from __future__ import annotations

import os
import re
import tempfile
import time
from collections import deque
from concurrent.futures import Future, ThreadPoolExecutor, as_completed
from typing import Annotated, Dict, List, Tuple
from urllib.parse import urlparse

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

from .Web_Fetch import _fullpage_markdown_from_soup, _http_get_enhanced
from app import _log_call_end, _log_call_start, _search_rate_limiter, _truncate_for_log

HF_TEXTGEN_TOKEN = os.getenv("HF_READ_TOKEN") or os.getenv("HF_TOKEN")


class SlowHost(Exception):
    pass


def _normalize_query(q: str) -> str:
    if not q:
        return ""
    repl = {"“": '"', "”": '"', "‘": "'", "’": "'", "`": "'"}
    for key, value in repl.items():
        q = q.replace(key, value)
    q = re.sub(r"\s+", " ", q)
    q = re.sub(r'"\s+"', " ", q)
    q = q.strip().strip('"').strip()
    return q


def _search_urls_only(query: str, max_results: int) -> list[str]:
    if not query or not query.strip() or max_results <= 0:
        return []
    urls: list[str] = []
    try:
        _search_rate_limiter.acquire()
        with DDGS() as ddgs:
            for item in ddgs.text(query, region="wt-wt", safesearch="moderate", max_results=max_results):
                url = (item.get("href") or item.get("url") or "").strip()
                if url:
                    urls.append(url)
    except Exception:
        pass
    seen = set()
    deduped = []
    for url in urls:
        if url not in seen:
            seen.add(url)
            deduped.append(url)
    return deduped


def _fetch_page_markdown_fast(url: str, max_chars: int = 3000, timeout: float = 10.0) -> str:
    try:
        resp = _http_get_enhanced(url, timeout=timeout, skip_rate_limit=True)
        resp.raise_for_status()
    except requests.exceptions.RequestException as exc:
        msg = str(exc)
        if "timed out" in msg.lower():
            raise SlowHost(msg) from exc
        return ""
    final_url = str(resp.url)
    ctype = resp.headers.get("Content-Type", "")
    if "html" not in ctype.lower():
        return ""
    resp.encoding = resp.encoding or resp.apparent_encoding
    html = resp.text
    soup = BeautifulSoup(html, "lxml")
    md_text = _fullpage_markdown_from_soup(soup, final_url, "")
    if max_chars > 0 and len(md_text) > max_chars:
        md_text = md_text[:max_chars]
    return md_text


def _truncate_join(parts: List[str], max_chars: int) -> Tuple[str, bool]:
    out = []
    total = 0
    truncated = False
    for part in parts:
        if not part:
            continue
        if total + len(part) > max_chars:
            out.append(part[: max(0, max_chars - total)])
            truncated = True
            break
        out.append(part)
        total += len(part)
    return ("\n\n".join(out), truncated)


