import os
from typing import List

from langchain_chroma import Chroma
from langchain_core.documents.base import Document
from langchain_core.tools import tool
from langchain_core.tools.base import ArgsSchema
from langchain_huggingface import HuggingFaceEmbeddings
from pydantic import SecretStr
from sqlalchemy.sql.selectable import ForUpdateParameter

# Initialize RAG vector store for strategy retrieval
CHROMA_PATH = "./chroma_gaia_db"
_embeddings = None
_vector_store = None


def _get_vector_store():
    """Lazy load vector store."""
    global _embeddings, _vector_store
    if _vector_store is None:
        _embeddings = HuggingFaceEmbeddings(
            model_name="sentence-transformers/all-mpnet-base-v2"
        )
        _vector_store = Chroma(
            persist_directory=CHROMA_PATH, embedding_function=_embeddings
        )
    return _vector_store


@tool
def get_solving_strategy(question: str) -> str:
    """Search for similar solved questions and get the solving strategy.
    Use this FIRST to understand how to approach a problem before using other tools.

    Args:
        question: The question you need to solve."""
    print(f"\n[GET_SOLVING_STRATEGY] Searching for: {question[:80]}...")
    try:
        vector_store = _get_vector_store()
        similar_docs = vector_store.similarity_search(question, k=1)
        print(f"[GET_SOLVING_STRATEGY] Found {len(similar_docs)} similar questions")

        if similar_docs:
            doc = similar_docs[0]
            steps = (
                doc.page_content.split("Steps to solve:")[-1]
                .split("Tools needed:")[0]
                .strip()
            )
            tools_raw = doc.metadata.get("tools", "")
            # Clean up tools format - replace inline numbers with newlines
            tools = tools_raw.replace("\n", "\n- ").strip()
            if tools and not tools.startswith("-"):
                tools = "- " + tools

            set_current_strategy(steps)

            return f"""Similar question found!

## Strategy to solve (按此策略执行):
{steps}

## Rules (必须严格遵守):
1. Use EXACT wording from sources. Do not paraphrase or shorten.
2. For lists: sort items alphabetically, separate with comma and space.
3. Use tools to find information. Do not guess.
4. When you find the answer, call `submit_answer` immediately. 不要继续搜索。

"""
        else:
            return "No similar questions found. Use your best judgment."
    except Exception as e:
        return f"Error searching for strategy: {e}"


def _get_llm():
    """Get LLM for post-processing."""
    from langchain_openai import ChatOpenAI

    if os.getenv("ZAI_API_KEY"):
        api_base = "https://api.z.ai/api/paas/v4/"
        if os.getenv("ZAI_USE_CODING_PLAN", "f") == "t":
            api_base = "https://api.z.ai/api/coding/paas/v4/"
        return ChatOpenAI(
            model="GLM-4.5-Air",
            temperature=0,
            base_url=api_base,
            api_key=SecretStr(os.getenv("ZAI_API_KEY", "")),
        )
    else:
        return ChatOpenAI(model="gpt-4o-mini", temperature=0)


def _fetch_url_with_tables(url: str) -> str:
    """Fetch URL content including tables using Jina reader."""
    import requests

    try:
        # Use Jina to get full page content including tables
        api_key = os.getenv("JINA_API_KEY", "")
        headers = {"Authorization": f"Bearer {api_key}"} if api_key else {}

        response = requests.get(f"https://r.jina.ai/{url}", headers=headers, timeout=30)
        return response.text
    except Exception:
        return ""


@tool
def wiki_search(query: str) -> str:
    """Search Wikipedia for a query and return relevant content including tables.

