app.py

import json
import os
import re
import time
from dataclasses import dataclass, field
from datetime import date
from typing import Any, Dict, List, Optional, Set, Tuple, Union

import gradio as gr
import requests
from bs4 import BeautifulSoup
from duckduckgo_search import DDGS
from huggingface_hub import InferenceClient


# --- Model configuration ---------------------------------------------------
# Our own DeepResearch model. When QUEST_BASE_URL is configured in Space
# Secrets, the app will route requests to that dedicated HF Inference Endpoint
# instead of the shared HF Inference API.
QUEST_MODEL_ID = "osunlp/QUEST-35B"
QUEST_BASE_URL = os.getenv("QUEST_BASE_URL", "").strip()
# Endpoints built from the TGI image expose a single-model OpenAI route; the
# model name passed to chat_completion is usually "tgi". vLLM endpoints usually
# want the original repo id. QUEST_ENDPOINT_MODEL overrides this if needed.
QUEST_ENDPOINT_MODEL = os.getenv("QUEST_ENDPOINT_MODEL", "tgi").strip() or "tgi"

# This Space runs exclusively on QUEST-35B served via the private HF Inference
# Endpoint pointed to by QUEST_BASE_URL. No public fallback list — the model
# field in the UI is display-only.
DEFAULT_MODEL = QUEST_MODEL_ID

# Internal defaults. Search budget is no longer user-tunable.
DEFAULT_MAX_SEARCH_RESULTS = 10

PAPER_URL = os.getenv("PAPER_URL", "https://osu-nlp-group.github.io/quest-gh-test/")
CODE_URL = os.getenv("CODE_URL", "https://github.com/OSU-NLP-Group/QUEST")
DATASET_URL = os.getenv("DATASET_URL", "https://huggingface.co/collections/osunlp/quest")
MODEL_URL = os.getenv("MODEL_URL", "https://huggingface.co/osunlp/QUEST-35B-RL")


# --- System prompt ---------------------------------------------------------
# Full QUEST SYSTEM_PROMPT (mirrors inference/prompt.py in the research repo)
# so that QUEST-35B sees the exact tool schema it was trained with. Other
# models still follow this schema just fine in practice.
QUEST_SYSTEM_PROMPT = """You are a deep research assistant. Your core function is to conduct thorough, multi-source investigations into any topic. You must handle both broad, open-domain inquiries and queries within specialized academic fields. For every request, synthesize information from credible, diverse sources to deliver a comprehensive, accurate, and objective response. When you have gathered sufficient information and are ready to provide the definitive response, you must enclose the entire final answer within <answer></answer> tags.

# Tools

You may call one or more functions to assist with the user query.

You are provided with function signatures within <tools></tools> XML tags:
<tools>
{"type": "function", "function": {"name": "search", "description": "Perform Google web searches then returns a string of the top search results. Accepts multiple queries.", "parameters": {"type": "object", "properties": {"query": {"type": "array", "items": {"type": "string", "description": "The search query."}, "minItems": 1, "description": "The list of search queries."}}, "required": ["query"]}}}
{"type": "function", "function": {"name": "visit", "description": "Visit webpage(s) and return the summary of the content.", "parameters": {"type": "object", "properties": {"url": {"type": "array", "items": {"type": "string"}, "description": "The URL(s) of the webpage(s) to visit. Can be a single URL or an array of URLs."}, "goal": {"type": "string", "description": "The specific information goal for visiting webpage(s)."}}, "required": ["url", "goal"]}}}
</tools>

# Using prev_state (Research State Summary)

If you see a "RESEARCH STATE SUMMARY (prev_state)" section in the user message, it contains a compressed summary of previous research progress. Use it to avoid repeating searches/visits that have already been executed, use verified information directly in your answer, and follow up on uncertain claims only when needed.

For each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:
<tool_call>
{"name": <function-name>, "arguments": <args-json-object>}
</tool_call>

Current date: """


def build_system_prompt() -> str:
    return QUEST_SYSTEM_PROMPT + date.today().isoformat()


TOOL_RESPONSE_TEMPLATE = """<tool_response>
{payload}
</tool_response>"""

SEARCH_CACHE: Dict[str, Dict[str, Any]] = {}
VISIT_CACHE: Dict[str, Dict[str, Any]] = {}
# Quest paper palette. The Gradio shell is themed to match the OSU-NLP Quest
# microsite: soft off-white page, paper-white cards, terracotta accent, mint
# secondary, Manrope for UI type and Source Serif 4 for display headings.
APP_THEME = gr.themes.Base(
    primary_hue=gr.themes.colors.orange,
    secondary_hue=gr.themes.colors.teal,
    neutral_hue=gr.themes.colors.slate,
    font=[
        gr.themes.GoogleFont("Manrope"),
        "ui-sans-serif",
        "system-ui",
        "sans-serif",
    ],
    font_mono=[
        gr.themes.GoogleFont("JetBrains Mono"),
        "ui-monospace",
        "monospace",
    ],
).set(
    body_background_fill="#F2F4F8",
    body_text_color="#0D1117",
    body_text_color_subdued="#64748B",
    color_accent="#BE5B2B",
    color_accent_soft="rgba(190,91,43,0.09)",
    background_fill_primary="#FFFFFF",
    background_fill_secondary="#EEF1F7",
    border_color_primary="rgba(10,15,40,0.08)",
    border_color_accent="#BE5B2B",
    block_background_fill="#FFFFFF",
    block_border_width="1px",
    block_border_color="rgba(10,15,40,0.08)",
    block_shadow="0 1px 2px rgba(10,15,40,0.05), 0 2px 10px rgba(10,15,40,0.06)",
    block_radius="16px",
    block_label_background_fill="transparent",
    block_label_border_width="0px",
    block_label_text_color="#64748B",
    block_label_text_weight="700",
    block_title_text_color="#0D1117",
    block_title_text_weight="700",
    block_title_border_width="0px",
    panel_background_fill="transparent",
    panel_border_width="0px",
    panel_border_color="transparent",
    input_background_fill="#FFFFFF",
    input_background_fill_focus="#FFFFFF",
    input_border_color="rgba(10,15,40,0.12)",
    input_border_color_focus="#BE5B2B",
    input_border_width="1px",
    input_radius="12px",
    input_shadow="none",
    input_shadow_focus="0 0 0 3px rgba(190,91,43,0.15)",
    code_background_fill="#EEF1F7",
    slider_color="#BE5B2B",
    button_primary_background_fill="#0D1117",
    button_primary_background_fill_hover="#1F2A37",
    button_primary_text_color="#FFFFFF",
    button_primary_border_color="transparent",
    button_primary_shadow="0 1px 2px rgba(10,15,40,0.08), 0 6px 18px rgba(10,15,40,0.12)",
    button_secondary_background_fill="#FFFFFF",
    button_secondary_background_fill_hover="rgba(190,91,43,0.09)",
    button_secondary_text_color="#BE5B2B",
    button_secondary_border_color="rgba(10,15,40,0.16)",
    button_cancel_background_fill="#FFFFFF",
    button_cancel_background_fill_hover="#FEE2E2",
    button_cancel_text_color="#DC2626",
    button_cancel_border_color="#FCA5A5",
    table_border_color="rgba(10,15,40,0.08)",
    table_even_background_fill="#FAFBFD",
    table_odd_background_fill="#FFFFFF",
)

CUSTOM_CSS = """
/* === Quest paper palette applied to the Gradio shell ==================== */
/* Brings the OSU-NLP Quest microsite aesthetic into the live Space: soft
   off-white background, paper-white cards with subtle 1px borders and
   low-opacity shadows, terracotta accent, Source Serif 4 for display
   headings, Manrope for everything else. */

:root {
  --q-bg: #F2F4F8;
  --q-paper: #FFFFFF;
  --q-surface-alt: #EEF1F7;
  --q-line: rgba(10, 15, 40, 0.08);
  --q-line-strong: rgba(10, 15, 40, 0.16);
  --q-text: #0D1117;
  --q-muted: #64748B;
  --q-accent: #BE5B2B;
  --q-accent-soft: rgba(190, 91, 43, 0.09);
  --q-accent-line: rgba(190, 91, 43, 0.55);
  --q-mint: #0B9E8A;
  --q-mint-deep: #0A8070;
  --q-cover-bg: #0D1117;
  --q-shadow: 0 1px 3px rgba(10,15,40,0.04), 0 8px 32px rgba(10,15,40,0.08);
  --q-shadow-card: 0 1px 2px rgba(10,15,40,0.05), 0 2px 10px rgba(10,15,40,0.06);
  --q-radius-xl: 20px;
  --q-radius-lg: 16px;
  --q-radius-md: 12px;
}

html, body, gradio-app, [class*="gradio-container"] {
  background: var(--q-bg) !important;
}

/* Full-height shell ------------------------------------------------------- */
html, body { width: 100% !important; min-height: 100vh !important; margin: 0 !important; font-size: 17px !important; }
gradio-app {
  display: block !important;
  width: 100% !important;
  min-height: 100vh !important;
  margin-left: auto !important;
  margin-right: auto !important;
}
gradio-app > .gradio-container,
gradio-app > div {
  display: block !important;
  width: 100% !important;
  margin-left: auto !important;
  margin-right: auto !important;
}

[class*="gradio-container"] {
  max-width: 1700px !important;
  width: 100% !important;
  min-width: 320px !important;
  margin-left: auto !important;
  margin-right: auto !important;
  padding: 28px 36px 72px !important;
  color: var(--q-text);
  box-sizing: border-box !important;
  font-family: "Manrope", ui-sans-serif, system-ui, sans-serif;
  font-size: 1rem !important;
}

