app.py

import os
import re
import sys
import time
import concurrent.futures
# Force UTF-8 output on Windows to avoid charmap crashes with Unicode characters
if sys.platform == "win32":
    sys.stdout.reconfigure(encoding="utf-8", errors="replace")
    sys.stderr.reconfigure(encoding="utf-8", errors="replace")
import gradio as gr
import requests
import pandas as pd
from typing import Literal, TypedDict, get_args
from langchain_core.messages import HumanMessage, SystemMessage
from langchain_openai import ChatOpenAI
from langgraph.graph import END, StateGraph
from config import DEFAULT_API_URL, HF_TOKEN, GROQ_API_KEY, OPENROUTER_API_KEY, get_prompt
from tools import (
    web_search,
    wikipedia_search,
    visit_webpage,
    get_youtube_transcript,
    describe_image,
    transcribe_audio,
    run_python_file,
    read_task_file,
)

# ---------------------------------------------------------------------------
# Model fallback chain (primary → backup → last-resort)
# ---------------------------------------------------------------------------
# Use OpenRouter for the main reasoning model (better quality) and Groq for routing (fast)
GROQ_MODELS = [
    {"model_id": "llama-3.3-70b-versatile"},
    {"model_id": "llama-3.1-8b-instant"},
]

OPENROUTER_MODELS = [
    {"model_id": "google/gemini-2.0-flash-001"},
    {"model_id": "qwen/qwen-2.5-72b-instruct"},
    {"model_id": "meta-llama/llama-3.3-70b-instruct"},
]

_LABELS = Literal[
    "python_script",
    "image",
    "audio",
    "other_ext",
    "youtube",
    "research",
    "logic"
]

def _download_task_file(task_id: str, api_url: str = DEFAULT_API_URL) -> tuple[bytes, str]:
    """Download a file attached to a GAIA task."""
    url = f"{api_url}/files/{task_id}"
    try:
        headers = {"Authorization": f"Bearer {HF_TOKEN}"}
        resp = requests.get(url, headers=headers, timeout=30)
    except requests.exceptions.RequestException as e:
        print(f"[DEBUG] Download error for {task_id}: {e}")
        return b"", ""
    if resp.status_code != 200:
        print(f"[DEBUG] GET {url} → {resp.status_code}")
        return b"", ""
    ctype = resp.headers.get("content-type", "").lower()
    print(f"[DEBUG] Downloaded file for {task_id}: {len(resp.content)} bytes, type={ctype}")
    return resp.content, ctype


def _extract_relevant_content(page_text: str, question: str, max_chars: int = 30000) -> str:
    """Extract the most question-relevant sections from a long page.

    Instead of blindly truncating at max_chars (which loses content deep in a page),
    this splits the page into sections, scores each by keyword overlap with the
    question, and returns the highest-scoring sections first — always including the
    intro for context.

    This is general-purpose: works for any question + any page by matching keywords.
    """
    if len(page_text) <= max_chars:
        return page_text

    # Build keyword set from the question (lowercase, words 3+ chars)
    q_words = set(w.lower() for w in re.findall(r'\b\w{3,}\b', question))

    # Split into sections by markdown headers (##, ###, etc.)
    section_pattern = re.compile(r'^(#{1,4}\s+.+)$', re.MULTILINE)
    splits = section_pattern.split(page_text)

    # Group into (header, body) pairs
    sections = []
    if splits[0].strip():
        sections.append(("INTRO", splits[0].strip()))
    i = 1
    while i < len(splits):
        header = splits[i].strip() if i < len(splits) else ""
        body = splits[i + 1].strip() if i + 1 < len(splits) else ""
        if header or body:
            sections.append((header, body))
        i += 2

    if not sections:
        return page_text[:max_chars]

    # Score each section by keyword overlap
    scored = []
    for idx, (header, body) in enumerate(sections):
        combined = (header + " " + body).lower()
        score = sum(1 for w in q_words if w in combined)
        # Boost sections whose headers contain question keywords
        header_score = sum(2 for w in q_words if w in header.lower())
        scored.append((idx, score + header_score, header, body))

    # Always include the intro section first
    result_parts = []
    used_chars = 0
    used_indices = set()

    # Add intro section (index 0) first if it exists
    if scored and scored[0][0] == 0:
        intro_text = scored[0][2] + "\n" + scored[0][3] if scored[0][2] != "INTRO" else scored[0][3]
        intro_truncated = intro_text[:max_chars // 3]  # Give intro at most 1/3 of space
        result_parts.append(intro_truncated)
        used_chars += len(intro_truncated)
        used_indices.add(0)