def _build_research_prompt(summary: str, queries: List[str], url_list: List[str], pages_map: Dict[str, str]) -> str:
    researcher_instructions = (
        "You are Nymbot, a helpful deep research assistant. You will be asked a Query from a user and you will create a long, comprehensive, well-structured research report in response to the user's Query.\n\n"
        "You have been provided with User Question, Search Queries, and numerous webpages that the searches yielded.\n\n"
        "<report_format>\n"
        "Write a well-formatted report in the structure of a scientific report to a broad audience. The report must be readable and have a nice flow of Markdown headers and paragraphs of text. Do NOT use bullet points or lists which break up the natural flow. The report must be exhaustive for comprehensive topics.\n"
        "For any given user query, first determine the major themes or areas that need investigation, then structure these as main sections, and develop detailed subsections that explore various facets of each theme. Each section and subsection requires paragraphs of texts that need to all connect into one narrative flow.\n"
        "</report_format>\n\n"
        "<document_structure>\n"
        "- Always begin with a clear title using a single # header\n"
        "- Organize content into major sections using ## headers\n"
        "- Further divide into subsections using ### headers\n"
        "- Use #### headers sparingly for special subsections\n"
        "- Never skip header levels\n"
        "- Write multiple paragraphs per section or subsection\n"
        "- Each paragraph must contain at least 4-5 sentences, present novel insights and analysis grounded in source material, connect ideas to original query, and build upon previous paragraphs to create a narrative flow\n"
        "- Never use lists, instead always use text or tables\n\n"
        "Mandatory Section Flow:\n"
        "1. Title (# level)\n   - Before writing the main report, start with one detailed paragraph summarizing key findings\n"
        "2. Main Body Sections (## level)\n   - Each major topic gets its own section (## level). There MUST BE at least 5 sections.\n   - Use ### subsections for detailed analysis\n   - Every section or subsection needs at least one paragraph of narrative before moving to the next section\n   - Do NOT have a section titled \"Main Body Sections\" and instead pick informative section names that convey the theme of the section\n"
        "3. Conclusion (## level)\n   - Synthesis of findings\n   - Potential recommendations or next steps\n"
        "</document_structure>\n\n"
        "<planning_rules>\n"
        "- Always break it down into multiple steps\n"
        "- Assess the different sources and whether they are useful for any steps needed to answer the query\n"
        "- Create the best report that weighs all the evidence from the sources\n"
        "- Remember that the current date is: Wednesday, April 23, 2025, 11:50 AM EDT\n"
        "- Make sure that your final report addresses all parts of the query\n"
        "- Communicate a brief high-level plan in the introduction; do not reveal chain-of-thought.\n"
        "- When referencing sources during analysis, you should still refer to them by index with brackets and follow <citations>\n"
        "- As a final step, review your planned report structure and ensure it completely answers the query.\n"
        "</planning_rules>\n\n"
    )
    sources_blocks: List[str] = []
    indexed_urls: List[str] = []
    for idx, url in enumerate(url_list, start=1):
        text = pages_map.get(url, "").strip()
        if not text:
            continue
        indexed_urls.append(f"[{idx}] {url}")
        sources_blocks.append(f"[Source {idx}] URL: {url}\n\n{text}")
    sources_joined, truncated = _truncate_join(sources_blocks, max_chars=100_000)
    prompt_parts = [researcher_instructions]
    prompt_parts.append("<user_query_summary>\n" + (summary or "") + "\n</user_query_summary>\n")
    populated = [q for q in queries if q and q.strip()]
    if populated:
        prompt_parts.append("<search_queries>\n" + "\n".join(f"- {q.strip()}" for q in populated) + "\n</search_queries>\n")
    if indexed_urls:
        prompt_parts.append("<sources_list>\n" + "\n".join(indexed_urls) + "\n</sources_list>\n")
    prompt_parts.append("<fetched_documents>\n" + sources_joined + ("\n\n[NOTE] Sources truncated due to context limits." if truncated else "") + "\n</fetched_documents>")
    return "\n\n".join(prompt_parts)


def _write_report_tmp(text: str) -> str:
    tmp_dir = tempfile.mkdtemp(prefix="deep_research_")
    path = os.path.join(tmp_dir, "research_report.txt")
    with open(path, "w", encoding="utf-8") as file:
        file.write(text)
    return path