[class*="gradio-container"] *::selection { background: rgba(190,91,43,0.18); }

/* Prevent inner wrappers from collapsing when streaming content first arrives. */
[class*="gradio-container"] .layout-gap { width: 100% !important; }
[class*="gradio-container"] .layout-gap > .gr-column,
[class*="gradio-container"] .layout-gap > div { min-width: 0 !important; }

[class*="gradio-container"] .gradio-markdown,
[class*="gradio-container"] [data-testid="markdown"] { min-height: 220px !important; }
[class*="gradio-container"] .codemirror-wrapper,
[class*="gradio-container"] .cm-editor { min-height: 220px !important; }

/* Long code / markdown cannot push the layout sideways. */
[class*="gradio-container"] .gradio-code,
[class*="gradio-container"] .gradio-markdown,
[class*="gradio-container"] .prose,
[class*="gradio-container"] .markdown,
[class*="gradio-container"] [data-testid="markdown"],
[class*="gradio-container"] .tabs,
[class*="gradio-container"] .tabitem,
[class*="gradio-container"] .tab-content {
  max-width: 100% !important;
  width: 100% !important;
  min-width: 0 !important;
  word-wrap: break-word !important;
  overflow-wrap: anywhere !important;
}
[class*="gradio-container"] .codemirror-wrapper {
  max-width: 100% !important;
  border-radius: 14px !important;
  overflow: hidden !important;
}
[class*="gradio-container"] .cm-editor { max-width: 100% !important; overflow: hidden !important; }
[class*="gradio-container"] .cm-scroller { max-width: 100% !important; overflow-x: auto !important; }
[class*="gradio-container"] .cm-content,
[class*="gradio-container"] .cm-line {
  max-width: 100% !important;
  white-space: pre-wrap !important;
  word-break: break-word !important;
}
[class*="gradio-container"] .prose pre,
[class*="gradio-container"] .markdown pre {
  max-width: 100% !important;
  overflow-x: auto !important;
  white-space: pre-wrap !important;
}

/* === Quest-style header ================================================= */
.quest-header {
  display: flex;
  align-items: center;
  gap: 18px;
  padding: 18px 22px;
  margin: 8px 0 24px;
  border: 1px solid var(--q-line);
  border-radius: var(--q-radius-lg);
  background: var(--q-paper);
  box-shadow: var(--q-shadow-card);
}
.quest-header-mark {
  display: grid;
  place-items: center;
  width: 48px;
  height: 48px;
  flex-shrink: 0;
  border-radius: 12px;
  background: var(--q-text);
  color: #FFFFFF;
  font-family: "Source Serif 4", "Source Serif Pro", ui-serif, Georgia, serif;
  font-weight: 700;
  font-size: 1.55rem;
}
.quest-header-text {
  display: flex;
  flex-direction: column;
  gap: 4px;
  min-width: 0;
}
.quest-header-title {
  margin: 0;
  font-family: "Source Serif 4", "Source Serif Pro", ui-serif, Georgia, serif;
  font-weight: 600;
  font-size: clamp(1.25rem, 2vw, 1.75rem);
  line-height: 1.2;
  letter-spacing: -0.01em;
  color: var(--q-text);
}
.quest-header-byline {
  color: var(--q-muted);
  font-size: 0.9rem;
  font-weight: 500;
  text-decoration: underline;
  text-decoration-color: rgba(100,116,139,0.45);
  text-underline-offset: 3px;
  text-decoration-thickness: 1px;
  width: fit-content;
  transition: color 140ms ease, text-decoration-color 140ms ease;
}
.quest-header-byline:hover {
  color: var(--q-accent);
  text-decoration-color: var(--q-accent);
}

/* === Cards (section-card) =============================================== */
.section-card {
  background: var(--q-paper) !important;
  border: 1px solid var(--q-line) !important;
  border-radius: var(--q-radius-xl) !important;
  box-shadow: var(--q-shadow-card) !important;
  padding: 22px !important;
}
.no-frame {
  background: transparent !important;
  border: none !important;
  box-shadow: none !important;
  padding: 0 !important;
}

/* Section kicker + hero heading follow the paper treatment. */
.section-heading {
  font-size: 0.7rem;
  font-weight: 800;
  letter-spacing: 0.14em;
  text-transform: uppercase;
  color: var(--q-accent);
  margin: 0 0 14px 0;
}
.hero-heading {
  font-family: "Source Serif 4", "Source Serif Pro", ui-serif, Georgia, serif !important;
  font-weight: 600 !important;
  font-size: 1.6rem !important;
  letter-spacing: -0.01em !important;
  text-transform: none !important;
  color: var(--q-text) !important;
}
/* Match the .brand mark from the Quest microsite (github-page branch). */
.quest-name {
  font-family: "Source Serif 4", "Source Serif Pro", ui-serif, Georgia, serif !important;
  font-style: italic !important;
  font-weight: 700 !important;
  color: inherit !important;
  letter-spacing: -0.005em;
  margin: 4px 0 14px 0 !important;
}
.hero-subtitle {
  color: var(--q-muted);
  font-size: 0.95rem;
  line-height: 1.6;
  margin: -6px 0 16px 0;
}

/* Layout gap: mirror the paper's column rhythm. */
.layout-gap { gap: 24px !important; align-items: flex-start; }
.right-stack > * { margin-bottom: 14px; }
.action-row { gap: 10px !important; margin-top: 14px; }
.action-row button { min-width: 0; flex: 1; }

/* === Icon grid (Paper / Code / Dataset / Model) ========================= */
.icon-grid {
  display: grid;
  grid-template-columns: repeat(2, minmax(0, 1fr));
  gap: 10px;
  width: 100%;
}
.icon-link {
  display: flex;
  align-items: center;
  justify-content: center;
  gap: 8px;
  padding: 11px 14px;
  border-radius: 999px;
  text-decoration: none !important;
  color: var(--q-text) !important;
  background: var(--q-paper);
  font-weight: 600;
  font-size: 0.88rem;
  white-space: nowrap;
  border: 1px solid var(--q-line-strong);
  transition: background 140ms ease, border-color 140ms ease, color 140ms ease, transform 140ms ease;
}
.icon-link:hover {
  background: var(--q-accent-soft);
  border-color: var(--q-accent-line);
  color: var(--q-accent) !important;
  transform: translateY(-1px);
}

/* Resource cards (paper / code / data / model) — icon + label, eye-catching */
.resource-grid {
  display: grid;
  grid-template-columns: repeat(2, minmax(0, 1fr));
  gap: 10px;
  width: 100%;
}
.resource-card {
  display: flex;
  align-items: center;
  gap: 10px;
  padding: 12px 14px;
  border-radius: 14px;
  text-decoration: none !important;
  color: var(--q-text) !important;
  background: var(--q-paper);
  border: 1px solid var(--q-line-strong);
  transition: background 140ms ease, border-color 140ms ease, color 140ms ease, transform 140ms ease;
}
.resource-card:hover {
  background: var(--q-accent-soft);
  border-color: var(--q-accent-line);
  color: var(--q-accent) !important;
  transform: translateY(-1px);
}
.resource-card-icon {
  display: inline-flex;
  align-items: center;
  justify-content: center;
  width: 30px;
  height: 30px;
  flex-shrink: 0;
  border-radius: 8px;
  background: var(--q-surface-alt);
  color: var(--q-text);
}
.resource-card-icon svg {
  width: 18px;
  height: 18px;
  fill: currentColor;
}
.resource-card-icon.resource-card-emoji {
  background: transparent;
  font-size: 22px;
  line-height: 1;
}
.resource-card-text {
  display: flex;
  flex-direction: column;
  line-height: 1.15;
  min-width: 0;
}
.resource-card-text strong {
  font-weight: 700;
  font-size: 0.92rem;
}
.resource-card-text small {
  font-size: 0.72rem;
  color: var(--q-muted);
  margin-top: 2px;
}

/* === Buttons ============================================================ */
[class*="gradio-container"] button.primary,
[class*="gradio-container"] .gr-button-primary {
  background: var(--q-text) !important;
  color: #ffffff !important;
  border: 1px solid var(--q-text) !important;
  box-shadow: 0 1px 2px rgba(10,15,40,0.08), 0 6px 18px rgba(10,15,40,0.12) !important;
  font-weight: 700 !important;
  letter-spacing: 0.01em !important;
}
[class*="gradio-container"] button.primary:hover,
[class*="gradio-container"] .gr-button-primary:hover {
  background: #1F2A37 !important;
  border-color: #1F2A37 !important;
}
[class*="gradio-container"] button.secondary,
[class*="gradio-container"] .gr-button-secondary {
  background: var(--q-paper) !important;
  color: var(--q-text) !important;
  border: 1px solid var(--q-line-strong) !important;
  box-shadow: none !important;
  font-weight: 600 !important;
}
[class*="gradio-container"] button.secondary:hover,
[class*="gradio-container"] .gr-button-secondary:hover {
  background: var(--q-accent-soft) !important;
  border-color: var(--q-accent-line) !important;
  color: var(--q-accent) !important;
}
[class*="gradio-container"] button.stop,
[class*="gradio-container"] .gr-button-stop {
  background: var(--q-paper) !important;
  color: #DC2626 !important;
  border: 1px solid #FCA5A5 !important;
  box-shadow: none !important;
  font-weight: 600 !important;
}
[class*="gradio-container"] button.stop:hover,
[class*="gradio-container"] .gr-button-stop:hover {
  background: #FEE2E2 !important;
  color: #B91C1C !important;
}

