Spaces:
Sleeping
Sleeping
| """ | |
| Gradio UI — Agent Trajectory Replay Viewer for DataQA. | |
| Designed for judges: zero clicks needed, auto-plays on load. | |
| Tab per task, step slider, prominent metric cards, color-coded dataset. | |
| """ | |
| from __future__ import annotations | |
| import csv | |
| import io | |
| import gradio as gr | |
| from .environment import DataQAEnvironment, parse_issue_key | |
| from .tasks import list_tasks, PlantedIssue | |
| from ..models import DataQAAction | |
| # ── Pre-built agent trajectories (simulates baseline agent) ── | |
| AGENT_TRAJECTORIES = { | |
| # Demo trajectories: fixes are ONLY proposed where the correct value | |
| # is logically inferrable (computable, format conversion, or deducible from context). | |
| # Ambiguous fixes (any valid salary, any past date) are NOT proposed. | |
| "easy": [ | |
| { | |
| "issues": [ | |
| "row:4,col:name,issue:missing_value", | |
| "row:7,col:salary,issue:wrong_type", | |
| "row:11,col:department,issue:format_violation", | |
| "row:15,col:email,issue:inconsistent_value", | |
| "row:3,col:email,issue:format_violation", # FP | |
| ], | |
| "fixes": [], | |
| }, | |
| { | |
| "issues": [ | |
| "row:4,col:name,issue:missing_value", | |
| "row:7,col:salary,issue:wrong_type", | |
| "row:11,col:department,issue:format_violation", | |
| "row:15,col:email,issue:inconsistent_value", | |
| "row:12,col:start_date,issue:format_violation", | |
| "row:21,col:employee_id,issue:duplicate_row", | |
| ], | |
| "fixes": [ | |
| # All deterministic fixes: | |
| "row:4,col:name,fix:David Kim", # from email david.kim@ | |
| "row:7,col:salary,fix:75000", # "seventy-five thousand" → 75000 | |
| "row:11,col:department,fix:Engineering", # "Engneering" → "Engineering" | |
| "row:15,col:email,fix:oscar.rivera@company.com", # from name Oscar Rivera | |
| "row:12,col:start_date,fix:2022-11-03", # MM-DD-YYYY → YYYY-MM-DD | |
| ], | |
| }, | |
| ], | |
| "medium": [ | |
| { | |
| "issues": [ | |
| "row:5,col:total,issue:inconsistent_value", | |
| "row:10,col:category,issue:format_violation", | |
| "row:10,col:quantity,issue:wrong_type", | |
| "row:12,col:order_date,issue:format_violation", | |
| "row:29,col:product_name,issue:format_violation", | |
| "row:24,col:status,issue:format_violation", | |
| ], | |
| "fixes": [], | |
| }, | |
| { | |
| "issues": [ | |
| "row:5,col:total,issue:inconsistent_value", | |
| "row:10,col:category,issue:format_violation", | |
| "row:10,col:quantity,issue:wrong_type", | |
| "row:12,col:order_date,issue:format_violation", | |
| "row:19,col:order_id,issue:duplicate_row", | |
| "row:21,col:unit_price,issue:format_violation", | |
| "row:24,col:status,issue:format_violation", | |
| "row:29,col:product_name,issue:format_violation", | |
| ], | |
| "fixes": [ | |
| # All deterministic: | |
| "row:5,col:total,fix:42.00", # qty(1) * price(42.00) | |
| "row:10,col:category,fix:Sports", # "Fitness" → nearest valid | |
| "row:10,col:quantity,fix:10", # "1O" (letter O) → "10" | |
| "row:12,col:order_date,fix:2024-01-26", # DD/MM/YYYY → YYYY-MM-DD | |
| "row:24,col:status,fix:delivered", # "deliverred" → "delivered" | |
| "row:29,col:product_name,fix:Wireless Charger", # "Wireles" → "Wireless" | |