    # Sort remaining sections by relevance score (descending)
    remaining = [(idx, score, header, body) for idx, score, header, body in scored if idx not in used_indices]
    remaining.sort(key=lambda x: (-x[1], x[0]))  # highest score first, then original order for ties

    for idx, score, header, body in remaining:
        section_text = header + "\n" + body if header else body
        if used_chars + len(section_text) > max_chars:
            # Try to fit a truncated version if it has high relevance
            space_left = max_chars - used_chars
            if space_left > 500 and score > 0:
                result_parts.append(section_text[:space_left])
                used_chars += space_left
            break
        result_parts.append(section_text)
        used_chars += len(section_text)
        used_indices.add(idx)

    return "\n\n".join(result_parts)

class AgentState(TypedDict):
    question: str
    label: str
    context: str
    answer: str
    task_id: str | None
    file_name: str | None


MAX_WORKERS = 1        # sequential to stay within rate limits
QUESTION_TIMEOUT = 300  # seconds before a single question is abandoned
_exhausted_models: set[str] = set()

# --------------------------------------------------------------------------- #
#                         NODES  (LangGraph  functions)                        #
# --------------------------------------------------------------------------- #
# Router uses Groq (fast, cheap)
_llm_router = ChatOpenAI(
    model=GROQ_MODELS[0]["model_id"],
    base_url="https://api.groq.com/openai/v1",
    api_key=GROQ_API_KEY,
    timeout=60,
)

# Reasoning uses OpenRouter (higher quality)
_llm_answer = ChatOpenAI(
    model=OPENROUTER_MODELS[0]["model_id"],
    base_url="https://openrouter.ai/api/v1",
    api_key=OPENROUTER_API_KEY,
    timeout=120,
)

def route_question(state: AgentState) -> AgentState:
    """Label the task so we know which toolchain to invoke."""
    question = state["question"]

    label_values = set(get_args(_LABELS))
    prompt = get_prompt(
        prompt_key="router",
        question=question,
        labels=", ".join(repr(v) for v in label_values),
    )
    resp = _llm_router.invoke(prompt).content.strip().lower()
    state["label"] = resp if resp in label_values else "logic"
    return state

def call_tools(state: AgentState) -> AgentState:
    question, label, task_id = state["question"], state["label"], state["task_id"]
    file_name = state.get("file_name") or ""

    matched_obj = re.search(r"https?://\S+", question)

    # ---- attachment: try download when task has a file or label suggests one -----
    should_try_file = bool(task_id and file_name)
    if not should_try_file and task_id and label in ("python_script", "image", "audio", "other_ext"):
        should_try_file = True  # label says there's a file — try anyway

    if should_try_file:
        blob, ctype = _download_task_file(api_url=DEFAULT_API_URL, task_id=task_id)
        if blob:
            print(f"[DEBUG] attachment type={ctype}, size={len(blob)} bytes")
            if "python" in ctype or file_name.endswith(".py") or (label == "python_script" and "text" in ctype):
                print("[DEBUG] Working with a Python attachment file")
                state["answer"] = run_python_file.invoke({"code": blob.decode("utf-8", errors="replace")})
                state["label"] = "python_script"
                return state
            if "audio" in ctype or any(file_name.endswith(ext) for ext in [".mp3", ".wav", ".m4a", ".flac"]) or (label == "audio" and "octet" in ctype):
                print("[DEBUG] Working with an audio attachment file")
                state["context"] = transcribe_audio.invoke({"audio_bytes": blob})
                state["label"] = "audio"
                return state
            if "image" in ctype or any(file_name.endswith(ext) for ext in [".png", ".jpg", ".jpeg", ".gif", ".webp"]) or (label == "image" and "octet" in ctype):
                print("[DEBUG] Working with an image attachment file")
                state["answer"] = describe_image.invoke({"img_bytes": blob, "question": question})
                state["label"] = "image"
                return state
            # Excel / CSV / PDF / other binary
            print("[DEBUG] Working with a data file attachment")
            state["context"] = read_task_file.invoke({"xls_bytes": blob})
            state["label"] = "other_ext"
            return state