/* Flatten every grey block Gradio drops inside our cards. */
[class*="gradio-container"] .gr-group,
[class*="gradio-container"] fieldset,
[class*="gradio-container"] .gr-box,
[class*="gradio-container"] .gr-panel,
[class*="gradio-container"] .form,
[class*="gradio-container"] .gr-form,
[class*="gradio-container"] .container {
  background: transparent !important;
}
.section-card {
  --block-shadow: none;
  --block-shadow-dark: none;
  --block-background-fill: transparent;
  --block-border-color: transparent;
  --block-border-width: 0px;
  --panel-background-fill: transparent;
  --panel-border-width: 0px;
  --background-fill-secondary: transparent;
  --border-color-primary: transparent;
  overflow: visible !important;
}
.section-card > div,
.section-card > div > div,
.section-card > div > div > div {
  background: transparent !important;
  border: none !important;
  box-shadow: none !important;
  overflow: visible !important;
}
.section-card .block,
.section-card .form,
.section-card .gr-form,
.section-card .gr-block,
.section-card .gr-panel,
.section-card .gr-group,
.section-card .gradio-dropdown,
.section-card .gradio-slider,
.section-card .gradio-textbox,
.section-card .gradio-markdown,
.section-card .gradio-code {
  background: transparent !important;
  border: none !important;
  box-shadow: none !important;
  overflow: visible !important;
}
.section-card .form,
.section-card .gr-form {
  display: flex !important;
  flex-direction: column !important;
  gap: 14px !important;
}
[class*="gradio-container"] .section-card .row,
[class*="gradio-container"] .section-card [class*="row"] {
  display: flex !important;
  flex-direction: row !important;
  flex-wrap: wrap !important;
  gap: 10px !important;
}
.action-row {
  display: flex !important;
  flex-direction: row !important;
  gap: 10px !important;
  margin-top: 14px;
}
.action-row > * { flex: 1 1 0; min-width: 0; }
.section-card > * + * { margin-top: 14px; }

/* === Inputs ============================================================= */
[class*="gradio-container"] textarea,
[class*="gradio-container"] input:not([type="checkbox"]):not([type="radio"]):not([type="range"]) {
  background: var(--q-paper) !important;
  border: 1px solid var(--q-line-strong) !important;
  box-shadow: none !important;
  border-radius: var(--q-radius-md) !important;
  color: var(--q-text) !important;
  font-family: "Manrope", ui-sans-serif, system-ui, sans-serif !important;
}
/* Make the Model Textbox match the Memory Strategy Dropdown's height (46px outer = 44px content + 2*1px border). */
.section-card [data-testid="textbox"] textarea,
.section-card [data-testid="textbox"] input {
  min-height: 44px !important;
  padding: 11px 14px !important;
  line-height: 1.4 !important;
  box-sizing: border-box !important;
}
[class*="gradio-container"] textarea::placeholder,
[class*="gradio-container"] input::placeholder { color: #94A3B8 !important; }
[class*="gradio-container"] textarea:focus,
[class*="gradio-container"] input:focus {
  border-color: var(--q-accent) !important;
  box-shadow: 0 0 0 3px rgba(190,91,43,0.15) !important;
  outline: none !important;
}

/* === Dropdown =========================================================== */
[class*="gradio-container"] [data-testid="dropdown"],
[class*="gradio-container"] .gradio-dropdown {
  background: var(--q-paper) !important;
  border: 1px solid var(--q-line-strong) !important;
  border-radius: var(--q-radius-md) !important;
  box-shadow: none !important;
  padding: 0 !important;
  min-height: 46px !important;
  width: 100% !important;
  box-sizing: border-box !important;
}
[class*="gradio-container"] [data-testid="dropdown"] > .wrap,
[class*="gradio-container"] [data-testid="dropdown"] .secondary-wrap,
[class*="gradio-container"] [data-testid="dropdown"] .wrap-inner,
[class*="gradio-container"] [data-testid="dropdown"] .input-container,
[class*="gradio-container"] [data-testid="dropdown"] .single-select,
[class*="gradio-container"] .gradio-dropdown .wrap,
[class*="gradio-container"] .gradio-dropdown .wrap-inner,
[class*="gradio-container"] .gradio-dropdown .secondary-wrap,
[class*="gradio-container"] .gradio-dropdown .input-container,
[class*="gradio-container"] .gradio-dropdown .single-select,
[class*="gradio-container"] [class*="dropdown"] .wrap {
  background: transparent !important;
  border: 0 !important;
  outline: 0 !important;
  box-shadow: none !important;
  border-radius: 0 !important;
  width: 100% !important;
  min-height: 44px !important;
  padding: 0 14px !important;
  display: flex !important;
  align-items: center !important;
  box-sizing: border-box !important;
}
[class*="gradio-container"] [data-testid="dropdown"] input,
[class*="gradio-container"] .gradio-dropdown input,
[class*="gradio-container"] [data-testid="dropdown"] select,
[class*="gradio-container"] .gradio-dropdown select {
  background: transparent !important;
  border: 0 !important;
  outline: 0 !important;
  box-shadow: none !important;
  padding: 0 !important;
  height: 44px !important;
  line-height: 44px !important;
  font-size: 0.95rem !important;
  width: 100% !important;
  border-radius: 0 !important;
}
/* Force-remove any nested pill/rounded background that makes the dropdown
   look like it has two concentric frames. */
[class*="gradio-container"] [data-testid="dropdown"] .container,
[class*="gradio-container"] [data-testid="dropdown"] .wrap > .wrap,
[class*="gradio-container"] .gradio-dropdown .container,
[class*="gradio-container"] .gradio-dropdown .wrap > .wrap {
  border: 0 !important;
  outline: 0 !important;
  box-shadow: none !important;
  background: transparent !important;
  border-radius: 0 !important;
  padding: 0 !important;
}
/* The little caret/arrow icon container — vertically center it */
[class*="gradio-container"] [data-testid="dropdown"] .icon-wrap,
[class*="gradio-container"] .gradio-dropdown .icon-wrap {
  top: 50% !important;
  transform: translateY(-50%) !important;
  right: 14px !important;
}
[class*="gradio-container"] .options ul,
[class*="gradio-container"] .options {
  background: var(--q-paper) !important;
  border: 1px solid var(--q-line) !important;
  border-radius: var(--q-radius-md) !important;
  box-shadow: 0 10px 30px rgba(10,15,40,0.12) !important;
}
[class*="gradio-container"] .options li[aria-selected="true"],
[class*="gradio-container"] .options li:hover {
  background: var(--q-accent-soft) !important;
  color: var(--q-accent) !important;
}

/* Info hint text under inputs */
[class*="gradio-container"] .info,
[class*="gradio-container"] [data-testid*="info"],
[class*="gradio-container"] .gr-info {
  color: var(--q-muted) !important;
  background: transparent !important;
  font-size: 12px !important;
}

/* === Sliders ============================================================ */
/* Flatten the Slider's outer wrapper — Gradio paints a rectangular block
   around the label + track + value-input by default; remove it. */
.section-card .gradio-slider,
.section-card .gradio-slider > div,
.section-card .gradio-slider .form,
.section-card .gradio-slider .gr-form,
.section-card .gradio-slider .wrap,
.section-card .gradio-slider .container,
.section-card .gradio-slider .head {
  background: transparent !important;
  border: 0 !important;
  box-shadow: none !important;
  padding: 0 !important;
}

/* === Per-component flatteners (id-based; max specificity vs Gradio defaults) === */
/* The Memory Strategy dropdown and the two sliders ship with an outer block
   wrapper that paints a small rectangle. Flatten the wrapper AND any nested
   div Gradio inserts (form/container/wrap/etc), keeping label + interactive
   element visible. */
#quest-memory-strategy,
#quest-memory-strategy > div,
#quest-memory-strategy .form,
#quest-memory-strategy .gr-form,
#quest-memory-strategy .container,
#quest-memory-strategy .wrap-inner,
#quest-memory-strategy .head,
#quest-max-turns,
#quest-max-turns > div,
#quest-max-turns .form,
#quest-max-turns .gr-form,
#quest-max-turns .container,
#quest-max-turns .wrap-inner,
#quest-max-turns .head,
#quest-temperature,
#quest-temperature > div,
#quest-temperature .form,
#quest-temperature .gr-form,
#quest-temperature .container,
#quest-temperature .wrap-inner,
#quest-temperature .head,
#quest-model,
#quest-model > div,
#quest-model .form,
#quest-model .gr-form,
#quest-model .container,
#quest-model .wrap-inner,
#quest-model .head {
  background: transparent !important;
  border: 0 !important;
  outline: 0 !important;
  box-shadow: none !important;
  padding: 0 !important;
  border-radius: 0 !important;
}
/* Memory Strategy radio: stack vertically, terracotta-tinted check state. */
#quest-memory-strategy .wrap,
#quest-memory-strategy fieldset,
#quest-memory-strategy [data-testid="radio"] {
  display: flex !important;
  flex-direction: column !important;
  gap: 6px !important;
  background: transparent !important;
  border: 0 !important;
  padding: 0 !important;
}
#quest-memory-strategy label {
  background: transparent !important;
  border: 1px solid var(--q-line) !important;
  border-radius: 8px !important;
  padding: 8px 12px !important;
  cursor: pointer !important;
  font-weight: 500 !important;
  font-size: 0.95rem !important;
  color: var(--q-text) !important;
  text-transform: none !important;
  letter-spacing: 0 !important;
  display: flex !important;
  align-items: center !important;
  gap: 10px !important;
  transition: border-color 120ms ease, background 120ms ease;
}
#quest-memory-strategy label:hover {
  border-color: var(--q-line-strong) !important;
}
#quest-memory-strategy input[type="radio"] {
  accent-color: var(--q-accent) !important;
  width: 16px !important;
  height: 16px !important;
}

