# Purpose: One Space that offers four tools/tabs (all exposed as MCP tools):
#   1) Fetch — extract relevant page content (title, metadata, clean text, hyperlinks)
#   2) DuckDuckGo Search — compact JSONL search output (short keys to minimize tokens)
#   3) Python Code Executor — run Python code and capture stdout/errors
#   4) Kokoro TTS — synthesize speech from text using Kokoro-82M

from __future__ import annotations

import re
import json
import sys
import os
import random
from io import StringIO
from typing import List, Dict, Tuple, Annotated

import gradio as gr
import requests
from bs4 import BeautifulSoup
from readability import Document
from urllib.parse import urljoin, urldefrag, urlparse
from duckduckgo_search import DDGS
from PIL import Image
from huggingface_hub import InferenceClient
import time

# Optional imports for Kokoro TTS (loaded lazily)
import numpy as np
try:
    import torch  # type: ignore
except Exception:  # pragma: no cover - optional dependency
    torch = None  # type: ignore
try:
    from kokoro import KModel, KPipeline  # type: ignore
except Exception:  # pragma: no cover - optional dependency
    KModel = None  # type: ignore
    KPipeline = None  # type: ignore


# ==============================
# Fetch: HTTP + extraction utils
# ==============================

def _http_get(url: str) -> requests.Response:
    """
    Download the page politely with a short timeout and realistic headers.
    (Layman's terms: grab the web page like a normal browser would, but quickly.)
    """
    headers = {
        "User-Agent": "Mozilla/5.0 (compatible; WebMCP/1.0; +https://example.com)",
        "Accept-Language": "en-US,en;q=0.9",
        "Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8",
    }
    return requests.get(url, headers=headers, timeout=15)


def _normalize_whitespace(text: str) -> str:
    """
    Squeeze extra spaces and blank lines to keep things compact.
    (Layman's terms: tidy up the text so it’s not full of weird spacing.)
    """
    text = re.sub(r"[ \t\u00A0]+", " ", text)
    text = re.sub(r"\n\s*\n\s*\n+", "\n\n", text.strip())
    return text.strip()


def _truncate(text: str, max_chars: int) -> Tuple[str, bool]:
    """
    Cut text if it gets too long; return the text and whether we trimmed.
    (Layman's terms: shorten long text and tell us if we had to cut it.)
    """
    if max_chars is None or max_chars <= 0 or len(text) <= max_chars:
        return text, False
    return text[:max_chars].rstrip() + " …", True


def _shorten(text: str, limit: int) -> str:
    """
    Hard cap a string with an ellipsis to keep tokens small.
    (Layman's terms: force a string to a max length with an ellipsis.)
    """
    if limit <= 0 or len(text) <= limit:
        return text
    return text[: max(0, limit - 1)].rstrip() + "…"


def _domain_of(url: str) -> str:
    """
    Show a friendly site name like "example.com".
    (Layman's terms: pull the website's domain.)
    """
    try:
        return urlparse(url).netloc or ""
    except Exception:
        return ""


def _meta(soup: BeautifulSoup, name: str) -> str | None:
    tag = soup.find("meta", attrs={"name": name})
    return tag.get("content") if tag and tag.has_attr("content") else None


def _og(soup: BeautifulSoup, prop: str) -> str | None:
    tag = soup.find("meta", attrs={"property": prop})
    return tag.get("content") if tag and tag.has_attr("content") else None


def _extract_metadata(soup: BeautifulSoup, final_url: str) -> Dict[str, str]:
    """
    Pull the useful bits: title, description, site name, canonical URL, language, etc.
    (Layman's terms: gather page basics like title/description/address.)
    """
    meta: Dict[str, str] = {}

    # Title preference: <title> > og:title > twitter:title
    title_candidates = [
        (soup.title.string if soup.title and soup.title.string else None),
        _og(soup, "og:title"),
        _meta(soup, "twitter:title"),
    ]
    meta["title"] = next((t.strip() for t in title_candidates if t and t.strip()), "")