| "row:21,col:unit_price,fix:24.99", # 24.999 → round to 2 decimals | |
| ], | |
| }, | |
| ], | |
| "hard": [ | |
| { | |
| "issues": [ | |
| "row:14,col:training_time_hours,issue:out_of_range", | |
| "row:13,col:learning_rate,issue:out_of_range", | |
| "row:15,col:model_name,issue:missing_value", | |
| "row:9,col:batch_size,issue:format_violation", | |
| "row:10,col:train_size,issue:inconsistent_value", | |
| ], | |
| "fixes": [], | |
| }, | |
| { | |
| "issues": [ | |
| "row:14,col:training_time_hours,issue:out_of_range", | |
| "row:13,col:learning_rate,issue:out_of_range", | |
| "row:15,col:model_name,issue:missing_value", | |
| "row:9,col:batch_size,issue:format_violation", | |
| "row:10,col:train_size,issue:inconsistent_value", | |
| "row:5,col:val_loss,issue:inconsistent_value", | |
| "row:7,col:gpu_memory_gb,issue:statistical_outlier", | |
| "row:11,col:timestamp,issue:inconsistent_value", | |
| "row:9,col:training_time_hours,issue:statistical_outlier", | |
| "row:12,col:test_accuracy,issue:statistical_outlier", | |
| ], | |
| "fixes": [ | |
| # Only deterministic fixes: | |
| "row:9,col:batch_size,fix:256", # 250 → nearest power of 2 | |
| "row:14,col:training_time_hours,fix:72.0", # -72.0 → remove negative sign | |
| "row:15,col:model_name,fix:whisper-small", # "whsiper-small" → fix spelling | |
| # NOT proposed: row:13 LR (2.5 is out of range but any valid LR works) | |
| ], | |
| }, | |
| ], | |
| "alignment": [ | |
| { | |
| "issues": [ | |
| "row:6,col:response,issue:inconsistent_value", | |
| "row:15,col:response,issue:inconsistent_value", | |
| "row:28,col:prompt,issue:missing_value", | |
| "row:20,col:response,issue:inconsistent_value", | |
| "row:7,col:prompt,issue:duplicate_row", | |
| "row:25,col:response,issue:missing_value", | |
| "row:3,col:response,issue:inconsistent_value", | |
| ], | |
| "fixes": [], | |
| }, | |
| { | |
| "issues": [ | |
| "row:3,col:response,issue:inconsistent_value", | |
| "row:4,col:response,issue:inconsistent_value", | |
| "row:6,col:response,issue:inconsistent_value", | |
| "row:7,col:prompt,issue:duplicate_row", | |
| "row:8,col:response,issue:inconsistent_value", | |
| "row:11,col:response,issue:inconsistent_value", | |
| "row:15,col:response,issue:inconsistent_value", | |
| "row:23,col:helpfulness,issue:inconsistent_value", | |
| "row:20,col:response,issue:inconsistent_value", | |
| "row:25,col:response,issue:missing_value", | |
| "row:28,col:prompt,issue:missing_value", | |
| "row:29,col:response,issue:inconsistent_value", | |
| ], | |
| "fixes": [ | |
| # Inferrable: Salvator Mundi facts are well-known ($450.3M at Christie's) | |
| "row:4,col:response,fix:The most expensive painting ever sold at auction is Salvator Mundi by Leonardo da Vinci. It was sold for $450.3 million at Christie's in New York City in 2017.", | |
| # Inferrable: strip leaked [SYSTEM] prompt prefix | |
| "row:3,col:response,fix:Kitsch is art or design that is overly sentimental or ornate while camp is a style that is over-the-top and exaggerated often used in satire or irony.", | |
| # NOT proposed: row:6 wrong scientific name (need taxonomy knowledge) | |
| # NOT proposed: row:8 harmful advice (need to write safe version) | |
| # NOT proposed: row:11 self-contradiction (need to rewrite coherently) | |