/* Slider head input (the "[6 ↺]" / "[1 ↺]" pill next to the slider track):
   the global input rule paints a 1px border on it, which looks like a stray
   rectangle. Flatten it AND hide the reset button (it's redundant — the
   slider's range already shows the default value). */
#quest-max-turns input[type="number"],
#quest-temperature input[type="number"] {
  border: 0 !important;
  background: transparent !important;
  box-shadow: none !important;
  border-radius: 0 !important;
  padding: 0 !important;
  min-height: 0 !important;
  height: auto !important;
  text-align: center !important;
  width: 3.5em !important;
  font-weight: 600 !important;
  color: var(--q-text) !important;
}
#quest-max-turns button,
#quest-temperature button {
  display: none !important;
}
[class*="gradio-container"] input[type="range"] {
  -webkit-appearance: none;
  appearance: none;
  width: 100%;
  height: 6px;
  background: var(--q-surface-alt);
  border-radius: 999px;
  outline: none;
  box-shadow: none !important;
  border: none !important;
}
[class*="gradio-container"] input[type="range"]::-webkit-slider-runnable-track {
  height: 6px;
  background: linear-gradient(90deg,var(--q-accent) var(--val,50%), var(--q-surface-alt) var(--val,50%));
  border-radius: 999px;
}
[class*="gradio-container"] input[type="range"]::-webkit-slider-thumb {
  -webkit-appearance: none;
  appearance: none;
  width: 18px;
  height: 18px;
  border-radius: 50%;
  background: #ffffff;
  border: 2px solid var(--q-accent);
  box-shadow: 0 2px 6px rgba(190,91,43,0.25);
  margin-top: -6px;
  cursor: pointer;
}
[class*="gradio-container"] input[type="range"]::-moz-range-track {
  height: 6px;
  background: var(--q-surface-alt);
  border-radius: 999px;
}
[class*="gradio-container"] input[type="range"]::-moz-range-progress {
  height: 6px;
  background: var(--q-accent);
  border-radius: 999px;
}
[class*="gradio-container"] input[type="range"]::-moz-range-thumb {
  width: 16px;
  height: 16px;
  border-radius: 50%;
  background: #ffffff;
  border: 2px solid var(--q-accent);
  box-shadow: 0 2px 6px rgba(190,91,43,0.25);
}

/* === Tabs =============================================================== */
[class*="gradio-container"] .tabs,
[class*="gradio-container"] .tab-container,
[class*="gradio-container"] .tab-wrapper { background: transparent !important; }
[class*="gradio-container"] .tab-container::after { background: var(--q-line) !important; }
[class*="gradio-container"] .tab-wrapper button {
  color: var(--q-muted) !important;
  font-weight: 700 !important;
  letter-spacing: 0.04em !important;
  text-transform: uppercase !important;
  font-size: 0.78rem !important;
}
[class*="gradio-container"] .tab-wrapper button.selected { color: var(--q-accent) !important; }
[class*="gradio-container"] .tab-wrapper button.selected::after { background: var(--q-accent) !important; }
/* Hide the orange streaming-progress bar that Gradio paints at the top of
   the Markdown/Code panel while a run is in flight. */
[class*="gradio-container"] .progress,
[class*="gradio-container"] .progress-level,
[class*="gradio-container"] .progress-level-inner,
[class*="gradio-container"] .progress-bar,
[class*="gradio-container"] .progress-text,
[class*="gradio-container"] [class*="progress-level"],
[class*="gradio-container"] .generating,
[class*="gradio-container"] div[class*="progress-bar"] {
  display: none !important;
  background: transparent !important;
  border: 0 !important;
  height: 0 !important;
}
/* Kill any stray orange/thick separator that Gradio paints above the tab
   panel content (border-top or ::before on the tab content wrapper). */
[class*="gradio-container"] .tabitem,
[class*="gradio-container"] .tab-content,
[class*="gradio-container"] .gradio-tabitem,
[class*="gradio-container"] .tabs > div.tabitem {
  border-top: 0 !important;
  box-shadow: none !important;
  background: transparent !important;
}
[class*="gradio-container"] .tabitem::before,
[class*="gradio-container"] .tab-content::before,
[class*="gradio-container"] .gradio-tabitem::before { content: none !important; }
[class*="gradio-container"] .tab-nav,
[class*="gradio-container"] .tab-wrapper {
  border-bottom: 1px solid var(--q-line) !important;
  border-top: 0 !important;
}
[class*="gradio-container"] .tab-nav::before,
[class*="gradio-container"] .tab-wrapper::before { content: none !important; }

/* Block labels above components */
[class*="gradio-container"] .gr-block label,
[class*="gradio-container"] .gradio-slider label,
[class*="gradio-container"] .gradio-dropdown label,
[class*="gradio-container"] .gradio-textbox label {
  color: var(--q-muted) !important;
  font-weight: 700 !important;
  font-size: 0.74rem !important;
  letter-spacing: 0.08em !important;
  text-transform: uppercase !important;
}

/* === Markdown / prose =================================================== */
[class*="gradio-container"] .gr-markdown,
[class*="gradio-container"] .prose,
[class*="gradio-container"] .markdown {
  color: var(--q-text) !important;
  font-family: "Manrope", ui-sans-serif, system-ui, sans-serif !important;
  line-height: 1.75;
}
[class*="gradio-container"] .gr-markdown a,
[class*="gradio-container"] .prose a { color: var(--q-accent) !important; text-decoration: underline; text-decoration-color: rgba(190,91,43,0.35); }
[class*="gradio-container"] .gr-markdown a:hover,
[class*="gradio-container"] .prose a:hover { text-decoration-color: var(--q-accent); }
[class*="gradio-container"] .gr-markdown h1,
[class*="gradio-container"] .gr-markdown h2,
[class*="gradio-container"] .gr-markdown h3,
[class*="gradio-container"] .prose h1,
[class*="gradio-container"] .prose h2,
[class*="gradio-container"] .prose h3 {
  font-family: "Source Serif 4", "Source Serif Pro", ui-serif, Georgia, serif !important;
  font-weight: 600 !important;
  letter-spacing: -0.01em !important;
  color: var(--q-text) !important;
}
[class*="gradio-container"] .gr-markdown code,
[class*="gradio-container"] .prose code {
  background: var(--q-surface-alt);
  border: 1px solid var(--q-line);
  padding: 1px 6px;
  border-radius: 6px;
  font-size: 0.9em;
}

/* === Code block (Record tab) ============================================ */
[class*="gradio-container"] .codemirror-wrapper,
[class*="gradio-container"] .cm-editor,
[class*="gradio-container"] .cm-scroller,
[class*="gradio-container"] .cm-gutters,
[class*="gradio-container"] .cm-content {
  background: var(--q-surface-alt) !important;
  color: var(--q-text) !important;
  border: none !important;
  font-family: "JetBrains Mono", ui-monospace, monospace !important;
}
[class*="gradio-container"] .cm-gutters {
  border-right: 1px solid var(--q-line) !important;
  color: var(--q-muted) !important;
}

/* === Rounded corners on everything ====================================== */
[class*="gradio-container"] .block,
[class*="gradio-container"] .form,
[class*="gradio-container"] .gr-box,
[class*="gradio-container"] .gr-panel,
[class*="gradio-container"] .gr-group,
[class*="gradio-container"] [data-testid="textbox"],
[class*="gradio-container"] [data-testid="dropdown"],
[class*="gradio-container"] .tabitem,
[class*="gradio-container"] .tab-content,
[class*="gradio-container"] .gradio-markdown,
[class*="gradio-container"] .gradio-code { border-radius: var(--q-radius-md) !important; }
[class*="gradio-container"] button { border-radius: 999px !important; }

/* === Example buttons ==================================================== */
.example-note { color: var(--q-muted); font-size: 13px; margin: 0 0 12px 0; line-height: 1.5; }
.memory-help {
  color: var(--q-muted);
  font-size: 12.5px;
  line-height: 1.55;
  margin: 6px 0 0 0;
  padding: 10px 12px;
  background: var(--q-surface-alt);
  border: 1px solid var(--q-line);
  border-radius: 8px;
}
.memory-help b { color: var(--q-text); font-weight: 600; }
.example-buttons { display: grid; gap: 10px; margin-top: 4px; }

[class*="gradio-container"] .example-btn {
  text-align: left !important;
  justify-content: flex-start !important;
  white-space: normal !important;
  line-height: 1.5 !important;
  padding: 14px 16px !important;
  font-size: 14px !important;
  color: var(--q-text) !important;
  background: var(--q-paper) !important;
  border: 1px solid var(--q-line) !important;
  border-radius: var(--q-radius-md) !important;
  box-shadow: none !important;
  font-weight: 500 !important;
  letter-spacing: normal !important;
  text-transform: none !important;
}
[class*="gradio-container"] .example-btn:hover {
  background: var(--q-accent-soft) !important;
  border-color: var(--q-accent-line) !important;
  color: var(--q-accent) !important;
}
[class*="gradio-container"] .example-btn > * {
  color: inherit !important;
  white-space: normal !important;
  display: inline !important;
}

/* Footer tagline block */
.quest-footer {
  margin-top: 28px;
  padding: 18px 24px;
  border: 1px solid var(--q-line);
  border-radius: var(--q-radius-xl);
  background: var(--q-paper);
  box-shadow: var(--q-shadow-card);
  display: flex;
  align-items: center;
  justify-content: space-between;
  gap: 20px;
  color: var(--q-muted);
  font-size: 0.86rem;
  line-height: 1.65;
}
.quest-footer a { color: var(--q-muted); text-decoration: none; }
.quest-footer a:hover { color: var(--q-text); }
.quest-footer-links { display: flex; gap: 16px; flex-wrap: wrap; }

/* Tiny mark that replaces the HF watermark block. */
footer { display: none !important; }

