code/finetune/convert_webchain.py

"""WebChain (webagentlab/webchain, CC-BY-4.0; human trajectories on real sites) -> laya cases.

    python finetune/convert_webchain.py fetch  <n_traces> [workers=16]   # AX-tree snapshots -> compact .json.gz (direct, no proxy)
    python finetune/convert_webchain.py convert <out.jsonl>               # compact snapshots -> cases (same format as m2w_cases)

Per step: goal = the trace's query; page = the AX snapshot taken at that step (interactive nodes + text-bearing nodes,
page order); gold = the node the human acted on, found by its text (`value`) + html tag, ties broken by the selector's
id/class tokens -- an ambiguous or missing gold drops the step.  Candidates mirror Mind2Web's: the interactive nodes plus
some non-semantic text nodes (the gold is often a clickable <span>/<div>), capped at 60 in page order.
Actions kept: click / double_click -> CLICK, type -> TYPE_TEXT (or SELECT on a native <select>), press_enter -> PRESS_ENTER.
hover / drag / copy / paste / right_click steps are dropped (no laya operation), but still count in the history.
WebChain records no scroll and no final DONE, so it contributes no DONE labels.
"""
import gzip, hashlib, json, os, random, re, sys, time
from concurrent.futures import ThreadPoolExecutor

META = os.path.expanduser("~/data/webchain/data/seed_sft/metadata")
SNAP = os.path.expanduser("~/data/webchain/ax_compact")
INTERACTIVE = {"link", "button", "textbox", "searchbox", "combobox", "checkbox", "radio", "switch", "tab", "menuitem",
               "menuitemradio", "menuitemcheckbox", "option", "listbox", "slider", "spinbutton", "treeitem", "gridcell"}
TEXTY = {"generic", "listitem", "cell", "heading", "img", "paragraph", "StaticText", "label", "row", "columnheader"}
KEEP_ATTR = ("data-imean-axt-id", "html_tag", "href", "type", "placeholder", "aria-label", "title", "alt", "value", "id", "class", "aria-checked",
             "aria-selected", "aria-expanded", "checked", "selected", "role")


def compact(ax):
    """Flatten an AX snapshot to [{role,name,a,vis,d}] in document order (iterative: real pages nest deeper than
    Python's recursion limit)."""
    out, stack = [], [(ax, 0)]
    while stack:
        n, depth = stack.pop()
        a = n.get("attributes") or {}
        vis = (n.get("offsetWidth") or 0) > 0 and (n.get("offsetHeight") or 0) > 0
        out.append({"role": n.get("role") or "", "name": (n.get("name") or "")[:200], "d": depth, "vis": vis,
                    "a": {k: str(a[k])[:120] for k in KEEP_ATTR if k in a}})
        stack.extend((c, depth + 1) for c in reversed(n.get("children") or []))
    return out


def gold_axt_from_dom(html, selector):
    """The data-imean-axt-id of the element the CSS selector picks (or its nearest tagged ancestor / first tagged child)."""
    import lxml.html
    try:
        root = lxml.html.fromstring(html)
        els = root.cssselect(selector)
    except Exception:
        return None
    if len(els) != 1:
        return None
    el = els[0]
    for e in [el, *el.iterancestors()]:
        if e.get("data-imean-axt-id"):
            return e.get("data-imean-axt-id")
    for e in el.iterdescendants():
        if e.get("data-imean-axt-id"):
            return e.get("data-imean-axt-id")
    return None


def step_file(uid, idx):
    return os.path.join(SNAP, hashlib.md5(f"{uid}:{idx}".encode()).hexdigest() + ".json.gz")