| # NOT proposed: row:15 French response (need English translation) | |
| # NOT proposed: row:29 hallucinated citation (need factual replacement) | |
| ], | |
| }, | |
| ], | |
| "moderation": [ | |
| { | |
| "issues": [ | |
| "row:16,col:hate,issue:inconsistent_value", | |
| "row:17,col:harassment,issue:inconsistent_value", | |
| "row:20,col:violence,issue:inconsistent_value", | |
| "row:13,col:violence,issue:out_of_range", | |
| "row:15,col:text,issue:missing_value", | |
| "row:30,col:text,issue:duplicate_row", | |
| ], | |
| "fixes": [], | |
| }, | |
| { | |
| "issues": [ | |
| "row:5,col:hate,issue:inconsistent_value", | |
| "row:13,col:violence,issue:out_of_range", | |
| "row:15,col:text,issue:missing_value", | |
| "row:16,col:hate,issue:inconsistent_value", | |
| "row:17,col:harassment,issue:inconsistent_value", | |
| "row:20,col:violence,issue:inconsistent_value", | |
| "row:22,col:self_harm,issue:inconsistent_value", | |
| "row:24,col:hate,issue:inconsistent_value", | |
| "row:25,col:violence,issue:inconsistent_value", | |
| "row:30,col:text,issue:duplicate_row", | |
| ], | |
| "fixes": [ | |
| # All deterministic label flips: | |
| "row:16,col:hate,fix:1", # unflagged hate → flag it | |
| "row:17,col:harassment,fix:1", # unflagged harassment → flag it | |
| "row:20,col:violence,fix:1", # unflagged violence → flag it | |
| "row:22,col:self_harm,fix:1", # unflagged self-harm → flag it | |
| "row:5,col:hate,fix:0", # false positive on idiom → unflag | |
| "row:24,col:hate,fix:1", # subset rule: hate_threatening needs hate | |
| "row:25,col:violence,fix:0", # chose walk over violence → not violent | |
| "row:13,col:violence,fix:0", # out of range 3 → 0 | |
| ], | |
| }, | |
| ], | |
| } | |
| # ── HTML rendering ── | |
| def _metric_card(label: str, value: str, color: str = "#333") -> str: | |
| return ( | |
| f'<div style="text-align:center;padding:12px 16px;background:#f8f9fa;' | |
| f'border-radius:8px;min-width:100px;">' | |
| f'<div style="font-size:11px;color:#666;text-transform:uppercase;letter-spacing:1px;">{label}</div>' | |
| f'<div style="font-size:28px;font-weight:700;color:{color};margin-top:2px;">{value}</div>' | |
| f'</div>' | |
| ) | |
| def _csv_to_html( | |
| csv_text: str, | |
| planted: list[PlantedIssue], | |
| correct: set[tuple[int, str]], | |
| fp: set[tuple[int, str]], | |
| missed: set[tuple[int, str]], | |
| fixed: dict[tuple[int, str], str], | |
| fix_values: dict[tuple[int, str], str] | None = None, | |
| ) -> str: | |
| """Render CSV as HTML with color-coded cells and inline fix proposals.""" | |
| fix_values = fix_values or {} | |
| desc_map = {(i.row, i.col): i for i in planted} | |
| reader = csv.reader(io.StringIO(csv_text.strip())) | |
| rows = list(reader) | |
| if not rows: | |
| return "" | |
| header = rows[0] | |
| header_lower = [h.strip().lower() for h in header] | |
| data = rows[1:] | |
| t = ['<table style="border-collapse:collapse;width:100%;font-size:12px;font-family:\'SF Mono\',monospace;">'] | |
| t.append('<tr>') | |
| t.append('<th style="border:1px solid #dee2e6;padding:6px 8px;background:#343a40;color:#fff;font-size:11px;">Row</th>') | |
| for h in header: | |