    # Description preference: description > og:description > twitter:description
    desc_candidates = [
        _meta(soup, "description"),
        _og(soup, "og:description"),
        _meta(soup, "twitter:description"),
    ]
    meta["description"] = next((d.strip() for d in desc_candidates if d and d.strip()), "")

    # Canonical link (helps dedupe)
    link_canonical = soup.find("link", rel=lambda v: v and "canonical" in v)
    meta["canonical"] = (link_canonical.get("href") or "").strip() if link_canonical else ""

    # Site name + language info if present
    meta["site_name"] = (_og(soup, "og:site_name") or "").strip()
    html_tag = soup.find("html")
    meta["lang"] = (html_tag.get("lang") or "").strip() if html_tag else ""

    # Final URL + domain
    meta["fetched_url"] = final_url
    meta["domain"] = _domain_of(final_url)

    return meta


def _extract_main_text(html: str) -> Tuple[str, BeautifulSoup]:
    """
    Use Readability to isolate the main article and turn it into clean text.
    Returns (clean_text, soup_of_readable_html).
    (Layman's terms: find the real article text and clean it.)
    """
    # Simplified article HTML from Readability
    doc = Document(html)
    readable_html = doc.summary(html_partial=True)

    # Parse simplified HTML
    s = BeautifulSoup(readable_html, "lxml")

    # Remove noisy tags
    for sel in ["script", "style", "noscript", "iframe", "svg"]:
        for tag in s.select(sel):
            tag.decompose()

    # Keep paragraphs, list items, and subheadings for structure without bloat
    text_parts: List[str] = []
    for p in s.find_all(["p", "li", "h2", "h3", "h4", "blockquote"]):
        chunk = p.get_text(" ", strip=True)
        if chunk:
            text_parts.append(chunk)

    clean_text = _normalize_whitespace("\n\n".join(text_parts))
    return clean_text, s


def _extract_links(readable_soup: BeautifulSoup, base_url: str, max_links: int) -> List[Tuple[str, str]]:
    """
    Collect clean, unique, absolute links from the readable section only.
    (Layman's terms: pull a tidy list of links from the article body.)
    """
    seen = set()
    links: List[Tuple[str, str]] = []

    for a in readable_soup.find_all("a", href=True):
        href = a.get("href").strip()
        # Skip junk links we can't use
        if not href or href.startswith("#") or href.startswith("mailto:") or href.startswith("javascript:"):
            continue

        # Resolve relative URLs, strip fragments (#…)
        absolute = urljoin(base_url, href)
        absolute, _ = urldefrag(absolute)

        if absolute in seen:
            continue
        seen.add(absolute)

        text = a.get_text(" ", strip=True)
        if len(text) > 120:
            text = text[:117] + "…"

        links.append((text or absolute, absolute))

        if len(links) >= max_links > 0:
            break

    return links


def _format_markdown(
    meta: Dict[str, str],
    body: str,
    body_truncated: bool,
    links: List[Tuple[str, str]],
    include_text: bool,
    include_metadata: bool,
    include_links: bool,
    verbosity: str,
) -> str:
    """
    Assemble a compact Markdown summary with optional sections.
    (Layman's terms: build the final markdown output with options.)
    """
    lines: List[str] = []

    # Title header
    title = meta.get("title") or meta.get("domain") or "Untitled"
    lines.append(f"# {title}")

    # Metadata section (only show what exists)
    if include_metadata:
        md: List[str] = []
        if meta.get("description"):
            md.append(f"- **Description:** {meta['description']}")
        if meta.get("site_name"):
            md.append(f"- **Site:** {meta['site_name']}")
        if meta.get("canonical"):
            md.append(f"- **Canonical:** {meta['canonical']}")
        if meta.get("lang"):
            md.append(f"- **Language:** {meta['lang']}")
        if meta.get("fetched_url"):
            md.append(f"- **Fetched From:** {meta['fetched_url']}")
        if md:
            lines.append("## Metadata")
            lines.extend(md)

    # Body text
    if include_text and body:
        if verbosity == "Brief":
            brief, was_more = _truncate(body, 800)
            lines.append("## Text")
            lines.append(brief)
            if was_more or body_truncated:
                lines.append("\n> (Trimmed for brevity)")
        else:
            lines.append("## Text")
            lines.append(body)
            if body_truncated:
                lines.append("\n> (Trimmed for brevity)")