    Args:
        query: The search query."""
    import wikipedia

    try:
        # Search for pages
        search_results = wikipedia.search(query, results=3)
        if not search_results:
            return "No Wikipedia results found."

        formatted_parts = []
        for title in search_results[:2]:
            try:
                page = wikipedia.page(title, auto_suggest=False)
                url = page.url

                # Fetch the page via Jina to get full content including tables
                content = _fetch_url_with_tables(url)

                if not content:
                    # Fallback to wikipedia API content
                    content = page.content

                # Use smart section extraction
                extracted = _extract_relevant_content(content, query)
                formatted_parts.append(
                    f'<Document source="{url}" title="{title}">\n{extracted}\n</Document>'
                )
            except (wikipedia.DisambiguationError, wikipedia.PageError):
                continue
            except Exception:
                continue

        return (
            "\n\n---\n\n".join(formatted_parts)
            if formatted_parts
            else "No results found."
        )
    except Exception as e:
        return f"Wikipedia search error: {e}"


_zai_mcp_tools = None


async def _get_zai_mcp_tools():
    """Lazy load Z.AI MCP tools."""
    global _zai_mcp_tools
    if _zai_mcp_tools is None:
        from langchain_mcp_adapters.client import MultiServerMCPClient

        api_key = os.getenv("ZAI_API_KEY", "")
        client = MultiServerMCPClient(
            {
                "web-search": {
                    "transport": "streamable_http",
                    "url": "https://api.z.ai/api/mcp/web_search_prime/mcp",
                    "headers": {"Authorization": f"Bearer {api_key}"},
                },
                "web-reader": {
                    "transport": "streamable_http",
                    "url": "https://api.z.ai/api/mcp/web_reader/mcp",
                    "headers": {"Authorization": f"Bearer {api_key}"},
                },
                "zai-mcp": {
                    "transport": "stdio",
                    "command": "npx",
                    "args": ["-y", "@z_ai/mcp-server"],
                    "env": {
                        "Z_AI_API_KEY": api_key,
                        "Z_AI_MODE": "ZAI",
                    },
                },
            }
        )
        _zai_mcp_tools = await client.get_tools()
    return _zai_mcp_tools


@tool
def jina_search(query: str) -> str:
    """Search the web using Jina AI and return clean results.

    Args:
        query: The search query."""
    import requests

    api_key = os.getenv("JINA_API_KEY", "")
    headers = {"Authorization": f"Bearer {api_key}"} if api_key else {}

    response = requests.get(f"https://s.jina.ai/{query}", headers=headers, timeout=30)
    return response.text


def _extract_section_by_marker(
    content: str, section_marker: str, context_lines: int = 50
) -> str:
    """Extract a section starting from a marker found in strategy steps.

    This is the SMART extraction - uses strategy steps like "scrolled down to Studio albums"
    to find the exact section we need.
    """
    import re

    lines = content.split("\n")
    marker_lower = section_marker.lower().strip()

    print(f"[EXTRACT_SECTION] Looking for section marker: '{section_marker}'")

    # Find the line containing the section marker
    start_idx = None
    for i, line in enumerate(lines):
        if marker_lower in line.lower():
            start_idx = i
            print(f"[EXTRACT_SECTION] Found marker at line {i}: {line[:80]}")
            break

    if start_idx is None:
        # Try partial matching (e.g., "Studio albums" might be "Studio Albums" or "Discography")
        for i, line in enumerate(lines):
            # Check if most words from marker are in line
            marker_words = [
                w for w in re.findall(r"\b\w+\b", marker_lower) if len(w) > 2
            ]
            line_lower = line.lower()
            matches = sum(1 for w in marker_words if w in line_lower)
            if matches >= len(marker_words) * 0.6:  # 60% match threshold
                start_idx = i
                print(f"[EXTRACT_SECTION] Found partial match at line {i}: {line[:80]}")
                break

    if start_idx is None:
        print(f"[EXTRACT_SECTION] Section marker not found")
        return ""

    # Extract from marker line + context_lines after it
    end_idx = min(start_idx + context_lines, len(lines))
    section = "\n".join(lines[start_idx:end_idx])

    print(f"[EXTRACT_SECTION] Extracted {end_idx - start_idx} lines from section")
    return section


def _parse_section_markers_from_strategy(strategy: str) -> list:
    """Parse strategy steps to extract section markers.