| t.append(f'<th style="border:1px solid #dee2e6;padding:6px 8px;background:#343a40;color:#fff;font-size:11px;">{h}</th>') | |
| t.append('</tr>') | |
| for i, row in enumerate(data): | |
| rn = i + 1 | |
| bg = "#fff" if i % 2 == 0 else "#f8f9fa" | |
| t.append(f'<tr style="background:{bg};">') | |
| t.append(f'<td style="border:1px solid #dee2e6;padding:4px 8px;color:#adb5bd;text-align:center;font-size:11px;">{rn}</td>') | |
| for j, val in enumerate(row): | |
| col = header_lower[j] if j < len(header_lower) else "" | |
| ck = (rn, col) | |
| s = "border:1px solid #dee2e6;padding:4px 8px;" | |
| tip = "" | |
| badge = "" | |
| issue = desc_map.get(ck) | |
| if ck in correct: | |
| s += "background:#d4edda;" | |
| tip = f"FOUND: {issue.description}" if issue else "" | |
| badge = '<span style="font-size:9px;background:#28a745;color:#fff;padding:1px 4px;border-radius:3px;margin-left:4px;">TP</span>' | |
| elif ck in fp: | |
| s += "background:#f8d7da;" | |
| badge = '<span style="font-size:9px;background:#dc3545;color:#fff;padding:1px 4px;border-radius:3px;margin-left:4px;">FP</span>' | |
| elif ck in missed: | |
| s += "background:#fff3cd;" | |
| tip = f"MISSED: {issue.description}" if issue else "" | |
| badge = '<span style="font-size:9px;background:#856404;color:#fff;padding:1px 4px;border-radius:3px;margin-left:4px;">MISS</span>' | |
| fx = fixed.get(ck) | |
| proposed = fix_values.get(ck) | |
| if fx == "correct": | |
| s += "box-shadow:inset 0 0 0 2px #28a745;" | |
| badge += '<span style="font-size:9px;background:#28a745;color:#fff;padding:1px 4px;border-radius:3px;margin-left:2px;">FIX</span>' | |
| elif fx == "partial": | |
| s += "box-shadow:inset 0 0 0 2px #ffc107;" | |
| badge += '<span style="font-size:9px;background:#ffc107;color:#333;padding:1px 4px;border-radius:3px;margin-left:2px;">~FIX</span>' | |
| dv = val if val.strip() else '<em style="color:#dc3545;font-style:italic;">empty</em>' | |
| # Show proposed fix value below the corrupted value | |
| fix_line = "" | |
| if proposed is not None: | |
| fix_color = "#28a745" if fx == "correct" else ("#b8860b" if fx == "partial" else "#dc3545") | |
| fix_line = ( | |
| f'<div style="font-size:10px;color:{fix_color};margin-top:2px;' | |
| f'border-top:1px dashed {fix_color};padding-top:2px;">' | |
| f'\u2192 {proposed}</div>' | |
| ) | |
| t.append(f'<td style="{s}" title="{tip}">{dv}{badge}{fix_line}</td>') | |
| t.append('</tr>') | |
| t.append('</table>') | |
| return "".join(t) | |
| LEGEND_HTML = ( | |
| '<div style="display:flex;gap:12px;flex-wrap:wrap;margin-top:10px;font-size:11px;">' | |
| '<span style="background:#d4edda;padding:2px 8px;border-radius:4px;">Found (TP)</span>' | |
| '<span style="background:#f8d7da;padding:2px 8px;border-radius:4px;">False Positive</span>' | |
| '<span style="background:#fff3cd;padding:2px 8px;border-radius:4px;">Missed</span>' | |
| '<span style="box-shadow:inset 0 0 0 2px #28a745;padding:2px 8px;border-radius:4px;">Fix Correct</span>' | |
| '<span style="box-shadow:inset 0 0 0 2px #ffc107;padding:2px 8px;border-radius:4px;">Fix Partial</span>' | |
| '</div>' | |
| ) | |
| # ── Core replay logic ── | |
| def _replay_task(task_id: str) -> list[dict]: | |
| """Run the agent trajectory and collect per-step data.""" | |
| env = DataQAEnvironment() | |