def Deep_Research(
    summary: Annotated[str, "Summarization of research topic (one or more sentences)."],
    query1: Annotated[str, "DDG Search Query 1"],
    max1: Annotated[int, "Max results for Query 1 (1-50)"] = 10,
    query2: Annotated[str, "DDG Search Query 2"] = "",
    max2: Annotated[int, "Max results for Query 2 (1-50)"] = 10,
    query3: Annotated[str, "DDG Search Query 3"] = "",
    max3: Annotated[int, "Max results for Query 3 (1-50)"] = 10,
    query4: Annotated[str, "DDG Search Query 4"] = "",
    max4: Annotated[int, "Max results for Query 4 (1-50)"] = 10,
    query5: Annotated[str, "DDG Search Query 5"] = "",
    max5: Annotated[int, "Max results for Query 5 (1-50)"] = 10,
) -> tuple[str, str, str]:
    _log_call_start(
        "Deep_Research",
        summary=_truncate_for_log(summary or "", 200),
        queries=[q for q in [query1, query2, query3, query4, query5] if q],
    )
    if not HF_TEXTGEN_TOKEN:
        _log_call_end("Deep_Research", "error=missing HF token")
        raise gr.Error("Please provide a `HF_READ_TOKEN` to enable Deep Research.")
    queries = [
        _normalize_query(query1 or ""),
        _normalize_query(query2 or ""),
        _normalize_query(query3 or ""),
        _normalize_query(query4 or ""),
        _normalize_query(query5 or ""),
    ]
    reqs = [
        max(1, min(50, int(max1))),
        max(1, min(50, int(max2))),
        max(1, min(50, int(max3))),
        max(1, min(50, int(max4))),
        max(1, min(50, int(max5))),
    ]
    total_requested = sum(reqs)
    if total_requested > 50:
        reqs = [10, 10, 10, 10, 10]
    start_ts = time.time()
    budget_seconds = 55.0
    deadline = start_ts + budget_seconds

    def time_left() -> float:
        return max(0.0, deadline - time.time())

    all_urls: list[str] = []
    tasks = []
    with ThreadPoolExecutor(max_workers=min(5, sum(1 for q in queries if q.strip())) or 1) as executor:
        for query, count in zip(queries, reqs):
            if not query.strip():
                continue
            tasks.append(executor.submit(_search_urls_only, query.strip(), count))
        for future in as_completed(tasks):
            try:
                urls = future.result() or []
            except Exception:
                urls = []
            for url in urls:
                if url not in all_urls:
                    all_urls.append(url)
            if len(all_urls) >= 50:
                break
            if time_left() <= 0.5:
                break
    if len(all_urls) > 50:
        all_urls = all_urls[:50]
    blacklist = {
        "homedepot.com",
        "tractorsupply.com",
        "mcmaster.com",
        "mrchain.com",
        "answers.com",
        "city-data.com",
        "dictionary.cambridge.org",
    }

    def _domain(url: str) -> str:
        try:
            return urlparse(url).netloc.lower()
        except Exception:
            return ""

    all_urls = [url for url in all_urls if _domain(url) not in blacklist]
    skip_exts = (
        ".pdf",
        ".ppt",
        ".pptx",
        ".doc",
        ".docx",
        ".xls",
        ".xlsx",
        ".zip",
        ".gz",
        ".tgz",
        ".bz2",
        ".7z",
        ".rar",
    )

    def _skip_url(url: str) -> bool:
        try:
            path = urlparse(url).path.lower()
        except Exception:
            return False
        return any(path.endswith(ext) for ext in skip_exts)

    all_urls = [url for url in all_urls if not _skip_url(url)]
    pages: dict[str, str] = {}
    if all_urls:
        queue = deque(all_urls)
        attempts: dict[str, int] = {url: 0 for url in all_urls}
        max_attempts = 2
        max_workers = min(12, max(4, len(all_urls)))
        in_flight: dict[Future, str] = {}
        delayed: list[tuple[float, str]] = []

        def schedule_next(executor: ThreadPoolExecutor) -> None:
            while queue and len(in_flight) < max_workers:
                url = queue.popleft()
                if url in pages:
                    continue
                if attempts[url] >= max_attempts:
                    continue
                attempts[url] += 1
                tl = time_left()
                per_timeout = 10.0 if tl > 15 else (5.0 if tl > 8 else 2.0)
                future = executor.submit(_fetch_page_markdown_fast, url, 3000, per_timeout)
                in_flight[future] = url