    Looks for phrases like:
    - "scrolled down to Studio albums" -> "Studio albums"
    - "found the Discography section" -> "Discography"
    - "went to Studio albums" -> "Studio albums"
    """
    import re

    markers = []

    # Patterns that indicate a section name
    patterns = [
        r'scrolled?\s+(?:down\s+)?to\s+["\']?([^"\',.]+)["\']?',  # scrolled down to X
        r'went\s+to\s+(?:the\s+)?["\']?([^"\',.]+)["\']?\s+section',  # went to X section
        r'found\s+(?:the\s+)?["\']?([^"\',.]+)["\']?\s+section',  # found X section
        r'clicked\s+on\s+["\']?([^"\',.]+)["\']?',  # clicked on X
        r'looked\s+(?:at|under)\s+["\']?([^"\',.]+)["\']?',  # looked at/under X
        r'(?:in|under)\s+(?:the\s+)?["\']?([^"\',.]+)["\']?\s+section',  # in/under X section
    ]

    for pattern in patterns:
        matches = re.findall(pattern, strategy.lower())
        for match in matches:
            cleaned = match.strip()
            if cleaned and len(cleaned) > 2 and len(cleaned) < 50:
                markers.append(cleaned)

    # Also look for quoted section names
    quoted = re.findall(r'"([^"]+)"', strategy)
    for q in quoted:
        if len(q) > 2 and len(q) < 50 and q.lower() not in ["wikipedia", "google"]:
            markers.append(q)

    print(f"[PARSE_STRATEGY] Extracted section markers: {markers}")
    return markers


# Global variable to store current strategy for smart extraction
_current_strategy = None


def set_current_strategy(strategy: str):
    """Store the current strategy for use by content extraction."""
    global _current_strategy
    _current_strategy = strategy
    print(f"[STRATEGY] Updated current strategy")


@tool
def jina_read(url: str, question: str = "") -> str:
    """Read a webpage and extract content relevant to the question.

    Args:
        url: The URL to read.
        question: The question to extract relevant info for."""
    import requests

    api_key = os.getenv("JINA_API_KEY", "")
    headers = {"Authorization": f"Bearer {api_key}"} if api_key else {}

    response = requests.get(f"https://r.jina.ai/{url}", headers=headers, timeout=30)
    content = response.text

    # Use smart extraction with strategy section markers
    if question:
        return content[:10000]


@tool
def web_search(query: str) -> str:
    """Search the web and return summarized results with URLs."""
    if os.getenv("TAVILY_API_KEY"):
        from langchain_tavily import TavilySearch

        web_search_tool = TavilySearch(
            max_results=5,
            include_answer=False,
        )
    else:
        from langchain_community.tools import DuckDuckGoSearchResults

        web_search_tool = DuckDuckGoSearchResults()

    search_docs = web_search_tool.invoke(query)

    if isinstance(search_docs, str):
        return search_docs
    elif isinstance(search_docs, dict) and "results" in search_docs:
        results = search_docs["results"]
    elif isinstance(search_docs, list):
        results = search_docs
    else:
        return str(search_docs)

    formatted_search_docs = "\n\n---\n\n".join(
        [
            f'<Document source="{doc.get("url", "")}"/>\n{doc.get("content", "")}\n</Document>'
            for doc in results
        ]
    )
    return formatted_search_docs


@tool
def arxiv_search(query: str) -> str:
    """Search arXiv for a query and return maximum 2 results.

    Args:
        query: The search query."""
    from langchain_community.document_loaders import ArxivLoader

    search_docs = ArxivLoader(query=query, load_max_docs=2).load()
    parts = []
    for doc in search_docs:
        source = doc.metadata.get("source", "")
        parts.append(f"Source: {source}\n{doc.page_content}")
    return "\n\n---\n\n".join(parts)


@tool
def analyze_text(text: str, question: str) -> str:
    """Analyze text and extract the answer to a specific question. Use after fetching a webpage or PDF."""
    llm = _get_llm()
    response = llm.invoke(
        f"Given this text:\n\n{text[:8000]}\n\n"
        f"Answer this question: {question}\n\n"
        f"Be specific and list any relevant data points (numbers, dates, names). "
        f"If counting items, list each one explicitly before giving the count."
    )
    return response.content


@tool
def read_excel(file_path: str) -> str:
    """Read and extract data from an Excel file (.xlsx, .xls).