/* === Responsive ========================================================= */
@media (max-width: 1100px) {
  .quest-cover-inner { grid-template-columns: 1fr; }
  .quest-cover-panel.wide { grid-column: auto; min-height: 180px; }
}
@media (max-width: 760px) {
  [class*="gradio-container"] { padding: 16px !important; }
  .quest-footer { flex-direction: column; align-items: flex-start; }
}
"""


@dataclass
class AgentState:
    searched_queries: List[str] = field(default_factory=list)
    visited_urls: List[str] = field(default_factory=list)
    searched_query_set: Set[str] = field(default_factory=set)
    visited_url_set: Set[str] = field(default_factory=set)
    trusted_notes: List[str] = field(default_factory=list)
    trace: List[Dict[str, Any]] = field(default_factory=list)


# Accept a variety of placeholder-only answers: a bare ellipsis (ASCII `...`
# or unicode `…`), a single interpunct, and any whitespace-only content. These
# show up when the model echoes a literal `<answer>...</answer>` template
# from the prompt instead of producing a real answer.
_PLACEHOLDER_ANSWER_RE = re.compile(r"^[\s.\u2026\u00b7]*$")

# Pipe-table separator line, e.g. `| --- | :---: |`. The outer pipes are
# optional in some GFM dialects, so we accept both.
_TABLE_SEPARATOR_RE = re.compile(
    r"^\s*\|?\s*:?-{2,}:?(?:\s*\|\s*:?-{2,}:?)+\s*\|?\s*$"
)


def strip_think_blocks(text: str) -> str:
    """Remove any <think>...</think> reasoning blocks.

    QUEST-35B (Qwen3 family) emits `<think>` reasoning before the final
    answer. When the endpoint is deployed without a reasoning parser, the raw
    tags leak into chat completion `content`; stripping them here keeps the
    extracted answer clean for Markdown rendering.
    """
    return re.sub(
        r"<think>.*?</think>", "", text, flags=re.DOTALL | re.IGNORECASE
    )


def decode_escaped_whitespace(text: str) -> str:
    """Decode literal `\\n`/`\\t`/`\\r` sequences back to real whitespace.

    Some OpenAI-compatible servers (and some vLLM builds when a tokenizer's
    chat template escapes control characters) return `choices[0].message.content`
    with newlines stored as the two-character backslash+n sequence rather than
    as a real newline. That breaks Markdown rendering because a pipe table on
    a single line is not a table — it is just a sentence with `|` in it, which
    is exactly the symptom we saw with:

        \\n| Color | Hex |\\n|---|---|\\n| Red | #FF0000 |...

    We only decode when the escapes dominate (at least 3 of them, and at
    least as many as the real newlines in the text). That keeps us from
    corrupting legitimate backslash-n pairs that happen to appear in a code
    sample the model produced.
    """
    if not text:
        return text
    escaped_newlines = text.count("\\n")
    if escaped_newlines == 0 and "\\t" not in text and "\\r" not in text:
        return text
    real_newlines = text.count("\n")
    if escaped_newlines < max(3, real_newlines + 1):
        return text
    # Preserve real backslashes so that `\\\\n` (an actual `\n` the model
    # wrote) doesn't get collapsed to a newline.
    sentinel = "\x00__BS__\x00"
    out = text.replace("\\\\", sentinel)
    out = out.replace("\\n", "\n").replace("\\r", "\r").replace("\\t", "\t")
    out = out.replace(sentinel, "\\")
    return out


def _is_placeholder_answer(text: str) -> bool:
    return bool(_PLACEHOLDER_ANSWER_RE.match(text or ""))


def ensure_markdown_table_blank_lines(text: str) -> str:
    """Insert a blank line before any pipe-table header row.

    GitHub-Flavored Markdown requires a pipe table to be preceded by a
    paragraph break; otherwise the header row is folded into the previous
    paragraph and the whole table renders as raw text. Models sometimes glue
    the table directly under a sentence (e.g. "Here's the comparison: | Col
    ..."), so we fix that up defensively.
    """
    lines = text.split("\n")
    out: List[str] = []
    for idx, line in enumerate(lines):
        is_header = (
            "|" in line
            and idx + 1 < len(lines)
            and _TABLE_SEPARATOR_RE.match(lines[idx + 1]) is not None
        )
        if is_header and out and out[-1].strip() != "":
            out.append("")
        out.append(line)
    return "\n".join(out)


def extract_answer(text: str) -> Optional[str]:
    """Return the content of the first `<answer>...</answer>` block.

    Tries two strategies, in order, and discards placeholder-only content
    (bare ellipses) that the model sometimes echoes from the prompt:

    1. Well-formed `<answer>...</answer>` block.
    2. Truncated `<answer>...` with no closing tag (tokens ran out);
       in that case we take everything after the opening tag.
    """
    # Decode escaped whitespace on the whole output first so the <answer>
    # regex can actually match the opening and closing tags across lines.
    decoded = decode_escaped_whitespace(text or "")
    cleaned = strip_think_blocks(decoded)

    full_match = re.search(
        r"<answer>\s*(.*?)\s*</answer>",
        cleaned,
        flags=re.DOTALL | re.IGNORECASE,
    )
    if full_match is not None:
        candidate = decode_escaped_whitespace(full_match.group(1).strip())
        if candidate and not _is_placeholder_answer(candidate):
            return candidate
        # Closed block was a placeholder / empty: fail fast. Do NOT fall
        # through to the open-ended strategy, or it would re-match the same
        # tag and incorrectly capture `...</answer>` as the answer.
        return None

    open_match = re.search(
        r"<answer>\s*(.*)$", cleaned, flags=re.DOTALL | re.IGNORECASE
    )
    if open_match is not None:
        candidate = decode_escaped_whitespace(open_match.group(1).strip())
        if candidate and not _is_placeholder_answer(candidate):
            return candidate

    return None


def parse_tool_call(text: str) -> Tuple[Optional[str], Optional[Dict[str, Any]], Optional[str]]:
    cleaned = strip_think_blocks(text or "")
    match = re.search(r"<tool_call>\s*(.*?)\s*</tool_call>", cleaned, flags=re.DOTALL | re.IGNORECASE)
    if not match:
        return None, None, None
    payload = match.group(1).strip()
    try:
        data = json.loads(payload)
    except json.JSONDecodeError:
        return None, None, "Invalid JSON in <tool_call> block."

    name = data.get("name")
    arguments = data.get("arguments", {})
    if not isinstance(name, str) or not isinstance(arguments, dict):
        return None, None, "Invalid tool format. Expect name(str) and arguments(dict)."
    return name, arguments, None


_SEARCH_UNAVAILABLE_HINT = (
    "The web-search backend is currently rate-limited or unreachable. "
    "If this question can be answered confidently from your own training "
    "knowledge (e.g. common product specs, historical facts, definitions), "
    "please produce your best answer now inside <answer>...</answer>, and "
    "mention any value that might be out of date. Only ask the user to "
    "retry later if the question truly requires a fresh web lookup."
)

# Google Serper API key. Either SERPER_API_KEY or SERPER_KEY_ID is accepted
# so that the Space matches the env-var name used by the research repo.
SERPER_API_KEY = (
    os.getenv("SERPER_API_KEY") or os.getenv("SERPER_KEY_ID") or ""
).strip()
SERPER_ENDPOINT = os.getenv("SERPER_ENDPOINT", "https://google.serper.dev/search")


def _serper_search(query: str, max_results: int) -> Dict[str, Any]:
    """Hit the Google Serper API. Returns the same shape as `_ddg_search`.

    Serper responds in well under a second and is not subject to the 202
    Ratelimit we get from html.duckduckgo.com, so preferring it when the
    key is set cuts latency dramatically and eliminates most search
    failures on shared Space IPs.
    """
    try:
        resp = requests.post(
            SERPER_ENDPOINT,
            headers={
                "X-API-KEY": SERPER_API_KEY,
                "Content-Type": "application/json",
            },
            json={"q": query, "num": max_results},
            timeout=15,
        )
        resp.raise_for_status()
        data = resp.json()
    except Exception as exc:
        return {
            "ok": False,
            "query": query,
            "error": f"Serper error: {type(exc).__name__}: {exc}",
            "results": [],
            "backend": "serper",
        }

    rows: List[Dict[str, str]] = []
    for item in (data.get("organic") or [])[:max_results]:
        rows.append(
            {
                "title": item.get("title", ""),
                "href": item.get("link", ""),
                "body": item.get("snippet", ""),
            }
        )
    # Fold in the answer box and knowledge graph when present; these often
    # carry the exact fact the model is looking for in a compact form.
    answer_box = data.get("answerBox") or {}
    if answer_box:
        rows.insert(
            0,
            {
                "title": answer_box.get("title", "Answer box"),
                "href": answer_box.get("link", ""),
                "body": answer_box.get("snippet")
                or answer_box.get("answer")
                or "",
            },
        )
    if not rows:
        return {
            "ok": False,
            "query": query,
            "error": "Serper returned no organic results",
            "results": [],
            "backend": "serper",
        }
    return {
        "ok": True,
        "query": query,
        "results": rows,
        "cached": False,
        "backend": "serper",
    }


def _ddg_search(query: str, max_results: int) -> Dict[str, Any]:
    """Fallback path: scrape DuckDuckGo. Rate-limits on shared IPs."""
    last_exc: Optional[BaseException] = None
    for attempt in range(2):
        try:
            rows: List[Dict[str, str]] = []
            with DDGS() as ddgs:
                for item in ddgs.text(query, max_results=max_results):
                    rows.append(
                        {
                            "title": item.get("title", ""),
                            "href": item.get("href", ""),
                            "body": item.get("body", ""),
                        }
                    )
            return {
                "ok": True,
                "query": query,
                "results": rows,
                "cached": False,
                "backend": "duckduckgo",
            }
        except Exception as exc:
            last_exc = exc
            if attempt == 0:
                time.sleep(1.5)
                continue

    err = f"{type(last_exc).__name__}: {last_exc}" if last_exc else "unknown error"
    return {
        "ok": False,
        "query": query,
        "error": f"DuckDuckGo unavailable ({err}).",
        "results": [],
        "backend": "duckduckgo",
    }


def _run_search_single(query: str, max_results: int) -> Dict[str, Any]:
    """Run one search query, preferring Serper when the key is set.