    # Links section
    if include_links and links:
        lines.append(f"## Links ({len(links)})")
        for text, url in links:
            lines.append(f"- [{text}]({url})")

    return "\n\n".join(lines).strip()


def Fetch_Webpage(  # <-- MCP tool #1 (Fetch)
    url: Annotated[str, "The absolute URL to fetch (must return HTML)."] ,
    verbosity: Annotated[str, "Controls body length: one of 'Brief', 'Standard', or 'Full'."] = "Standard",
    include_metadata: Annotated[bool, "Include a Metadata section (description, site name, canonical, lang, fetched URL)."] = True,
    include_text: Annotated[bool, "Include the readable main text extracted with Readability."] = True,
    include_links: Annotated[bool, "Include outbound links discovered in the readable section."] = True,
    max_chars: Annotated[int, "Hard cap for body characters after the verbosity preset. Use 0 to disable the cap."] = 3000,
    max_links: Annotated[int, "Maximum number of links to include from the readable content. Set 0 to omit links."] = 20,
) -> str:
    """
    Fetch a web page and return a compact Markdown summary containing title, key
    metadata, readable main text, and outbound links.

    Args:
        url: The absolute URL to fetch (must return HTML).
        verbosity: Controls body length: one of 'Brief', 'Standard', or 'Full'.
        include_metadata: Include a Metadata section (description, site name, canonical, lang, fetched URL).
        include_text: Include the readable main text extracted with Readability.
        include_links: Include outbound links discovered in the readable section.
        max_chars: Hard cap for body characters after the verbosity preset. Use 0 to disable the cap.
        max_links: Maximum number of links to include from the readable content. Set 0 to omit links.

    Returns:
        str: Markdown that may contain the following sections:
            - Title (H1)
            - Metadata (optional)
            - Text (optional, may be trimmed)
            - Links (optional, deduped and absolute)
    """
    if not url or not url.strip():
        return "Please enter a valid URL."

    try:
        resp = _http_get(url)
        resp.raise_for_status()
    except requests.exceptions.RequestException as e:
        return f"An error occurred: {e}"

    final_url = str(resp.url)
    ctype = resp.headers.get("Content-Type", "")
    if "html" not in ctype.lower():
        return f"Unsupported content type for extraction: {ctype or 'unknown'}"

    # Decode to text
    resp.encoding = resp.encoding or resp.apparent_encoding
    html = resp.text

    # Full-page soup for metadata
    full_soup = BeautifulSoup(html, "lxml")
    meta = _extract_metadata(full_soup, final_url)

    # Readable content
    body_text, readable_soup = _extract_main_text(html)
    if not body_text:
        # Fallback to "whole-page text" if Readability found nothing
        fallback_text = full_soup.get_text(" ", strip=True)
        body_text = _normalize_whitespace(fallback_text)

    # Verbosity presets (we keep the smaller of preset vs. user cap)
    preset_caps = {"Brief": 1200, "Standard": 3000, "Full": 999_999}
    target_cap = preset_caps.get(verbosity, 3000)
    cap = min(max_chars if max_chars > 0 else target_cap, target_cap)
    body_text, truncated = _truncate(body_text, cap) if include_text else ("", False)

    # Extract links from the simplified content only
    links = _extract_links(readable_soup, final_url, max_links=max_links if include_links else 0)

    # Final compact Markdown
    md = _format_markdown(
        meta=meta,
        body=body_text,
        body_truncated=truncated,
        links=links,
        include_text=include_text,
        include_metadata=include_metadata,
        include_links=include_links,
        verbosity=verbosity,
    )
    return md or "No content could be extracted."