    Args:
        file_path: Path to the Excel file."""
    import pandas as pd

    try:
        # Read all sheets
        xlsx = pd.ExcelFile(file_path)
        results = []
        for sheet_name in xlsx.sheet_names:
            df = pd.read_excel(xlsx, sheet_name=sheet_name)
            results.append(f"=== Sheet: {sheet_name} ===\n{df.to_string()}")
        return "\n\n".join(results)[:15000]
    except Exception as e:
        return f"Error reading Excel: {e}"


@tool
def read_csv(file_path: str) -> str:
    """Read and extract data from a CSV file.

    Args:
        file_path: Path to the CSV file."""
    import pandas as pd

    try:
        df = pd.read_csv(file_path)
        return df.to_string()[:15000]
    except Exception as e:
        return f"Error reading CSV: {e}"


@tool
def read_docx(file_path: str) -> str:
    """Read and extract text from a Word document (.docx).

    Args:
        file_path: Path to the Word document."""
    try:
        from docx import Document

        doc = Document(file_path)
        text = "\n".join([para.text for para in doc.paragraphs])
        return text[:15000]
    except Exception as e:
        return f"Error reading Word doc: {e}"


@tool
def read_pptx(file_path: str) -> str:
    """Read and extract text from a PowerPoint presentation (.pptx).

    Args:
        file_path: Path to the PowerPoint file."""
    try:
        from pptx import Presentation

        prs = Presentation(file_path)
        text_parts = []
        for slide_num, slide in enumerate(prs.slides, 1):
            slide_text = [f"=== Slide {slide_num} ==="]
            for shape in slide.shapes:
                if hasattr(shape, "text"):
                    slide_text.append(shape.text)
            text_parts.append("\n".join(slide_text))
        return "\n\n".join(text_parts)[:15000]
    except Exception as e:
        return f"Error reading PowerPoint: {e}"


@tool
def extract_zip(file_path: str) -> str:
    """Extract a zip file and list its contents.

    Args:
        file_path: Path to the zip file."""
    import zipfile
    from pathlib import Path

    try:
        extract_dir = Path(file_path).parent / Path(file_path).stem
        extract_dir.mkdir(exist_ok=True)

        with zipfile.ZipFile(file_path, "r") as zip_ref:
            zip_ref.extractall(extract_dir)
            file_list = zip_ref.namelist()

        return f"Extracted to: {extract_dir}\nContents:\n" + "\n".join(file_list)
    except Exception as e:
        return f"Error extracting zip: {e}"


@tool
def analyze_image(file_path: str, question: str) -> str:
    """Analyze an image and answer a question about it using vision model.

    Args:
        file_path: Path to the image file (png, jpg, etc.)
        question: Question to answer about the image."""
    import base64

    from langchain_openai import ChatOpenAI

    try:
        with open(file_path, "rb") as f:
            image_data = base64.b64encode(f.read()).decode("utf-8")

        # Determine mime type
        ext = file_path.lower().split(".")[-1]
        mime_type = {"png": "image/png", "jpg": "image/jpeg", "jpeg": "image/jpeg"}.get(
            ext, "image/png"
        )

        # Use GPT-4o for vision
        llm = ChatOpenAI(model="gpt-4o", temperature=0)
        response = llm.invoke(
            [
                {
                    "role": "user",
                    "content": [
                        {"type": "text", "text": question},
                        {
                            "type": "image_url",
                            "image_url": {
                                "url": f"data:{mime_type};base64,{image_data}"
                            },
                        },
                    ],
                }
            ]
        )
        return response.content
    except Exception as e:
        return f"Error analyzing image: {e}"


@tool
def submit_answer(answer: str) -> str:
    """Submit your final answer. Use this when you have found the answer.