    Returns a structured dict on both success and failure; never raises.
    Order of preference:

    1. Google Serper (fast, no scraping, requires `SERPER_API_KEY` /
       `SERPER_KEY_ID`).
    2. DuckDuckGo HTML backend (free, but rate-limits on shared Space IPs).
    3. Graceful `ok: False` payload with a hint that tells the agent to
       answer from its own knowledge if it reasonably can.
    """
    if not query.strip():
        return {"ok": False, "error": "Search query cannot be empty."}
    cache_key = f"{query.strip().lower()}::{max_results}"
    if cache_key in SEARCH_CACHE:
        return {**SEARCH_CACHE[cache_key], "cached": True}

    tried: List[Dict[str, Any]] = []
    if SERPER_API_KEY:
        serper_result = _serper_search(query, max_results)
        if serper_result.get("ok"):
            SEARCH_CACHE[cache_key] = serper_result
            return serper_result
        tried.append(serper_result)

    ddg_result = _ddg_search(query, max_results)
    if ddg_result.get("ok"):
        SEARCH_CACHE[cache_key] = ddg_result
        return ddg_result
    tried.append(ddg_result)

    # Both backends failed (or no Serper key and DDG rate-limited).
    errors = "; ".join(
        f"{r.get('backend', 'unknown')}: {r.get('error', 'no results')}"
        for r in tried
    )
    return {
        "ok": False,
        "query": query,
        "error": f"All search backends failed ({errors}).",
        "results": [],
        "hint": _SEARCH_UNAVAILABLE_HINT,
    }


def run_search(query: Union[str, List[str]], max_results: int = 5) -> Dict[str, Any]:
    """Runs one or more queries through DuckDuckGo.

    QUEST's schema passes `query` as an array of strings, while the simpler
    starter schema used a single string. We accept both shapes.
    """
    if isinstance(query, list):
        sub_results: List[Dict[str, Any]] = []
        for q in query:
            if not isinstance(q, str) or not q.strip():
                continue
            sub_results.append(_run_search_single(q, max_results))
        return {"ok": True, "queries": query, "results": sub_results}
    return _run_search_single(str(query or "").strip(), max_results)


def _clean_html_to_text(html: str, max_chars: int) -> str:
    soup = BeautifulSoup(html, "html.parser")
    for tag in soup(["script", "style", "noscript"]):
        tag.decompose()
    text = soup.get_text(separator=" ", strip=True)
    text = re.sub(r"\s+", " ", text)
    return text[:max_chars]


def _run_visit_single(url: str, max_chars: int, goal: str = "") -> Dict[str, Any]:
    if not url.strip():
        return {"ok": False, "error": "URL cannot be empty."}
    cache_key = f"{url.strip()}::{max_chars}"
    if cache_key in VISIT_CACHE:
        return {**VISIT_CACHE[cache_key], "cached": True, "goal": goal}
    try:
        resp = requests.get(
            url,
            timeout=20,
            headers={"User-Agent": "Mozilla/5.0 (compatible; DeepResearchSpace/1.0)"},
        )
        resp.raise_for_status()
        content_type = resp.headers.get("content-type", "")
        if "text/html" in content_type or "<html" in resp.text[:200].lower():
            text = _clean_html_to_text(resp.text, max_chars=max_chars)
        else:
            text = resp.text[:max_chars]
        payload = {"ok": True, "url": url, "content": text, "cached": False, "goal": goal}
        VISIT_CACHE[cache_key] = payload
        return payload
    except Exception as exc:
        return {"ok": False, "url": url, "error": str(exc), "goal": goal}


def run_visit(
    url: Union[str, List[str]],
    max_chars: int = 6000,
    goal: str = "",
) -> Dict[str, Any]:
    """Fetches one or more URLs. Accepts string or list (QUEST schema)."""
    if isinstance(url, list):
        sub_results: List[Dict[str, Any]] = []
        for u in url:
            if not isinstance(u, str) or not u.strip():
                continue
            sub_results.append(_run_visit_single(u, max_chars, goal))
        return {"ok": True, "goal": goal, "results": sub_results}
    return _run_visit_single(str(url or "").strip(), max_chars, goal)


def _build_client_for_model(model: str) -> Tuple[InferenceClient, str, List[str]]:
    """Returns (client, primary_model_id, fallback_model_ids).

    When the user picks the Quest model and QUEST_BASE_URL is configured, the
    InferenceClient is pointed at the dedicated endpoint; otherwise we hit the
    shared HF Inference API and let the starter fall back across free models.
    """
    token = os.getenv("HF_TOKEN")
    quest_timeout = int(os.getenv("QUEST_REQUEST_TIMEOUT", "600"))
    if model == QUEST_MODEL_ID and QUEST_BASE_URL:
        # Prefer a dedicated key for the self-hosted endpoint so the real HF
        # token never travels into vLLM / tunnel logs.
        endpoint_token = os.getenv("QUEST_API_KEY") or token
        client = InferenceClient(
            base_url=QUEST_BASE_URL,
            token=endpoint_token,
            timeout=quest_timeout,
        )
        return client, QUEST_ENDPOINT_MODEL, []
    client = InferenceClient(token=token, timeout=quest_timeout)
    return client, model, []


def call_model(
    client: InferenceClient,
    messages: List[Dict[str, str]],
    preferred_model: str,
    candidate_models: List[str],
    temperature: float,
    max_new_tokens: int,
) -> Tuple[str, str]:
    model_order: List[str] = []
    for m in [preferred_model] + candidate_models:
        if m and m not in model_order:
            model_order.append(m)

    last_error = None
    for model_name in model_order:
        try:
            completion = client.chat_completion(
                model=model_name,
                messages=messages,
                temperature=temperature,
                max_tokens=max_new_tokens,
            )
            return completion.choices[0].message.content or "", model_name
        except Exception as exc:
            last_error = exc
            continue
    raise RuntimeError(f"All model candidates failed. Last error: {last_error}")


def _render_progress(
    lines: List[str],
    used_model: str,
    question: str,
) -> str:
    """Render the in-progress status view that replaces the Markdown panel
    while the agent is still running, so the user is not staring at a blank
    box for the 20-60 seconds a full QUEST-35B research run can take."""
    header = (
        f"### ⏳ Researching…\n\n"
        f"**Model:** `{used_model}`  \n"
        f"**Question:** {question.strip()[:200]}"
    )
    if not lines:
        body = "_Starting agent…_"
    else:
        body = "\n".join(f"- {line}" for line in lines)
    return f"{header}\n\n{body}"


def _trace_to_json(state: "AgentState", used_model: str) -> str:
    return json.dumps(
        {
            "used_model": used_model,
            "searched_queries": state.searched_queries,
            "visited_urls": state.visited_urls,
            "trusted_notes": state.trusted_notes[-10:],
            "trace": state.trace,
        },
        ensure_ascii=False,
        indent=2,
    )


MEMORY_STRATEGIES = ("condenser", "vanilla", "discard_all", "hide_tool_result")


def _normalize_memory_strategy(strategy: str) -> str:
    s = (strategy or "condenser").strip().lower().replace("-", "_")
    if s == "hide_tool_results":
        s = "hide_tool_result"
    return s if s in MEMORY_STRATEGIES else "condenser"


def _apply_memory_strategy(messages: List[Dict[str, str]], strategy: str, turn: int) -> None:
    """Lightweight port of the strategies defined in the Quest inference
    code (`inference/react_agent.py`). Upstream is token-threshold-driven;
    this Space approximates each strategy on a turn-count basis for demo
    purposes.

    - vanilla: no-op (matches MEMORY_ENABLED=false upstream).
    - condenser: no-op here; the main loop injects a compact research-state
      summary every few turns (a poor-man's stand-in for the upstream
      State Summarizer LLM that emits a structured trusted/untrusted/
      uncertain JSON when the token threshold is hit).
    - discard_all: every 8 turns, reset history to [system, user question]
      (upstream resets when token_count crosses the threshold).
    - hide_tool_result: keep only the most recent tool-response user
      message; older ones get their content replaced with a stub
      (mirrors upstream behavior).
    """
    if strategy == "discard_all":
        if turn > 1 and turn % 8 == 0 and len(messages) > 2:
            system_msg = messages[0]
            question_msg = messages[1]
            messages.clear()
            messages.append(system_msg)
            messages.append(question_msg)
            messages.append(
                {
                    "role": "user",
                    "content": "[memory discarded at turn "
                    f"{turn} — continue the research from the original question]",
                }
            )
    elif strategy == "hide_tool_result":
        keep_tail = 1
        tool_indices = [
            i for i, m in enumerate(messages)
            if m.get("role") == "user" and str(m.get("content", "")).startswith("<tool_response>")
        ]
        if len(tool_indices) > keep_tail:
            for i in tool_indices[:-keep_tail]:
                if messages[i]["content"] != "<tool_response>[hidden]</tool_response>":
                    messages[i] = {
                        "role": "user",
                        "content": "<tool_response>[hidden]</tool_response>",
                    }


def build_research_agent(
    question: str,
    model: str,
    max_turns: int,
    temperature: float,
    memory_strategy: str = "condenser",
):
    """Run the ReAct research loop as a generator.