    # ---- label-based routing (when no file was fetched) ----------
    if label == "youtube":
        print("[TOOL] youtube_transcript")
        if matched_obj:
            url = re.sub(r'[.,;:!?")\]]+$', '', matched_obj.group(0))
            print(f"[TOOL] fetching transcript for: {url}")
            transcript = get_youtube_transcript.invoke({"video_url": url})
            if transcript and transcript != "TRANSCRIPT_UNAVAILABLE":
                state["context"] = transcript
            else:
                # Fallback: search web using the actual question (more informative than URL alone)
                print("[TOOL] Transcript unavailable — searching web for video content")
                search_json = web_search.invoke({"query": question[:150]})
                search_json2 = web_search.invoke({"query": f"youtube video {url}"})
                context_parts = [f"TRANSCRIPT_UNAVAILABLE for {url}."]
                if search_json and search_json != "No search results found.":
                    context_parts.append(f"Question-based search:\n{search_json}")
                    try:
                        import json as _json
                        for hit in _json.loads(search_json)[:2]:
                            link = hit.get("link", "")
                            if link and "youtube.com" not in link:
                                page_content = visit_webpage.invoke({"url": link})
                                if page_content and "Could not fetch" not in page_content:
                                    context_parts.append(f"Page ({link}):\n{_extract_relevant_content(page_content, question, 20000)}")
                    except Exception:
                        pass
                if search_json2 and search_json2 != "No search results found.":
                    context_parts.append(f"Video search:\n{search_json2}")
                state["context"] = "\n\n".join(context_parts)
        else:
            print("[TOOL] youtube label but no URL found — falling back to web search")
            state["context"] = web_search.invoke({"query": question})

    elif label in ("image", "audio", "python_script", "other_ext"):
        # File was expected but unavailable — fall back to web research
        print(f"[TOOL] File unavailable for '{label}' question — falling back to web search")
        search_json = web_search.invoke({"query": question[:150]})
        wiki_text = wikipedia_search.invoke({"query": question[:100]})
        context_parts = ["NOTE: The attached file for this question was not available. Answer based on web research."]
        if search_json and search_json != "No search results found.":
            context_parts.append(f"Web search:\n{search_json}")
            try:
                import json as _json
                hits = _json.loads(search_json)
                for hit in hits[:3]:
                    link = hit.get("link", "")
                    if link:
                        page_content = visit_webpage.invoke({"url": link})
                        if page_content and "Could not fetch" not in page_content:
                            context_parts.append(f"Page ({link}):\n{_extract_relevant_content(page_content, question, 20000)}")
            except Exception:
                pass
        if wiki_text and "No Wikipedia results found" not in wiki_text:
            context_parts.append(f"Wikipedia:\n{wiki_text}")
        state["context"] = "\n\n".join(context_parts)

    elif label == "research":
        print("[TOOL] research — multi-step search")
        import json as _json

        # Step 1: Generate TWO search queries from different angles
        search_query_prompt = (
            "Write TWO different search queries to answer this question, each on its own line.\n"
            "Query 1: A precise, specific query (max 15 words). MUST include ALL key proper nouns, dates, years, and numbers from the question.\n"
            "Query 2: A broader or alternative-angle query (max 15 words) approaching from a different angle.\n"
            "CRITICAL: Never drop dates, years, or specific identifiers from the question.\n"
            "Output ONLY the two queries, one per line, no numbering.\n\nQuestion: " + question
        )
        raw_queries = _llm_router.invoke(search_query_prompt).content.strip()
        query_lines = [q.strip().strip('"').strip("'").lstrip("0123456789.) ") for q in raw_queries.split("\n") if q.strip()]
        focused_query = query_lines[0] if query_lines else question[:80]
        alt_query = query_lines[1] if len(query_lines) > 1 else None
        print(f"[TOOL] search query 1: {focused_query}")
        if alt_query:
            print(f"[TOOL] search query 2: {alt_query}")

        # Step 2: Run web searches + Wikipedia
        search_json = web_search.invoke({"query": focused_query})
        wiki_text = wikipedia_search.invoke({"query": focused_query})

        context_parts = []

        # Step 3: Collect all search hits
        all_hits = []
        if search_json and search_json != "No search results found.":
            context_parts.append(f"WEB SEARCH RESULTS:\n{search_json}")
            try:
                all_hits.extend(_json.loads(search_json))
            except Exception:
                pass

        # Run alternative query search
        if alt_query:
            search_json2 = web_search.invoke({"query": alt_query})
            if search_json2 and search_json2 != "No search results found.":
                context_parts.append(f"\nALT SEARCH RESULTS:\n{search_json2}")
                try:
                    seen_links = {h.get("link", "") for h in all_hits}
                    for h in _json.loads(search_json2):
                        if h.get("link", "") not in seen_links:
                            all_hits.append(h)
                except Exception:
                    pass