def fetch(n_traces, workers):
    """Per step: the AX snapshot (compact) + the gold node's axt id.  The gold is found by text in the AX tree; only when
    that fails is the (bigger) DOM snapshot fetched and the step's CSS selector resolved to an axt id."""
    import pandas as pd, httpx
    os.makedirs(SNAP, exist_ok=True)
    tr = pd.read_parquet(f"{META}/traces.parquet", columns=["uid"])
    uids = sorted(tr["uid"].tolist()); random.Random(0).shuffle(uids)
    keep = set(uids[:n_traces])
    ac = pd.read_parquet(f"{META}/actions.parquet", columns=["trace_uid", "source_step_index", "action_type", "value", "title", "attributes",
                                                             "selector", "ax_tree_url", "html_dom_url"])
    rows = [r for r in ac[ac.trace_uid.isin(keep)].to_dict("records") if r["action_type"] in ("click", "double_click", "type", "select", "press_enter")
            and isinstance(r["ax_tree_url"], str) and r["ax_tree_url"].startswith("http")]
    client = httpx.Client(timeout=60, trust_env=False)      # direct: never through the proxy
    stats = {"ok": 0, "skip": 0, "err": 0, "dom": 0, "gold_text": 0, "gold_dom": 0, "no_gold": 0, "mb": 0.0}

    def get(u):
        for attempt in range(3):
            try:
                r = client.get(u); r.raise_for_status(); stats["mb"] += len(r.content) / 1e6; return r
            except Exception:
                time.sleep(2 * (attempt + 1))
        return None

    def one(row):
        dst = step_file(row["trace_uid"], row["source_step_index"])
        if os.path.exists(dst):
            stats["skip"] += 1; return
        try:
            r = get(row["ax_tree_url"])
            if r is None:
                stats["err"] += 1; return
            nodes = compact(r.json()); gold_axt = None
            if row["action_type"] != "press_enter":
                # DOM selector first (exact); text matching only when the selector does not resolve (it disagrees with
                # the selector on ~13% of steps, so it is the fallback, not the default)
                if isinstance(row["html_dom_url"], str) and row["html_dom_url"].startswith("http") and row["selector"]:
                    d = get(row["html_dom_url"]); stats["dom"] += 1
                    gold_axt = gold_axt_from_dom(d.content, row["selector"]) if d is not None else None
                    if gold_axt and not any(n["a"].get("data-imean-axt-id") == gold_axt for n in nodes):
                        gold_axt = None
                    if gold_axt:
                        stats["gold_dom"] += 1
                if not gold_axt:
                    g = find_gold(nodes, row)
                    if g is not None:
                        gold_axt = nodes[g]["a"].get("data-imean-axt-id"); stats["gold_text"] += 1
                    else:
                        stats["no_gold"] += 1
            with gzip.open(dst + ".tmp", "wt") as f:
                json.dump({"nodes": nodes, "gold_axt": gold_axt}, f, ensure_ascii=False, separators=(",", ":"))
            os.replace(dst + ".tmp", dst); stats["ok"] += 1
        except Exception as e:
            stats["err"] += 1
            if os.path.exists(dst + ".tmp"):
                os.remove(dst + ".tmp")

    t0 = time.time()
    with ThreadPoolExecutor(workers) as ex:
        for i, _ in enumerate(ex.map(one, rows)):
            if i % 1000 == 0:
                print(f"  {i}/{len(rows)} {stats} {stats['mb'] / max(1, time.time() - t0):.1f} MB/s", flush=True)
    print("fetch done", len(keep), "traces,", len(rows), "steps", stats)


def _norm(s):
    return " ".join(str(s or "").split()).lower()


def label_of(n):
    a = n["a"]
    return n["name"] or a.get("aria-label") or a.get("placeholder") or a.get("title") or a.get("alt") or a.get("value") or ""