    Each `yield` emits a `(markdown_for_answer_panel, json_for_record_panel)`
    tuple. Intermediate yields show progress so that Gradio streams the
    status lines into the UI as work happens. The last yield contains the
    final answer and the final trace.
    """
    client, primary_model, fallback_models = _build_client_for_model(model)
    # Display label: the real HF repo id is nicer than the TGI shim name.
    display_primary = model if (model == QUEST_MODEL_ID) else primary_model
    state = AgentState()
    used_model = display_primary
    status_lines: List[str] = []

    def _emit():
        """Yield the current progress snapshot to Gradio."""
        return (
            _render_progress(status_lines, used_model, question),
            _trace_to_json(state, used_model),
        )

    messages: List[Dict[str, str]] = [
        {"role": "system", "content": build_system_prompt()},
        {"role": "user", "content": question},
    ]

    final_answer: Optional[str] = None

    status_lines.append("🚀 Starting research agent")
    yield _emit()

    strategy = _normalize_memory_strategy(memory_strategy)
    os.environ["MEMORY_STRATEGY"] = strategy

    for turn in range(1, max_turns + 1):
        _apply_memory_strategy(messages, strategy, turn)
        if strategy == "condenser" and state.trusted_notes and turn > 1 and turn % 3 == 0:
            summary_lines = "\n".join(f"- {n}" for n in state.trusted_notes[-6:])
            messages.append(
                {
                    "role": "user",
                    "content": f"RESEARCH STATE SUMMARY\n{summary_lines}\nUse this summary to avoid repeating work.",
                }
            )

        status_lines.append(f"🧠 turn {turn}: thinking…")
        yield _emit()

        t0 = time.time()
        raw_output, endpoint_model = call_model(
            client=client,
            messages=messages,
            preferred_model=primary_model,
            candidate_models=fallback_models,
            temperature=temperature,
            max_new_tokens=int(os.getenv("QUEST_MAX_NEW_TOKENS", "4096")),
        )
        dt = time.time() - t0
        model_output = raw_output
        # Preserve the human-friendly model id for the trace even if the
        # endpoint ignores the "model" param and returns the TGI shim name.
        used_model = display_primary if endpoint_model == primary_model == QUEST_ENDPOINT_MODEL else endpoint_model
        messages.append({"role": "assistant", "content": model_output})
        state.trace.append({"turn": turn, "assistant": model_output, "elapsed_s": round(dt, 2)})
        status_lines[-1] = f"🧠 turn {turn}: model reply in {dt:.1f}s"
        yield _emit()

        extracted_answer = extract_answer(model_output)
        if extracted_answer:
            final_answer = extracted_answer
            status_lines.append("✍️ writing final answer")
            yield _emit()
            break

        tool_name, tool_args, tool_err = parse_tool_call(model_output)
        if tool_err:
            tool_response = {"ok": False, "error": tool_err}
            status_lines.append(f"⚠️ turn {turn}: malformed tool call — {tool_err}")
            yield _emit()
        elif not tool_name:
            # No explicit tool call and no final answer: force finalization.
            # IMPORTANT: do not write the literal characters `<answer>...</answer>`
            # here. Some models (notably the Qwen3 family that QUEST-35B is
            # built on) will echo the template verbatim, which means the
            # extracted answer ends up being the three-dot placeholder `...`
            # and the user sees an empty-looking result.
            messages.append(
                {
                    "role": "user",
                    "content": (
                        "You did not call a tool and did not produce a final "
                        "answer. Please now write your best final answer, "
                        "wrapped between an opening <answer> tag and a "
                        "closing </answer> tag. Put the real answer text "
                        "between those tags; do not write a literal ellipsis "
                        "or other placeholder. If the question asks for "
                        "tabular data, use GitHub-Flavored Markdown pipe "
                        "tables (`| col1 | col2 |` + `|---|---|`) and put a "
                        "blank line before the first row so the table renders."
                    ),
                }
            )
            status_lines.append(f"🙃 turn {turn}: model stalled; asking for an answer")
            yield _emit()
            continue
        else:
            if tool_name == "search":
                raw_query = tool_args.get("query", "")
                queries: List[str]
                if isinstance(raw_query, list):
                    queries = [str(q).strip() for q in raw_query if str(q).strip()]
                else:
                    queries = [str(raw_query).strip()] if str(raw_query).strip() else []
                max_results = int(tool_args.get("max_results", DEFAULT_MAX_SEARCH_RESULTS))
                max_results = max(1, min(max_results, DEFAULT_MAX_SEARCH_RESULTS))

                queries_preview = ", ".join(f"`{q}`" for q in queries) or "_(empty)_"
                status_lines.append(f"🔍 turn {turn}: searching {queries_preview}")
                yield _emit()

                per_query: List[Dict[str, Any]] = []
                backend_labels: List[str] = []
                hits_total = 0
                for q in queries:
                    if q in state.searched_query_set:
                        per_query.append({
                            "ok": True,
                            "query": q,
                            "cached": True,
                            "note": "Already searched; reusing cached result.",
                            "results": [],
                        })
                        backend_labels.append("cache")
                        continue
                    state.searched_queries.append(q)
                    state.searched_query_set.add(q)
                    single = _run_search_single(q, max_results)
                    per_query.append(single)
                    backend_labels.append(single.get("backend", "unknown"))
                    if single.get("ok"):
                        hits_total += len(single.get("results", []))
                        first_titles = [r.get("title", "") for r in single.get("results", [])[:2]]
                        if first_titles:
                            state.trusted_notes.append(
                                f"Searched '{q}' and found leads: {', '.join(t for t in first_titles if t)}"
                            )
                    else:
                        status_lines.append(
                            f"⚠️ search failed on `{q}` via {single.get('backend', 'unknown')}: "
                            f"{single.get('error', 'no results')}"
                        )
                tool_response = (
                    per_query[0]
                    if len(per_query) == 1
                    else {"ok": True, "queries": queries, "results": per_query}
                )
                unique_backends = sorted(set(backend_labels))
                backend_str = "/".join(unique_backends) if unique_backends else "?"
                status_lines.append(
                    f"✅ turn {turn}: got {hits_total} hit(s) via {backend_str}"
                )
                yield _emit()
            elif tool_name == "visit":
                raw_url = tool_args.get("url", "")
                urls: List[str]
                if isinstance(raw_url, list):
                    urls = [str(u).strip() for u in raw_url if str(u).strip()]
                else:
                    urls = [str(raw_url).strip()] if str(raw_url).strip() else []
                goal = str(tool_args.get("goal", "")).strip()
                max_chars = int(tool_args.get("max_chars", 6000))
                max_chars = max(500, min(max_chars, 20000))

                urls_preview = ", ".join(f"`{u[:60]}`" for u in urls) or "_(empty)_"
                status_lines.append(f"🌐 turn {turn}: visiting {urls_preview}")
                yield _emit()

                per_url: List[Dict[str, Any]] = []
                visit_ok = 0
                for u in urls:
                    if u in state.visited_url_set:
                        per_url.append({
                            "ok": True,
                            "url": u,
                            "cached": True,
                            "note": "Already visited; reusing cached result.",
                        })
                        visit_ok += 1
                        continue
                    state.visited_urls.append(u)
                    state.visited_url_set.add(u)
                    single = _run_visit_single(u, max_chars, goal)
                    per_url.append(single)
                    if single.get("ok"):
                        visit_ok += 1
                        snippet = str(single.get("content", ""))[:180]
                        if snippet:
                            state.trusted_notes.append(
                                f"Visited {u} and extracted key context: {snippet}"
                            )
                tool_response = (
                    per_url[0]
                    if len(per_url) == 1
                    else {"ok": True, "goal": goal, "results": per_url}
                )
                status_lines.append(
                    f"✅ turn {turn}: read {visit_ok}/{len(urls)} page(s)"
                )
                yield _emit()
            else:
                tool_response = {"ok": False, "error": f"Unknown tool: {tool_name}"}
                status_lines.append(f"⚠️ turn {turn}: unknown tool `{tool_name}`")
                yield _emit()

        state.trace.append({"turn": turn, "tool": tool_name, "tool_response": tool_response})
        messages.append(
            {
                "role": "user",
                "content": TOOL_RESPONSE_TEMPLATE.format(
                    payload=json.dumps(tool_response, ensure_ascii=False)
                ),
            }
        )

    if final_answer is None:
        final_answer = (
            "I could not finish a complete research answer within the configured turns. "
            "Try increasing max turns or switching to a stronger model."
        )
    else:
        final_answer = ensure_markdown_table_blank_lines(final_answer)

    citations = "\n".join(f"- {url}" for url in sorted(set(state.visited_urls)))
    final_answer = f"**Model used:** `{used_model}`\n\n{final_answer}"
    if citations:
        final_answer = f"{final_answer}\n\n### Visited Sources\n{citations}"

    trace_text = _trace_to_json(state, used_model)
    yield (final_answer, trace_text)


def run_ui(
    question: str,
    max_turns: int,
    memory_strategy: str,
    temperature: float,
):
    if not question.strip():
        yield "Please input a question.", "{}"
        return
    if not os.getenv("HF_TOKEN"):
        warning = (
            "HF_TOKEN is not configured in Space Secrets. "
            "Go to Settings -> Secrets -> add `HF_TOKEN`, then retry."
        )
        yield warning, json.dumps({"error": warning}, ensure_ascii=False, indent=2)
        return
    if not QUEST_BASE_URL:
        warning = (
            f"`{QUEST_MODEL_ID}` needs a private HF Inference Endpoint. "
            "Create one at https://ui.endpoints.huggingface.co/, then set "
            "`QUEST_BASE_URL` in Space Secrets to the endpoint's `/v1/` URL."
        )
        yield warning, json.dumps({"error": warning}, ensure_ascii=False, indent=2)
        return
    try:
        for partial_answer, partial_trace in build_research_agent(
            question=question,
            model=QUEST_MODEL_ID,
            max_turns=max_turns,
            temperature=temperature,
            memory_strategy=memory_strategy,
        ):
            yield partial_answer, partial_trace
    except Exception as exc:
        yield f"Error: {exc}", json.dumps({"error": str(exc)}, ensure_ascii=False, indent=2)