        # Step 3b: Also search with raw question text (catches details LLM-generated queries miss)
        raw_q_search = web_search.invoke({"query": question[:150]})
        if raw_q_search and raw_q_search != "No search results found.":
            try:
                seen_links = {h.get("link", "") for h in all_hits}
                for h in _json.loads(raw_q_search):
                    if h.get("link", "") not in seen_links:
                        all_hits.append(h)
            except Exception:
                pass

        # Visit top pages (prefer Wikipedia links first, up to 4 pages)
        all_hits.sort(key=lambda h: (0 if "wikipedia.org" in h.get("link", "") else 1))
        visited_urls = set()
        visited = 0
        for hit in all_hits[:8]:
            link = hit.get("link", "")
            if link and visited < 4 and link not in visited_urls:
                visited_urls.add(link)
                page_content = visit_webpage.invoke({"url": link})
                if page_content and "Could not fetch" not in page_content:
                    context_parts.append(f"\nPAGE CONTENT ({link}):\n{_extract_relevant_content(page_content, question, 30000)}")
                    visited += 1

        if wiki_text and "No Wikipedia results found" not in wiki_text and "failed" not in wiki_text.lower():
            context_parts.append(f"\nWIKIPEDIA:\n{wiki_text}")

        # Step 4: For questions about discographies/albums, visit specific Wikipedia discography page
        q_lower = question.lower()
        if any(w in q_lower for w in ["album", "discography", "studio album", "published"]):
            # Extract artist name and try discography page
            artist_prompt = (
                "What is the name of the musical artist in this question? "
                "Output ONLY the artist name, nothing else.\n\nQuestion: " + question
            )
            artist_name = _llm_router.invoke(artist_prompt).content.strip().strip('"').replace(" ", "_")
            if artist_name and len(artist_name) > 2:
                disco_url = f"https://en.wikipedia.org/wiki/{artist_name}_discography"
                print(f"[TOOL] Trying Wikipedia discography page: {disco_url}")
                disco_content = visit_webpage.invoke({"url": disco_url})
                if disco_content and "Could not fetch" not in disco_content and "does not have an article" not in disco_content:
                    context_parts.append(f"\nWIKIPEDIA DISCOGRAPHY ({disco_url}):\n{_extract_relevant_content(disco_content, question, 40000)}")
                else:
                    # Try alternate format
                    disco_url2 = f"https://en.wikipedia.org/wiki/{artist_name}_albums_discography"
                    disco_content2 = visit_webpage.invoke({"url": disco_url2})
                    if disco_content2 and "Could not fetch" not in disco_content2:
                        context_parts.append(f"\nWIKIPEDIA DISCOGRAPHY ({disco_url2}):\n{disco_content2[:40000]}")

        # Step 5: For questions that mention Wikipedia specifically, construct direct wiki URLs
        if "wikipedia" in q_lower or "featured article" in q_lower:
            wiki_subject_prompt = (
                "What is the main Wikipedia article subject in this question? "
                "Output ONLY the article title (e.g. 'Psittacosaurus'), nothing else.\n\n"
                "Question: " + question
            )
            wiki_subject = _llm_router.invoke(wiki_subject_prompt).content.strip().strip('"').replace(" ", "_")
            fa_url = f"https://en.wikipedia.org/wiki/Wikipedia:Featured_article_candidates/{wiki_subject}"
            print(f"[TOOL] Trying Wikipedia FA page: {fa_url}")
            fa_content = visit_webpage.invoke({"url": fa_url})
            if fa_content and "Could not fetch" not in fa_content and "does not have an article" not in fa_content:
                context_parts.append(f"\nWIKIPEDIA FA CANDIDATES ({fa_url}):\n{_extract_relevant_content(fa_content, question, 25000)}")
            talk_url = f"https://en.wikipedia.org/wiki/Talk:{wiki_subject}"
            talk_content = visit_webpage.invoke({"url": talk_url})
            if talk_content and "Could not fetch" not in talk_content:
                context_parts.append(f"\nWIKIPEDIA TALK PAGE ({talk_url}):\n{_extract_relevant_content(talk_content, question, 15000)}")

        # Step 6: If results are still thin, try one more direct search
        if not context_parts or all("No " in p or "error" in p.lower() for p in context_parts):
            print("[TOOL] Initial search thin — trying direct question search")
            direct_results = web_search.invoke({"query": question[:120]})
            if direct_results and direct_results != "No search results found.":
                context_parts.append(f"\nDIRECT SEARCH:\n{direct_results}")

        state["context"] = "\n\n".join(context_parts) if context_parts else "No information found from web search or Wikipedia."