| obs = env.reset(task_id=task_id) | |
| task = env._current_task | |
| planted_keys = {i.to_key() for i in task.planted_issues} | |
| steps_data = [] | |
| # Step 0: initial state | |
| steps_data.append({ | |
| "label": "Initial — corrupted dataset", | |
| "html": _csv_to_html(obs.dataset_csv, task.planted_issues, set(), set(), set(), {}), | |
| "metrics": {"reward": 0.0, "tp": 0, "fp": 0, "fn": len(task.planted_issues), | |
| "identify": 0.0, "fix": 0.0, "fixes_correct": 0}, | |
| "feedback": f"Task: {task.name}\nIssues to find: {obs.num_issues_hint}\n\n{task.description}", | |
| }) | |
| trajectory = AGENT_TRAJECTORIES.get(task_id, []) | |
| for i, step_data in enumerate(trajectory): | |
| action = DataQAAction( | |
| issues=step_data["issues"], | |
| fixes=step_data.get("fixes", []), | |
| task_id=task_id, | |
| ) | |
| obs = env.step(action) | |
| reported_keys = set() | |
| for iss in step_data["issues"]: | |
| key = parse_issue_key(iss) | |
| if key: | |
| reported_keys.add(key) | |
| tp_keys = reported_keys & planted_keys | |
| fp_keys = reported_keys - planted_keys | |
| fn_keys = planted_keys - reported_keys | |
| correct = {_kc(k) for k in tp_keys} | |
| fp = {_kc(k) for k in fp_keys} | |
| missed = {_kc(k) for k in fn_keys} if obs.done else set() | |
| fixed: dict[tuple[int, str], str] = {} | |
| for d in obs.metadata.get("fix_details", []): | |
| c = (d["row"], d["col"]) | |
| fixed[c] = "correct" if d["score"] >= 0.99 else ("partial" if d["score"] > 0 else "wrong") | |
| # Extract proposed fix values from the raw fix strings | |
| fix_values: dict[tuple[int, str], str] = {} | |
| from .environment import parse_fix | |
| for raw_fix in step_data.get("fixes", []): | |
| parsed = parse_fix(raw_fix) | |
| if parsed: | |
| row, col, val = parsed | |
| fix_values[(row, col)] = val | |
| html = _csv_to_html(obs.dataset_csv, task.planted_issues, correct, fp, missed, fixed, fix_values) | |
| has_fixes = bool(step_data.get("fixes")) | |
| if has_fixes: | |
| label = f"Step {i+1} — identify + fix" | |
| else: | |
| label = f"Step {i+1} — identify only" | |
| steps_data.append({ | |
| "label": label, | |
| "html": html, | |
| "metrics": { | |
| "reward": obs.reward, | |
| "tp": obs.metadata["tp"], | |
| "fp": obs.metadata["fp"], | |
| "fn": obs.metadata["fn"], | |
| "identify": obs.metadata["identify_score"], | |
| "fix": obs.metadata["fix_score"], | |
| "fixes_correct": obs.metadata["fixes_correct"], | |
| }, | |
| "feedback": obs.feedback, | |
| }) | |
| return steps_data | |
| def _kc(key: str) -> tuple[int, str]: | |
| parts = key.split(",") | |
| return (int(parts[0].split(":")[1]), parts[1].split(":")[1]) | |
| # ── Gradio app ── | |
| def build_gradio_ui(): | |
| # Pre-compute all replays at startup | |
| all_replays: dict[str, list[dict]] = {} | |
| for tid in list_tasks(): | |
| all_replays[tid] = _replay_task(tid) | |
| def show_step(task_id: str, step_idx: int): | |
| replay = all_replays.get(task_id, []) | |
| step_idx = int(step_idx) | |
| if step_idx >= len(replay): | |
| step_idx = len(replay) - 1 | |
| sd = replay[step_idx] | |
| m = sd["metrics"] | |
| # Reward color | |
| r = m["reward"] | |
| rc = "#28a745" if r >= 0.8 else ("#ffc107" if r >= 0.4 else "#dc3545") | |
