {
+          let sawHeader = false;
+          let dataRows = 0;
+
+          Papa.parse(file, {
+            header: false,
+            skipEmptyLines: true,
+            worker: true,
+            chunkSize: 1024 * 1024,
+            step: function() {
+              if (!sawHeader) {
+                sawHeader = true;
+              } else {
+                dataRows++;
+              }
+            },
+            complete: function() {
+              resolve(dataRows);
+            },
+            error: function(err) {
+              reject(err);
+            }
+          });
+        });
+      }
+
+      try {
+        setStatus(`searching for "${term}"...`);
+        outEl.textContent = `Searching: "${term}"\n\nProgress: 0%`;
+
+        // Precompute total rows across all loaded CSVs (sum of data rows) for global percentage
+        const totalRowsPerFile = [];
+        let grandTotalRows = 0;
+
+        for (let di = 0; di < datasets.length; di++) {
+          const d = datasets[di];
+          if (!d.textTargets.size) {
+            totalRowsPerFile.push(0);
+            continue;
+          }
+          setStatus(`counting rows ${di + 1}/${datasets.length}: ${d.fileName}`);
+          outEl.textContent = `Searching: "${term}"\n\nProgress: 0% (counting rows in ${di + 1}/${datasets.length})`;
+          const cnt = await countDataRows(d.file);
+          totalRowsPerFile.push(cnt);
+          grandTotalRows += cnt;
+        }
+
+        // Avoid divide-by-zero
+        grandTotalRows = Math.max(1, grandTotalRows);
+
+        let processedRowsGlobal = 0;
+
+        for (let di = 0; di < datasets.length; di++) {
+          const d = datasets[di];
+          if (!d.textTargets.size) continue;
+
+          const fileTotalRows = totalRowsPerFile[di] || 0;
+
+          setStatus(`searching ${di + 1}/${datasets.length}: ${d.fileName}`);
+          outEl.textContent =
+            `Searching: "${term}"\n\nProgress: ${Math.floor((processedRowsGlobal / grandTotalRows) * 100)}% (processed ${processedRowsGlobal}/${grandTotalRows})`;
+
+          // Build candidate indices by header position (supports duplicate header names)
+          const targetIndices = [];
+          for (let i = 0; i < d.headers.length; i++) {
+            const h = d.headers[i];
+            if (h && d.textTargets.has(h)) targetIndices.push(i);
+          }
+
+          let dataRowIndex = 0;
+          let sawHeader = false;
+
+          await new Promise((resolve, reject) => {
+            Papa.parse(d.file, {
+              header: false,
+              skipEmptyLines: true,
+              worker: true,
+              chunkSize: 1024 * 1024,
+              step: function(results2, parser) {
+                if (!sawHeader) {
+                  sawHeader = true;
+                  return; // skip header row
+                }
+
+                // This is a data row (since we skipped the header row above)
+                dataRowIndex++;
+
+                // Update progress (global)
+                processedRowsGlobal++;
+                const pct = Math.floor((processedRowsGlobal / grandTotalRows) * 100);
+
+                outEl.textContent =
+                  `Searching: "${term}"\n\nProgress: ${pct}% (processed ${processedRowsGlobal}/${grandTotalRows})`;
+
+                // If any target cell in this row matches, store the whole row once
+                let rowMatched = false;
+
+                for (const ci of targetIndices) {
+                  const v = (results2.data?.[ci] ?? '').toString().trim();
+                  if (v && v.toLowerCase().includes(termLower)) {
+                    rowMatched = true;
+                    break;
+                  }
+                }
+
+                if (rowMatched) {
+                  results.push({
+                    file_name: d.fileName,
+                    row_index: dataRowIndex + 1, // header row is row 1; data starts at 2
+                    row: results2.data
+                  });
+
+                  if (results.length >= LIMIT) {
+                    parser.abort();
+                  }
+                }
+              },
+              complete: function() { resolve(); },
+              error: function(err) { reject(err); }
+            });
+          });
+
+          if (results.length >= LIMIT) break;
+        }
+
+        if (!results.length) {
+          outEl.textContent = `No matches found for "${term}".`;
+        } else {
+          const lines = results.map(x => {
+            const headers = headersByFile.get(x.file_name) || [];
+            const pairs = x.row.map((val, i) => {
+              const key = headers[i] ?? `col_${i + 1}`;
+              const safeVal = (val ?? '').toString();
+              return `${key}: ${safeVal}`;
+            });
+            return `${x.file_name} (row ${x.row_index}):\n  ${pairs.join("\n  ")}`;
+          });
+
+          outEl.textContent =
+            `Found ${results.length} matched row(s) for "${term}" (showing up to ${LIMIT}):\n\n` +
+            lines.join("\n\n");
+        }
+
+        setStatus("done.");
+      } catch (e) {
+        console.error(e);
+        setStatus("failed.");
+        outEl.textContent = "Error:\n" + e.message;
+      }
+    });
+  </script>
+</body>
+</html>
+