EXAMPLES = [
    {
        "category": "Multi-hop facts",
        "icon": "🎯",
        "text": "Who was the first person to walk on the Moon, and which U.S. President set that goal in his famous 1962 “Moon speech”?",
    },
    {
        "category": "Time-varying + multi-hop",
        "icon": "📈",
        "text": "Who is the current CEO of the company that acquired GitHub in 2018, and what was that company's market capitalization at the close of the most recent quarter?",
    },
    {
        "category": "Multi-constraint",
        "icon": "🧩",
        "text": "Find a 2-day itinerary in Tokyo under $250 focused on contemporary art museums and vegetarian restaurants, including transit between sites.",
    },
    {
        "category": "Research Report",
        "icon": "📚",
        "text": "Compare the LLM-safety research approaches of Anthropic, OpenAI, and Google DeepMind over the past 18 months, focusing on alignment techniques and red-teaming methodologies.",
    },
]


def _example_label(ex: Dict[str, str]) -> str:
    return f"{ex['icon']}  {ex['category']} — {ex['text']}"


with gr.Blocks(
    title="QUEST · Deep Research by OSU NLP",
    theme=APP_THEME,
    css=CUSTOM_CSS,
    fill_width=True,
) as demo:
    # --- Quest-style header (Q mark + title + byline) ---
    gr.HTML(
        """
<header class="quest-header">
  <div class="quest-header-text">
    <h1 class="quest-header-title"><span class="quest-name">QUEST</span>: A Fully Open Recipe for Training Deep Research Agents from Scratch</h1>
    <a class="quest-header-byline" href="https://x.com/osunlp" target="_blank" rel="noopener noreferrer">Built by OSU NLP Group</a>
  </div>
</header>
"""
    )

    # --- Main two-column layout ---
    with gr.Row(elem_classes="layout-gap"):
        with gr.Column(scale=6, min_width=420):
            with gr.Group(elem_classes="section-card"):
                gr.HTML(
                    '<div class="section-heading">Ask the agent</div>'
                    '<div class="hero-heading"><span class="quest-name">QUEST</span>: What I can research for you?</div>'
                )
                question = gr.Textbox(
                    show_label=False,
                    placeholder="Ask anything you want to research in depth...",
                    lines=6,
                )
                with gr.Row(elem_classes="action-row"):
                    run_btn = gr.Button("Run Research", variant="primary", size="lg")
                    stop_btn = gr.Button("Stop", variant="stop", size="lg")
                    clear_btn = gr.Button("Clear", variant="secondary", size="lg")

            with gr.Group(elem_classes="section-card"):
                gr.HTML(
                    '<div class="section-heading">Try examples</div>'
                    '<div class="example-note"><span class="quest-name">QUEST</span> can handle multiple types of queries as shown below.</div>'
                )
                with gr.Column(elem_classes="example-buttons"):
                    example_buttons = [
                        gr.Button(_example_label(ex), variant="secondary", elem_classes="example-btn")
                        for ex in EXAMPLES
                    ]

            with gr.Group(elem_classes="section-card"):
                gr.HTML('<div class="section-heading">Output</div>')
                with gr.Tabs():
                    with gr.TabItem("Result"):
                        answer = gr.Markdown(label="Final Answer")
                    with gr.TabItem("Record"):
                        trace = gr.Code(label="Execution Trace (JSON)", language="json")

        with gr.Column(scale=4, min_width=340, elem_classes="right-stack"):
            with gr.Group(elem_classes="section-card"):
                gr.HTML(
                    f"""
<div class="section-heading">Open release</div>
<div class="resource-grid">
  <a class="resource-card" href="{PAPER_URL}" target="_blank" rel="noopener noreferrer">
    <span class="resource-card-icon" aria-hidden="true">
      <svg viewBox="0 0 24 24" role="img" focusable="false"><path d="M6 2.5h8.2L19 7.3v14.2H6V2.5Zm8 1.9v3.2h3.2L14 4.4ZM8.1 9.8h8.8V8.4H8.1v1.4Zm0 3.3h8.8v-1.4H8.1v1.4Zm0 3.3h6.4V15H8.1v1.4Z"/></svg>
    </span>
    <span class="resource-card-text"><strong>Paper</strong><small>Blog</small></span>
  </a>
  <a class="resource-card" href="{CODE_URL}" target="_blank" rel="noopener noreferrer">
    <span class="resource-card-icon" aria-hidden="true">
      <svg viewBox="0 0 24 24" role="img" focusable="false"><path d="M12 1.8c-5.7 0-10.3 4.6-10.3 10.3 0 4.6 3 8.5 7.1 9.8.5.1.7-.2.7-.5v-1.8c-2.9.6-3.5-1.2-3.5-1.2-.5-1.2-1.1-1.5-1.1-1.5-.9-.6.1-.6.1-.6 1 .1 1.6 1.1 1.6 1.1.9 1.6 2.4 1.1 3 .8.1-.7.4-1.1.7-1.3-2.3-.3-4.7-1.2-4.7-5.1 0-1.1.4-2.1 1.1-2.8-.1-.3-.5-1.4.1-2.8 0 0 .9-.3 2.9 1.1.8-.2 1.7-.3 2.6-.3s1.8.1 2.6.3c2-1.4 2.9-1.1 2.9-1.1.6 1.4.2 2.5.1 2.8.7.8 1.1 1.7 1.1 2.8 0 4-2.4 4.8-4.7 5.1.4.3.7 1 .7 2v2.9c0 .3.2.6.7.5 4.1-1.4 7.1-5.2 7.1-9.8C22.3 6.4 17.7 1.8 12 1.8Z"/></svg>
    </span>
    <span class="resource-card-text"><strong>Code</strong><small>GitHub</small></span>
  </a>
  <a class="resource-card" href="{DATASET_URL}" target="_blank" rel="noopener noreferrer">
    <span class="resource-card-icon resource-card-emoji" aria-hidden="true">🤗</span>
    <span class="resource-card-text"><strong>Data</strong><small>Collection</small></span>
  </a>
  <a class="resource-card" href="{MODEL_URL}" target="_blank" rel="noopener noreferrer">
    <span class="resource-card-icon resource-card-emoji" aria-hidden="true">🤗</span>
    <span class="resource-card-text"><strong>Model</strong><small>QUEST-35B-RL</small></span>
  </a>
</div>
"""
                )

            with gr.Group(elem_classes="section-card"):
                gr.HTML('<div class="section-heading">Settings</div>')
                gr.Textbox(
                    label="Model",
                    value=QUEST_MODEL_ID,
                    interactive=False,
                    elem_id="quest-model",
                )
                memory_strategy = gr.Radio(
                    label="Memory Strategy",
                    choices=[
                        ("Condenser (default)", "condenser"),
                        ("Vanilla", "vanilla"),
                        ("Discard-all", "discard_all"),
                        ("Hide-tool-result", "hide_tool_result"),
                    ],
                    value="condenser",
                    elem_id="quest-memory-strategy",
                )
                gr.HTML(
                    '<div class="memory-help">'
                    '<b>Condenser</b> (default) — when context grows large, a State Summarizer LLM compresses earlier turns into a structured JSON of trusted/untrusted/uncertain claims, visited sources, and prior search queries; the agent continues with that compact state.<br>'
                    '<b>Vanilla</b> — memory management disabled; the full conversation history is kept.<br>'
                    '<b>Discard-all</b> — when context grows large, the entire message history is reset, restarting the agent from the original question with no accumulated context.<br>'
                    '<b>Hide-tool-result</b> — when context grows large, older tool responses are pruned; only the most recent tool result is kept.'
                    '</div>'
                )
                max_turns = gr.Slider(
                    label="Max Turns",
                    minimum=2,
                    maximum=50,
                    value=6,
                    step=1,
                    elem_id="quest-max-turns",
                )
                temperature = gr.Slider(
                    label="Temperature",
                    minimum=0.0,
                    maximum=1.5,
                    value=1.0,
                    step=0.1,
                    elem_id="quest-temperature",
                )

    gr.HTML(
        """
<footer class="quest-footer">
  <p>QUEST is a fully open recipe for training deep research agents from scratch &mdash; covering data synthesis, memory management, infrastructure, and long-horizon training.</p>
  <div class="quest-footer-links">
    <a href="https://nlp.osu.edu/" target="_blank" rel="noopener noreferrer">OSU NLP</a>
    <a href="https://huggingface.co/osunlp" target="_blank" rel="noopener noreferrer">Hugging Face</a>
  </div>
</footer>
"""
    )

    run_event = run_btn.click(
        fn=run_ui,
        inputs=[question, max_turns, memory_strategy, temperature],
        outputs=[answer, trace],
    )
    for btn, ex in zip(example_buttons, EXAMPLES):
        btn.click(
            fn=(lambda text=ex["text"]: text),
            inputs=[],
            outputs=[question],
        )
    stop_btn.click(fn=None, cancels=[run_event])
    clear_btn.click(
        fn=lambda: ("", "", "{}"),
        inputs=[],
        outputs=[question, answer, trace],
    )


if __name__ == "__main__":
    demo.launch()