        with ThreadPoolExecutor(max_workers=max_workers) as executor:
            schedule_next(executor)
            while (in_flight or queue) and time_left() > 0.2:
                now = time.time()
                if delayed:
                    ready = []
                    not_ready = []
                    for ready_time, url in delayed:
                        (ready if ready_time <= now else not_ready).append((ready_time, url))
                    delayed = not_ready
                    for _, url in ready:
                        queue.append(url)
                    if ready:
                        schedule_next(executor)
                done = [future for future in list(in_flight.keys()) if future.done()]
                if not done:
                    if not queue and delayed:
                        sleep_for = max(0.02, min(0.25, max(0.0, min(t for t, _ in delayed) - time.time())))
                        time.sleep(sleep_for)
                    else:
                        time.sleep(0.05)
                else:
                    for future in done:
                        url = in_flight.pop(future)
                        try:
                            md = future.result()
                            if md and not md.startswith("Unsupported content type") and not md.startswith("An error occurred"):
                                pages[url] = md
                                try:
                                    print(f"[FETCH OK] {url} (chars={len(md)})", flush=True)
                                except Exception:
                                    pass
                        except SlowHost:
                            if time_left() > 5.0:
                                delayed.append((time.time() + 3.0, url))
                        except Exception:
                            pass
                    schedule_next(executor)
    prompt = _build_research_prompt(summary=summary or "", queries=[q for q in queries if q.strip()], url_list=list(pages.keys()), pages_map=pages)
    messages = [
        {"role": "system", "content": "You are Nymbot, an expert deep research assistant."},
        {"role": "user", "content": prompt},
    ]
    try:
        prompt_chars = len(prompt)
    except Exception:
        prompt_chars = -1
    print(f"[PIPELINE] Fetch complete: pages={len(pages)}, unique_urls={len(pages.keys())}, prompt_chars={prompt_chars}", flush=True)
    print("[PIPELINE] Starting inference (provider=cerebras, model=Qwen/Qwen3-235B-A22B-Thinking-2507)", flush=True)

    def _run_inference(provider: str, max_tokens: int, temp: float, top_p: float):
        client = InferenceClient(provider=provider, api_key=HF_TEXTGEN_TOKEN)
        return client.chat.completions.create(
            model="Qwen/Qwen3-235B-A22B-Thinking-2507",
            messages=messages,
            max_tokens=max_tokens,
            temperature=temp,
            top_p=top_p,
        )