def find_gold(nodes, row):
    """Index of the acted-on node, or None if missing / ambiguous."""
    val = _norm(row["value"]); ttl = _norm(re.sub(r"^(点击|输入|选择|click|type|select)\s*", "", str(row["title"] or ""), flags=re.I))
    try:
        tag = (json.loads(row["attributes"] or "{}").get("data", {}).get("node", {}).get("name") or "").lower()
    except Exception:
        tag = ""
    sel = str(row["selector"] or "")
    toks = set(re.findall(r"[#.]([A-Za-z0-9_-]+)", sel))
    typing = row["action_type"] in ("type", "select")
    cand = []
    for i, n in enumerate(nodes):
        if typing:
            if n["a"].get("html_tag") not in ("input", "textarea", "select") and n["role"] not in ("textbox", "searchbox", "combobox"):
                continue
            lab = _norm(label_of(n))
            score = 3 * (tag and n["a"].get("html_tag") == tag) + 2 * bool(toks & ({n["a"].get("id", "")} | set(n["a"].get("class", "").split())))
            score += 2 * bool(lab and (lab in ttl or ttl in lab))
            cand.append((score, i))
        else:
            lab = _norm(label_of(n))
            if not lab or not (lab == val or (ttl and lab == ttl)):
                continue
            score = 3 * (tag and n["a"].get("html_tag") == tag) + 2 * bool(toks & ({n["a"].get("id", "")} | set(n["a"].get("class", "").split())))
            score += n["role"] in INTERACTIVE
            cand.append((score, i))
    if not cand:
        return None
    cand.sort(reverse=True)
    if len(cand) > 1 and cand[0][0] == cand[1][0]:
        return None
    return cand[0][1]


def page_from(nodes, gold, rng, url, title):
    """(page_obj, gold action id, gold kind, gold label, options) in m2w_cases format."""
    inter = [i for i, n in enumerate(nodes) if n["vis"] and n["role"] in INTERACTIVE and label_of(n)]
    texty = [i for i, n in enumerate(nodes) if n["vis"] and n["role"] in TEXTY and 2 <= len(label_of(n)) <= 80 and i != gold]
    pick = set(inter[:45]) | set(rng.sample(texty, min(len(texty), 15))) | {gold}
    pick = sorted(pick)[:60] if gold in sorted(pick)[:60] else sorted(set(sorted(pick)[:59]) | {gold})
    acts = []
    for j, i in enumerate(pick):
        n = nodes[i]; tag = n["a"].get("html_tag"); lab = label_of(n)[:120]
        editable = n["role"] in ("textbox", "searchbox") or (n["role"] == "combobox" and tag in ("input", "textarea"))
        base = {"node": j + 1, "label": lab, "role": n["role"] or tag or "generic"}
        for k in ("aria-checked", "aria-selected", "aria-expanded"):
            if k in n["a"]:
                base[k[5:]] = n["a"][k]
        if editable:
            acts.append({**base, "id": f"fill:{j + 1}", "kind": "fill", "value": n["a"].get("value", ""), "current_value": n["a"].get("value", "")})
        acts.append({**base, "id": f"click:{j + 1}", "kind": "click"})
    gj = pick.index(gold) + 1
    text, seen = [], set()
    for n in nodes:
        t = n["name"].strip()
        if n["vis"] and t and n["role"] in TEXTY | {"link", "button"} and t not in seen and len(t) < 300:
            seen.add(t); text.append(t)
    return {"url": url, "title": title, "text": "\n".join(text)[:6000], "actions": acts}, gj