    else:
        # Logic / pure reasoning — no search needed
        print("[TOOL] reasoning only (no search)")
        state["context"] = ""
    return state


def _do_research(question: str, query: str | None = None) -> str:
    """Run a research search and return combined context string."""
    import json as _json
    if not query:
        query = question[:120]
    search_json = web_search.invoke({"query": query})
    context_parts = []
    if search_json and search_json != "No search results found.":
        context_parts.append(f"Search results:\n{search_json}")
        try:
            hits = _json.loads(search_json)
            # Prioritize Wikipedia results
            hits.sort(key=lambda h: (0 if "wikipedia.org" in h.get("link", "") else 1))
            visited = 0
            for hit in hits[:5]:
                link = hit.get("link", "")
                if link and visited < 3:
                    page_content = visit_webpage.invoke({"url": link})
                    if page_content and "Could not fetch" not in page_content:
                        context_parts.append(f"Page ({link}):\n{_extract_relevant_content(page_content, question, 25000)}")
                        visited += 1
        except Exception:
            pass
    return "\n\n".join(context_parts)

def synthesize_response(state: AgentState) -> AgentState:
    # If a tool produced a direct final answer (python execution), skip reasoning
    if state.get("answer") and state["label"] == "python_script":
        print(f"[SYNTHESIZE] skipped — python output: {state['answer'][:200]}")
        return state

    # For image: the vision model already answered, but wrap it through reasoning
    # to extract the precise answer from the description
    if state.get("answer") and state["label"] == "image":
        state["context"] = f"VISION MODEL OUTPUT:\n{state['answer']}"
        state["answer"] = ""  # clear so reasoning runs

    # For other_ext with context (file data), make sure reasoning runs
    if state["label"] == "other_ext" and state.get("context") and not state.get("answer"):
        pass  # context is set, reasoning will run below

    # Pass 1: chain-of-thought reasoning
    reasoning_prompt = [
        SystemMessage(content=get_prompt("reasoning_system")),
        HumanMessage(
            content=get_prompt(
                prompt_key="reasoning_user",
                question=state["question"],
                context=state["context"],
            )
        ),
    ]
    reasoning = _llm_answer.invoke(reasoning_prompt).content.strip()
    print(f"\n[REASONING]\n{reasoning}\n")

    # Try to extract FINAL ANSWER directly from reasoning text
    fa_match = re.search(r"FINAL ANSWER:\s*(.+)", reasoning, re.IGNORECASE)
    if fa_match:
        answer = fa_match.group(1).strip().split('\n')[0].strip()
    elif reasoning.strip():
        extract_prompt = [
            SystemMessage(content=get_prompt("extract_system")),
            HumanMessage(
                content=get_prompt(
                    prompt_key="extract_user",
                    reasoning=reasoning,
                )
            ),
        ]
        answer = _llm_answer.invoke(extract_prompt).content.strip()
    else:
        answer = "ERROR: no reasoning produced"

    # --- KNOWLEDGE GAP DETECTION: ask the model if its reasoning has gaps ---
    # This is general-purpose: works for any question type where the model had
    # to guess, assume, or lacked a specific definition/fact.
    _answer_bad = any(w in answer.lower() for w in ["cannot", "unable", "not determine", "no answer", "not possible"])
    _reasoning_uncertain = any(w in reasoning.lower() for w in [
        "i will assume", "i'm not sure", "i cannot confirm", "my best guess",
        "without more information", "i will make a guess", "i don't have",
        "not explicitly", "i cannot find", "i will guess", "i am not certain",
        "it is possible", "low confidence", "not enough", "i assume",
        "i'm guessing", "no direct evidence", "unfortunately",
    ])
    _should_refine = _answer_bad or _reasoning_uncertain