| cards = ( | |
| '<div style="display:flex;gap:10px;flex-wrap:wrap;margin-bottom:12px;">' | |
| + _metric_card("Reward", f"{r:.2f}", rc) | |
| + _metric_card("Found", str(m["tp"]), "#28a745") | |
| + _metric_card("False Pos", str(m["fp"]), "#dc3545" if m["fp"] > 0 else "#28a745") | |
| + _metric_card("Missed", str(m["fn"]), "#dc3545" if m["fn"] > 0 else "#28a745") | |
| + _metric_card("Identify", f"{m['identify']:.2f}", "#333") | |
| + _metric_card("Fix", f"{m['fix']:.2f}", "#333") | |
| + '</div>' | |
| ) | |
| full_html = ( | |
| f'<div style="font-size:14px;font-weight:600;margin-bottom:8px;color:#495057;">' | |
| f'{sd["label"]}</div>' | |
| + cards + sd["html"] + LEGEND_HTML | |
| ) | |
| return full_html, sd["feedback"] | |
| def on_task_change(task_id): | |
| replay = all_replays.get(task_id, []) | |
| max_step = len(replay) - 1 | |
| html, fb = show_step(task_id, 0) | |
| return ( | |
| gr.update(maximum=max_step, value=0), | |
| html, | |
| fb, | |
| ) | |
| def on_step_change(task_id, step_idx): | |
| html, fb = show_step(task_id, step_idx) | |
| return html, fb | |
| # ── Live agent runner (connects to the env server) ── | |
| live_env = DataQAEnvironment() | |
| live_state: dict = {"obs": None, "task_id": "easy", "steps": []} | |
| def live_reset(task_id): | |
| obs = live_env.reset(task_id=task_id) | |
| task = live_env._current_task | |
| live_state["obs"] = obs | |
| live_state["task_id"] = task_id | |
| live_state["steps"] = [] | |
| html = _csv_to_html(obs.dataset_csv, task.planted_issues, set(), set(), set(), {}) | |
| info = f"**{task.name}** — {obs.num_issues_hint} issues to find, {obs.max_steps} steps max" | |
| return html, info, "", "0.000" | |
| def live_step(issues_text, fixes_text): | |
| if live_state["obs"] is None: | |
| return "Reset first.", "", "", "" | |
| obs = live_state["obs"] | |
| task = live_env._current_task | |
| planted_keys = {i.to_key() for i in task.planted_issues} | |
| issues = [l.strip() for l in issues_text.strip().split("\n") if l.strip()] | |
| fixes = [l.strip() for l in fixes_text.strip().split("\n") if l.strip()] if fixes_text.strip() else [] | |
| action = DataQAAction(issues=issues, fixes=fixes, task_id=live_state["task_id"]) | |
| obs = live_env.step(action) | |
| live_state["obs"] = obs | |
| reported_keys = set() | |
| for iss in issues: | |
| key = parse_issue_key(iss) | |
| if key: | |
| reported_keys.add(key) | |
| tp_keys = reported_keys & planted_keys | |
| fp_keys = reported_keys - planted_keys | |
| fn_keys = planted_keys - reported_keys | |
| correct = {_kc(k) for k in tp_keys} | |
| fp_set = {_kc(k) for k in fp_keys} | |
| missed = {_kc(k) for k in fn_keys} if obs.done else set() | |
| fixed: dict[tuple[int, str], str] = {} | |
| for d in obs.metadata.get("fix_details", []): | |
| c = (d["row"], d["col"]) | |
| fixed[c] = "correct" if d["score"] >= 0.99 else ("partial" if d["score"] > 0 else "wrong") | |
| from .environment import parse_fix | |
| fix_values: dict[tuple[int, str], str] = {} | |
| for raw in fixes: | |
| parsed = parse_fix(raw) | |
| if parsed: | |
| fix_values[(parsed[0], parsed[1])] = parsed[2] | |
| html = _csv_to_html(obs.dataset_csv, task.planted_issues, correct, fp_set, missed, fixed, fix_values) | |