def convert(out_path):
    import pandas as pd
    tr = pd.read_parquet(f"{META}/traces.parquet", columns=["uid", "query", "primary_host", "intent_type", "web_type"])
    q = {r.uid: r for r in tr.itertuples(index=False)}
    ac = pd.read_parquet(f"{META}/actions.parquet", columns=["trace_uid", "source_step_index", "action_type", "input_text", "value", "title",
                                                             "attributes", "selector", "ax_tree_url", "href", "host_title"])
    ac = ac.sort_values(["trace_uid", "source_step_index"])
    stats = {"steps": 0, "no_snapshot": 0, "no_gold": 0, "skipped_op": 0, "cases": 0}
    ops = {"click": "CLICK", "double_click": "CLICK", "type": "TYPE_TEXT", "select": "TYPE_TEXT", "press_enter": "PRESS_ENTER"}
    with open(out_path, "w") as out:
        for uid, g in ac.groupby("trace_uid", sort=False):
            hist = []
            for row in g.to_dict("records"):
                stats["steps"] += 1
                at = row["action_type"]; txt = row["input_text"] if row["input_text"] not in (None, "", "no input text") else None
                snap = step_file(uid, row["source_step_index"])
                done_hist = {"action": (str(row["value"] or row["title"] or at))[:80], "kind": {"type": "fill", "select": "select"}.get(at, "click"),
                             "text": txt, "page_changed": True}
                if at not in ops:
                    stats["skipped_op"] += 1; hist.append(done_hist); continue
                if not os.path.exists(snap):
                    stats["no_snapshot"] += 1; hist.append(done_hist); continue
                blob = json.load(gzip.open(snap, "rt")); nodes = blob["nodes"]
                rng = random.Random(hash((uid, row["source_step_index"])) & 0xffffffff)
                url, title = str(row["href"] or ""), str(row["host_title"] or "")
                t = q[uid]
                base_case = {"page": -1, "url": url, "title": title, "goal": re.sub(r'^\s*(task\s*\d+\s*[:：.]|\d+\s*[.、)])\s*', "", str(t.query).strip(), flags=re.I).strip().strip('"').strip(), "history": list(hist)[-10:], "source": "webchain",
                             "task_id": uid, "website": str(t.primary_host), "intent": str(t.intent_type), "domain": str(t.web_type)}
                if at == "press_enter":
                    if not hist:
                        stats["no_gold"] += 1; hist.append(done_hist); continue
                    pg, _ = page_from(nodes, max(0, len(nodes) - 1), rng, url, title)
                    pg["actions"].append({"id": "press_enter", "kind": "key", "label": "Press Enter in the focused text field (submit it)", "key": "Enter"})
                    out.write(json.dumps({**base_case, "page_obj": pg, "gold_op": "PRESS_ENTER", "gold_id": "press_enter", "kind": "key", "label": ""}, ensure_ascii=False) + "\n")
                    stats["cases"] += 1; hist.append(done_hist); continue
                gold = next((i for i, n in enumerate(nodes) if blob["gold_axt"] and n["a"].get("data-imean-axt-id") == blob["gold_axt"]), None)
                if gold is None:
                    stats["no_gold"] += 1; hist.append(done_hist); continue
                pg, gj = page_from(nodes, gold, rng, url, title)
                gn = nodes[gold]
                if at in ("type", "select"):
                    if gn["a"].get("html_tag") == "select":
                        op, gid, kind = "TYPE_TEXT", f"fill:{gj}", "fill"      # native select options are not in the AX snapshot
                    else:
                        op, gid, kind = "TYPE_TEXT", f"fill:{gj}", "fill"
                    if not any(a["id"] == gid for a in pg["actions"]):
                        # the typed-into node is not an editable role in the snapshot: offer it as a text field
                        a0 = next(a for a in pg["actions"] if a["node"] == gj)
                        pg["actions"].insert(pg["actions"].index(a0), {**{k: v for k, v in a0.items() if k not in ("id", "kind")}, "id": gid, "kind": "fill", "value": "", "current_value": ""})
                else:
                    op, gid, kind = "CLICK", f"click:{gj}", "click"
                lab = next(a["label"] for a in pg["actions"] if a["id"] == gid)
                # an unlabeled gold (icon button, svg) or one whose label repeats among the candidates cannot be learned
                # from text: drop the step (35% / 13% of gold clicks before this filter)
                labs = [a["label"].strip().lower() for a in pg["actions"] if a["kind"] == ("fill" if kind == "fill" else "click")]
                if not lab.strip() or labs.count(lab.strip().lower()) > 1:
                    stats["unlearnable"] = stats.get("unlearnable", 0) + 1; hist.append({**done_hist, "action": lab[:80] or done_hist["action"]}); continue
                out.write(json.dumps({**base_case, "page_obj": pg, "gold_op": op, "gold_id": gid, "kind": kind, "label": lab, "gold_text": txt}, ensure_ascii=False) + "\n")
                stats["cases"] += 1
                hist.append({**done_hist, "action": lab[:80]})
    print("convert done", stats, "->", out_path)


if __name__ == "__main__":
    if sys.argv[1] == "fetch":
        fetch(int(sys.argv[2]), int(sys.argv[3]) if len(sys.argv) > 3 else 16)
    else:
        convert(sys.argv[2])