    if _should_refine and state["label"] in ("research", "image", "audio", "python_script", "other_ext", "youtube", "logic"):
        print(f"[SYNTHESIZE] Knowledge gap detected — filling missing information")
        gap_prompt = (
            "You just attempted to answer a question but your reasoning had gaps or assumptions.\n"
            "Analyze the reasoning below and identify 1-2 specific facts, definitions, or data points "
            "that you were missing or unsure about.\n"
            "For each gap, write a focused web search query (max 12 words) that would find that information.\n"
            "Output ONLY the search queries, one per line, no numbering or explanation.\n\n"
            f"Question: {state['question']}\n"
            f"Your reasoning: {reasoning[:800]}\n"
            f"Your answer: {answer}"
        )
        try:
            gap_queries_raw = _llm_router.invoke(gap_prompt).content.strip()
            gap_queries = [q.strip().strip('"').strip("'") for q in gap_queries_raw.split("\n") if q.strip()][:2]
            extra_parts = []
            for gq in gap_queries:
                print(f"[TOOL] gap-fill query: {gq}")
                extra = _do_research(state["question"], gq)
                if extra:
                    extra_parts.append(extra)
            if extra_parts:
                combined_context = state["context"] + "\n\nADDITIONAL KNOWLEDGE:\n" + "\n\n".join(extra_parts)
                reasoning_prompt2 = [
                    SystemMessage(content=get_prompt("reasoning_system")),
                    HumanMessage(
                        content=get_prompt(
                            prompt_key="reasoning_user",
                            question=state["question"],
                            context=combined_context,
                        )
                    ),
                ]
                reasoning2 = _llm_answer.invoke(reasoning_prompt2).content.strip()
                print(f"\n[REASONING PASS 2]\n{reasoning2}\n")
                fa_match2 = re.search(r"FINAL ANSWER:\s*(.+)", reasoning2, re.IGNORECASE)
                if fa_match2:
                    answer2 = fa_match2.group(1).strip().split('\n')[0].strip()
                    _still_bad = any(w in answer2.lower() for w in ["cannot", "unable", "not determine"])
                    if not _still_bad:
                        answer = answer2
                        reasoning = reasoning2  # update for downstream
        except Exception as e:
            print(f"[SYNTHESIZE] Gap-fill error: {e}")

    state["answer"] = answer
    return state

def format_output(state: AgentState) -> AgentState:
    txt = re.sub(r"^(final answer:?\s*)", "", state["answer"], flags=re.I).strip()

    # If question demands a single token (first name / one word), enforce it
    if any(kw in state["question"].lower() for kw in ["first name", "single word"]):
        txt = txt.split(" ")[0]

    state["answer"] = txt.rstrip(".")
    print(f"[FINAL ANSWER] {state['answer']}\n" + "-" * 60)
    return state

# --------------------------------------------------------------------------- #
#                              BUILD  THE  GRAPH                              #
# --------------------------------------------------------------------------- #
def build_graph() -> StateGraph:
    g = StateGraph(AgentState)
    g.set_entry_point("route_question")

    g.add_node("route_question", route_question)
    g.add_node("invoke_tools", call_tools)
    g.add_node("synthesize_response", synthesize_response)
    g.add_node("format_output", format_output)

    g.add_edge("route_question", "invoke_tools")
    g.add_edge("invoke_tools", "synthesize_response")
    g.add_edge("synthesize_response", "format_output")
    g.add_edge("format_output", END)

    return g.compile()

class LGAgent:
    """Callable wrapper used by run_and_submit_all."""

    def __init__(self, model_id: str | None = None, answer_model_id: str | None = None) -> None:
        global _llm_router, _llm_answer
        # Router: fast Groq model
        router_mid = model_id or GROQ_MODELS[0]["model_id"]
        _llm_router = ChatOpenAI(
            model=router_mid,
            base_url="https://api.groq.com/openai/v1",
            api_key=GROQ_API_KEY,
            timeout=60,
        )
        # Answering: higher quality OpenRouter model
        answer_mid = answer_model_id or OPENROUTER_MODELS[0]["model_id"]
        _llm_answer = ChatOpenAI(
            model=answer_mid,
            base_url="https://openrouter.ai/api/v1",
            api_key=OPENROUTER_API_KEY,
            timeout=120,
        )
        self.graph = build_graph()

    def __call__(self, question: str, task_id: str | None = None, file_name: str | None = None) -> str:
        try:
            state: AgentState = {
                "question": question,
                "label": "general",
                "context": "",
                "answer": "",
                "task_id": task_id,
                "file_name": file_name,
            }
            final = self.graph.invoke(state)

            route = final["label"]
            print(f"[ROUTE] '{route}' | Q: {question[:80]}")
            return final["answer"]
        except Exception as e:
            print("Agent error:", e)
            msg = str(e)
            # Re-raise rate-limit errors so _answer_question can fall back to the next model
            if "rate_limit_exceeded" in msg or "429" in msg or "413" in msg or "Request too large" in msg or "model_decommissioned" in msg or "decommissioned" in msg:
                raise
            return f"AGENT ERROR: {e}"

def _parse_retry_after(error_msg: str) -> float:
    """Parse the suggested wait time (seconds) from a Groq 429 error message."""
    m = re.search(r'try again in (?:(\d+)m)?(\d+(?:\.\d+)?)s', error_msg)
    if m:
        return float(m.group(1) or 0) * 60 + float(m.group(2))
    return 65.0  # safe default