    try:
        print("[LLM] Attempt 1: provider=cerebras, max_tokens=32768", flush=True)
        completion = _run_inference("cerebras", max_tokens=32768, temp=0.3, top_p=0.95)
    except Exception as exc1:
        print(f"[LLM] Attempt 1 failed: {str(exc1)[:200]}", flush=True)
        try:
            prompt2 = _build_research_prompt(
                summary=summary or "",
                queries=[q for q in queries if q.strip()],
                url_list=list(pages.keys())[:30],
                pages_map={key: pages[key] for key in list(pages.keys())[:30]},
            )
            messages = [
                {"role": "system", "content": "You are Nymbot, an expert deep research assistant."},
                {"role": "user", "content": prompt2},
            ]
            print("[LLM] Attempt 2: provider=cerebras (trimmed), max_tokens=16384", flush=True)
            completion = _run_inference("cerebras", max_tokens=16384, temp=0.7, top_p=0.95)
        except Exception as exc2:
            print(f"[LLM] Attempt 2 failed: {str(exc2)[:200]}", flush=True)
            try:
                print("[LLM] Attempt 3: provider=auto, max_tokens=8192", flush=True)
                completion = _run_inference("auto", max_tokens=8192, temp=0.7, top_p=0.95)
            except Exception as exc3:
                _log_call_end("Deep_Research", f"error={_truncate_for_log(str(exc3), 260)}")
                raise gr.Error(f"Researcher model call failed: {exc3}")
    raw = completion.choices[0].message.content or ""
    try:
        no_think = re.sub(r"<think>[\s\S]*?<\\/think>", "", raw, flags=re.IGNORECASE)
        no_think = re.sub(r"<\\/?think>", "", no_think, flags=re.IGNORECASE)
    except Exception:
        no_think = raw
    try:
        paragraphs = [p for p in re.split(r"\n\s*\n", no_think) if p.strip()]
        keep: List[str] = []
        removed = 0
        planning_re = re.compile(r"\b(let me|now i(?:'ll| will)?|first,|i will now|i will|i'll|let's|now let me|i need to|now i'll|now i will)\b", re.IGNORECASE)
        for paragraph in paragraphs:
            if planning_re.search(paragraph):
                removed += 1
                continue
            keep.append(paragraph)
        report = "\n\n".join(keep).strip()
        if not report:
            report = no_think.strip()
    except Exception:
        report = no_think
        removed = 0
    report = re.sub(r"\n\s*\n\s*\n+", "\n\n", report)
    try:
        print(f"[POSTPROCESS] removed_planning_paragraphs={removed}, raw_chars={len(raw)}, final_chars={len(report)}", flush=True)
    except Exception:
        pass
    links_text = "\n".join([f"[{i+1}] {url}" for i, url in enumerate(pages.keys())])
    file_path = _write_report_tmp(report)
    elapsed = time.time() - start_ts
    print(f"[TIMING] Deep_Research elapsed: {elapsed:.2f}s", flush=True)
    _log_call_end("Deep_Research", f"urls={len(pages)} file={os.path.basename(file_path)} duration={elapsed:.2f}s")
    return report, links_text, file_path


def build_interface() -> gr.Interface:
    return gr.Interface(
        fn=Deep_Research,
        inputs=[
            gr.Textbox(label="Summarization of research topic", lines=3, placeholder="Briefly summarize the research topic or user question"),
            gr.Textbox(label="DDG Search Query 1", max_lines=1),
            gr.Slider(1, 50, value=10, step=1, label="Max results (Q1)"),
            gr.Textbox(label="DDG Search Query 2", value="", max_lines=1),
            gr.Slider(1, 50, value=10, step=1, label="Max results (Q2)"),
            gr.Textbox(label="DDG Search Query 3", value="", max_lines=1),
            gr.Slider(1, 50, value=10, step=1, label="Max results (Q3)"),
            gr.Textbox(label="DDG Search Query 4", value="", max_lines=1),
            gr.Slider(1, 50, value=10, step=1, label="Max results (Q4)"),
            gr.Textbox(label="DDG Search Query 5", value="", max_lines=1),
            gr.Slider(1, 50, value=10, step=1, label="Max results (Q5)"),
        ],
        outputs=[
            gr.Markdown(label="Research Report"),
            gr.Textbox(label="Fetched Links", lines=8),
            gr.File(label="Download Research Report", file_count="single"),
        ],
        title="Deep Research",
        description=(
            "<div style=\"text-align:center\">Perform multi-query web research: search with DuckDuckGo, fetch up to 50 pages in parallel, "
            "and generate a comprehensive report using a large LLM via Hugging Face Inference Providers (Cerebras). Requires HF_READ_TOKEN.</div>"
        ),
        api_description=(
            "Runs 1–5 DDG searches (URLs only), caps total results to 50 (when exceeding, each query returns 10). "
            "Fetches all URLs (3000 chars each) and calls the Researcher to write a research report. "
            "Returns the report (Markdown), the list of sources, and a downloadable text file path. "
            "Provide the user with one-paragraph summary of the research report and the txt file in this format `![research_report.txt](URL)`"
        ),
        flagging_mode="never",
        show_api=bool(HF_TEXTGEN_TOKEN),
    )


__all__ = ["Deep_Research", "build_interface"]