    Args:
        answer: The final answer to submit."""
    print(f"[SUBMIT_ANSWER] {answer}")
    return f"FINAL ANSWER: {answer}"


async def get_tools() -> list:
    """Retrieve the list of available tools for the agent."""
    base_tools = [
        get_solving_strategy,  # Use FIRST to get approach
        submit_answer,
        # wiki_search,
        download_file,
        read_pdf,
        read_excel,
        read_csv,
        read_docx,
        read_pptx,
        extract_zip,
        analyze_image,
        py_calc_tool,
        youtube_transcript_tool,
        transcribe_audio,
        arxiv_search,
    ]
    # Add Z.AI MCP tools (webSearchPrime, webReader)
    zai_tools = await _get_zai_mcp_tools()
    return base_tools + zai_tools


@tool
def py_calc_tool(expression: str) -> str:
    """Evaluate a Python expression safely."""
    try:
        allowed_builtins = {"__builtins__": {}}
        result = eval(expression, allowed_builtins, {})
        return str(result)
    except Exception as e:
        return f"Error evaluating expression: {e}"


@tool
def download_file(url: str) -> str:
    """Download a file (PDF, etc.) from URL and save locally. Returns the local file path."""
    import hashlib
    from pathlib import Path

    import requests

    try:
        # Create downloads directory
        downloads_dir = Path("downloads")
        downloads_dir.mkdir(exist_ok=True)

        # Generate filename from URL hash + extension
        ext = Path(url).suffix or ".bin"
        filename = hashlib.md5(url.encode()).hexdigest()[:12] + ext
        filepath = downloads_dir / filename

        # Download if not already cached
        if not filepath.exists():
            response = requests.get(url, timeout=60)
            response.raise_for_status()
            filepath.write_bytes(response.content)

        return f"Downloaded to: {filepath}"

    except Exception as e:
        return f"Error downloading: {e}"


@tool
def read_pdf(file_path: str) -> str:
    """Read and extract text from a local PDF file."""
    try:
        from pypdf import PdfReader

        reader = PdfReader(file_path)
        text = "\n".join(page.extract_text() or "" for page in reader.pages)
        return text[:15000]  # Limit to 15k chars
    except Exception as e:
        return f"Error reading PDF: {e}"


@tool
def fetch_webpage(url: str) -> str:
    """Fetch and read content from a webpage URL. For PDFs, use download_file then read_pdf instead."""
    import requests

    # Reject PDF URLs
    if url.lower().endswith(".pdf"):
        return "Error: This is a PDF file. Use download_file(url) first, then read_pdf(filepath) to read it."

    try:
        headers = {
            "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"
        }
        response = requests.get(url, timeout=30, headers=headers)
        response.raise_for_status()

        if "application/pdf" in response.headers.get("content-type", ""):
            return "Error: This is a PDF file. Use download_file(url) first, then read_pdf(filepath) to read it."

        import html2text

        h = html2text.HTML2Text()
        h.ignore_links = False
        h.ignore_images = True
        h.ignore_emphasis = False
        h.body_width = 0  # No wrapping

        markdown = h.handle(response.text)

        return markdown[:10000] if markdown else "No content found"

    except Exception as e:
        return f"Error fetching URL: {e}"


@tool
def transcribe_audio(file_path: str) -> str:
    """Transcribe an audio file to text using OpenAI Whisper.

    Args:
        file_path: Path to the audio file (mp3, wav)."""
    from openai import OpenAI

    client = OpenAI()

    with open(file_path, "rb") as audio_file:
        transcription = client.audio.transcriptions.create(
            model="whisper-1",
            file=audio_file,
        )

    print(f"[TRANSCRIPTION]: {transcription.text}")
    return transcription.text


@tool
def youtube_transcript_tool(video_url: str) -> List[Document]:
    """Fetch the transcript of a YouTube video given its URL."""

    from langchain_community.document_loaders import YoutubeLoader

    loader = YoutubeLoader.from_youtube_url(video_url, add_video_info=False)

    return loader.load()