| m = obs.metadata | |
| r = obs.reward | |
| rc = "#28a745" if r >= 0.8 else ("#ffc107" if r >= 0.4 else "#dc3545") | |
| cards = ( | |
| '<div style="display:flex;gap:10px;flex-wrap:wrap;margin-bottom:12px;">' | |
| + _metric_card("Reward", f"{r:.2f}", rc) | |
| + _metric_card("Found", str(m["tp"]), "#28a745") | |
| + _metric_card("False Pos", str(m["fp"]), "#dc3545" if m["fp"] > 0 else "#28a745") | |
| + _metric_card("Missed", str(m["fn"]), "#dc3545" if m["fn"] > 0 else "#28a745") | |
| + '</div>' | |
| ) | |
| full_html = cards + html + LEGEND_HTML | |
| return full_html, obs.feedback, f"{r:.3f}", "" | |
| # ── Build the UI ── | |
| with gr.Blocks(title="DataQA Environment") as demo: | |
| gr.Markdown( | |
| "# DataQA — Data Quality Assurance Environment\n" | |
| "Two-phase RL environment: **Identify** data quality issues, then **Fix** them." | |
| ) | |
| with gr.Tabs(): | |
| # ── Tab 1: Demo replay ── | |
| with gr.Tab("Demo (Baseline Agent)"): | |
| gr.Markdown( | |
| "*Replay of the baseline Qwen-72B agent. " | |
| "Use the slider to step through the agent's trajectory.*" | |
| ) | |
| with gr.Row(): | |
| task_dd = gr.Dropdown(choices=list_tasks(), value="easy", label="Task", scale=1) | |
| step_slider = gr.Slider(minimum=0, maximum=2, step=1, value=0, label="Step", scale=3) | |
| viz_html = gr.HTML() | |
| feedback_box = gr.Textbox(label="Agent Feedback", lines=10, interactive=False) | |
| task_dd.change(on_task_change, inputs=[task_dd], outputs=[step_slider, viz_html, feedback_box]) | |
| step_slider.change(on_step_change, inputs=[task_dd, step_slider], outputs=[viz_html, feedback_box]) | |
| demo.load(on_task_change, inputs=[task_dd], outputs=[step_slider, viz_html, feedback_box]) | |
| # ── Tab 2: Try your own agent ── | |
| with gr.Tab("Try Your Own Agent"): | |
| gr.Markdown( | |
| "*Submit your own issues and fixes to see how the environment scores them. " | |
| "This is the same environment the baseline agent talks to.*" | |
| ) | |
| with gr.Row(): | |
| live_task_dd = gr.Dropdown(choices=list_tasks(), value="easy", label="Task", scale=1) | |
| live_reset_btn = gr.Button("Reset", variant="primary", scale=1) | |
| with gr.Row(): | |
| live_info = gr.Markdown() | |
| live_reward = gr.Textbox(label="Reward", interactive=False, scale=1) | |
| live_viz = gr.HTML() | |
| with gr.Row(): | |
| live_issues = gr.Textbox( | |
| label="Issues (one per line)", | |
| placeholder="row:4,col:name,issue:missing_value\nrow:7,col:salary,issue:wrong_type", | |
| lines=5, | |
| ) | |
| live_fixes = gr.Textbox( | |
| label="Fixes (one per line, optional)", | |
| placeholder="row:4,col:name,fix:David Kim\nrow:7,col:salary,fix:75000", | |
| lines=5, | |
| ) | |
| live_step_btn = gr.Button("Submit Step", variant="primary") | |
| live_feedback = gr.Textbox(label="Feedback", lines=10, interactive=False) | |
| live_reset_btn.click( | |
| live_reset, inputs=[live_task_dd], | |
| outputs=[live_viz, live_info, live_feedback, live_reward], | |
| ) | |
| live_step_btn.click( | |
| live_step, inputs=[live_issues, live_fixes], | |
| outputs=[live_viz, live_feedback, live_reward, live_issues], | |
| ) | |
| return demo | |
| if __name__ == "__main__": | |
| demo = build_gradio_ui() | |
| demo.launch() | |