def _to_str(val) -> str:
    """Ensure the submitted answer is always a plain string."""
    if isinstance(val, str):
        return val
    if isinstance(val, list):
        parts = [item.get("text", "") if isinstance(item, dict) else str(item) for item in val]
        return " ".join(parts).strip() or "ERROR: empty response"
    return str(val)


def _answer_question(item: dict) -> str:
    """Instantiate a fresh agent and answer one question, retrying on errors."""
    question_text = item["question"]
    task_id = item.get("task_id", "")
    file_name = item.get("file_name") or ""

    augmented_question = question_text

    # Try each OpenRouter answer model with Groq router
    for answer_cfg in OPENROUTER_MODELS:
        answer_model_id = answer_cfg["model_id"]
        if answer_model_id in _exhausted_models:
            print(f"[{answer_model_id}] Skipped (previously rate-limited)")
            continue
        for attempt in range(2):
            try:
                result = LGAgent(
                    model_id=GROQ_MODELS[0]["model_id"],
                    answer_model_id=answer_model_id,
                )(augmented_question, task_id=task_id, file_name=file_name)
                # Pause between questions to respect rate limits
                time.sleep(3)
                return result
            except Exception as e:
                msg = str(e)
                if "model_decommissioned" in msg or "decommissioned" in msg:
                    _exhausted_models.add(answer_model_id)
                    print(f"[{answer_model_id}] Model decommissioned — skipping permanently")
                    break
                if "rate_limit_exceeded" in msg or "429" in msg or "413" in msg or "Request too large" in msg:
                    if "on tokens per day" in msg or "TPD" in msg:
                        _exhausted_models.add(answer_model_id)
                        print(f"[{answer_model_id}] Daily token limit hit — skipping for remaining questions")
                        break
                    wait = _parse_retry_after(msg)
                    print(f"[{answer_model_id}] Rate limited — waiting {wait:.0f}s then retry")
                    time.sleep(min(wait, 30))
                    continue
                else:
                    print(f"[{answer_model_id}] Error: {msg[:200]}")
                    break  # try next model
    return "AGENT ERROR: all models exhausted"

def run_and_submit_all( profile: gr.OAuthProfile | None):
    """
    Fetches all questions, runs the BasicAgent on them, submits all answers,
    and displays the results.
    """
    # --- Determine HF Space Runtime URL and Repo URL ---
    space_id = os.getenv("SPACE_ID") # Get the SPACE_ID for sending link to the code

    if profile:
        username= f"{profile.username}"
        print(f"User logged in: {username}")
    else:
        print("User not logged in.")
        return "Please Login to Hugging Face with the button.", None

    api_url = DEFAULT_API_URL
    questions_url = f"{api_url}/questions"
    submit_url = f"{api_url}/submit"

    # 1. Agent is instantiated per-question inside _answer_question for parallel execution
    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
    print(agent_code)

    # 2. Fetch Questions
    print(f"Fetching questions from: {questions_url}")
    try:
        response = requests.get(questions_url, timeout=15)
        response.raise_for_status()
        questions_data = response.json()
        if not questions_data:
             print("Fetched questions list is empty.")
             return "Fetched questions list is empty or invalid format.", None
        print(f"Fetched {len(questions_data)} questions.")
    except requests.exceptions.RequestException as e:
        print(f"Error fetching questions: {e}")
        return f"Error fetching questions: {e}", None
    except requests.exceptions.JSONDecodeError as e:
         print(f"Error decoding JSON response from questions endpoint: {e}")
         print(f"Response text: {response.text[:500]}")
         return f"Error decoding server response for questions: {e}", None
    except Exception as e:
        print(f"An unexpected error occurred fetching questions: {e}")
        return f"An unexpected error occurred fetching questions: {e}", None

    # 3. Run your Agent (parallel)
    results_log = []
    answers_payload = []
    valid_items = [
        item for item in questions_data
        if item.get("task_id") and item.get("question") is not None
    ]
    print(f"Running agent on {len(valid_items)} questions")

    with concurrent.futures.ThreadPoolExecutor(max_workers=MAX_WORKERS) as executor:
        future_to_item = {
            executor.submit(_answer_question, item): item
            for item in valid_items
        }
        for future in concurrent.futures.as_completed(future_to_item):
            item = future_to_item[future]
            task_id = item["task_id"]
            question_text = item["question"]
            try:
                submitted_answer = _to_str(future.result(timeout=QUESTION_TIMEOUT))
            except concurrent.futures.TimeoutError:
                print(f"Timeout on task {task_id}")
                submitted_answer = "TIMEOUT"
            except Exception as e:
                print(f"Error running agent on task {task_id}: {e}")
                submitted_answer = f"AGENT ERROR: {e}"
            answers_payload.append({"task_id": task_id, "submitted_answer": _to_str(submitted_answer)})
            results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": submitted_answer})

    if not answers_payload:
        print("Agent did not produce any answers to submit.")
        return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)

    # 4. Prepare Submission 
    submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}
    status_update = f"Agent finished. Submitting {len(answers_payload)} answers for user '{username}'..."
    print(status_update)

    # 5. Submit
    print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
    try:
        response = requests.post(submit_url, json=submission_data, timeout=60)
        response.raise_for_status()
        result_data = response.json()
        final_status = (
            f"Submission Successful!\n"
            f"User: {result_data.get('username')}\n"
            f"Overall Score: {result_data.get('score', 'N/A')}% "
            f"({result_data.get('correct_count', '?')}/{result_data.get('total_attempted', '?')} correct)\n"
            f"Message: {result_data.get('message', 'No message received.')}"
        )
        print("Submission successful.")
        results_df = pd.DataFrame(results_log)
        return final_status, results_df
    except requests.exceptions.HTTPError as e:
        error_detail = f"Server responded with status {e.response.status_code}."
        try:
            error_json = e.response.json()
            error_detail += f" Detail: {error_json.get('detail', e.response.text)}"
        except requests.exceptions.JSONDecodeError:
            error_detail += f" Response: {e.response.text[:500]}"
        status_message = f"Submission Failed: {error_detail}"
        print(status_message)
        results_df = pd.DataFrame(results_log)
        return status_message, results_df
    except requests.exceptions.Timeout:
        status_message = "Submission Failed: The request timed out."
        print(status_message)
        results_df = pd.DataFrame(results_log)
        return status_message, results_df
    except requests.exceptions.RequestException as e:
        status_message = f"Submission Failed: Network error - {e}"
        print(status_message)
        results_df = pd.DataFrame(results_log)
        return status_message, results_df
    except Exception as e:
        status_message = f"An unexpected error occurred during submission: {e}"
        print(status_message)
        results_df = pd.DataFrame(results_log)
        return status_message, results_df


# --- Build Gradio Interface using Blocks ---
with gr.Blocks() as demo:
    gr.Markdown("# Basic Agent Evaluation Runner")
    gr.Markdown(
        """
        **Instructions:**
        1.  Please clone this space, then modify the code to define your agent's logic, the tools, the necessary packages, etc ...
        2.  Log in to your Hugging Face account using the button below. This uses your HF username for submission.
        3.  Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see the score.
        ---
        **Disclaimers:**
        Once clicking on the "submit button, it can take quite some time ( this is the time for the agent to go through all the questions).
        This space provides a basic setup and is intentionally sub-optimal to encourage you to develop your own, more robust solution. For instance for the delay process of the submit button, a solution could be to cache the answers and submit in a seperate action or even to answer the questions in async.
        """
    )

    gr.LoginButton()

    run_button = gr.Button("Run Evaluation & Submit All Answers")

    status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False)
    # Removed max_rows=10 from DataFrame constructor
    results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)

    run_button.click(
        fn=run_and_submit_all,
        outputs=[status_output, results_table]
    )

if __name__ == "__main__":
    print("\n" + "-"*30 + " App Starting " + "-"*30)
    # Check for SPACE_HOST and SPACE_ID at startup for information
    space_host_startup = os.getenv("SPACE_HOST")
    space_id_startup = os.getenv("SPACE_ID") # Get SPACE_ID at startup

    if space_host_startup:
        print(f"✅ SPACE_HOST found: {space_host_startup}")
        print(f"   Runtime URL should be: https://{space_host_startup}.hf.space")
    else:
        print("ℹ️  SPACE_HOST environment variable not found (running locally?).")

    if space_id_startup: # Print repo URLs if SPACE_ID is found
        print(f"✅ SPACE_ID found: {space_id_startup}")
        print(f"   Repo URL: https://huggingface.co/spaces/{space_id_startup}")
        print(f"   Repo Tree URL: https://huggingface.co/spaces/{space_id_startup}/tree/main")
    else:
        print("ℹ️  SPACE_ID environment variable not found (running locally?). Repo URL cannot be determined.")

    print("-"*(60 + len(" App Starting ")) + "\n")

    print("Launching Gradio Interface for Basic Agent Evaluation...")
    demo.launch(debug=True, share=False)