# ============================================
# DuckDuckGo Search: ultra-succinct JSONL
# ============================================

def Search_DuckDuckGo(  # <-- MCP tool #2 (DDG Search)
    query: Annotated[str, "The search query (supports operators like site:, quotes, OR)."] ,
    max_results: Annotated[int, "Number of results to return (1–20)."] = 5,
    include_snippets: Annotated[bool, "Include a short snippet for each result (adds tokens)."] = False,
    max_snippet_chars: Annotated[int, "Character cap applied to each snippet when included."] = 80,
    dedupe_domains: Annotated[bool, "If true, only the first result from each domain is kept."] = True,
    title_chars: Annotated[int, "Character cap applied to titles."] = 80,
) -> str:
    """
    Run a DuckDuckGo search and return ultra-compact JSONL with short keys to
    minimize tokens.

    Args:
        query: The search query (supports operators like site:, quotes, OR).
        max_results: Number of results to return (1–20).
        include_snippets: Include a short snippet for each result (adds tokens).
        max_snippet_chars: Character cap applied to each snippet when included.
        dedupe_domains: If true, only the first result from each domain is kept.
        title_chars: Character cap applied to titles.

    Returns:
        str: Newline-delimited JSON (JSONL). Each line has:
            {"t": "title", "u": "url"[, "s": "snippet"]}
    """
    if not query or not query.strip():
        return ""

    try:
        with DDGS() as ddgs:
            raw = ddgs.text(query, max_results=max_results)
    except Exception as e:
        return json.dumps({"error": str(e)[:120]}, ensure_ascii=False, separators=(",", ":"))

    seen_domains = set()
    lines: List[str] = []

    for r in raw or []:
        title = _shorten((r.get("title") or "").strip(), title_chars)
        url = (r.get("href") or r.get("link") or "").strip()
        body = (r.get("body") or r.get("snippet") or "").strip()

        if not url:
            continue

        if dedupe_domains:
            dom = _domain_of(url)
            if dom in seen_domains:
                continue
            seen_domains.add(dom)

        obj = {"t": title or _domain_of(url), "u": url}

        if include_snippets and body:
            obj["s"] = _shorten(body, max_snippet_chars)

        # Emit most compact JSON possible (no spaces)
        lines.append(json.dumps(obj, ensure_ascii=False, separators=(",", ":")))

    # Join as JSONL (each result on its own line)
    return "\n".join(lines)


# ======================================
# Code Execution: Python (MCP tool #3)
# ======================================

def Execute_Python(code: Annotated[str, "Python source code to run; stdout is captured and returned."]) -> str:
    """
    Execute arbitrary Python code and return captured stdout or an error message.

    Args:
        code: Python source code to run; stdout is captured and returned.

    Returns:
        str: Combined stdout produced by the code, or the exception text if
        execution failed.
    """
    if code is None:
        return "No code provided."

    old_stdout = sys.stdout
    redirected_output = sys.stdout = StringIO()
    try:
        exec(code)
        return redirected_output.getvalue()
    except Exception as e:
        return str(e)
    finally:
        sys.stdout = old_stdout


# ==========================
# Kokoro TTS (MCP tool #4)
# ==========================

_KOKORO_STATE = {
    "initialized": False,
    "device": "cpu",
    "model": None,
    "pipelines": {},
}


def _init_kokoro() -> None:
    """Lazy-initialize Kokoro model and pipelines on first use.

    Tries CUDA if torch is present and available; falls back to CPU. Keeps a
    minimal English pipeline and custom lexicon tweak for the word "kokoro".
    """
    if _KOKORO_STATE["initialized"]:
        return

    if KModel is None or KPipeline is None:
        raise RuntimeError(
            "Kokoro is not installed. Please install the 'kokoro' package (>=0.9.4)."
        )

    device = "cpu"
    if torch is not None:
        try:
            if torch.cuda.is_available():  # type: ignore[attr-defined]
                device = "cuda"
        except Exception:
            device = "cpu"

    model = KModel().to(device).eval()
    pipelines = {"a": KPipeline(lang_code="a", model=False)}
    # Custom pronunciation
    try:
        pipelines["a"].g2p.lexicon.golds["kokoro"] = "kˈOkəɹO"
    except Exception:
        pass

    _KOKORO_STATE.update(
        {
            "initialized": True,
            "device": device,
            "model": model,
            "pipelines": pipelines,
        }
    )


def Generate_Speech(  # <-- MCP tool #4 (Generate Speech)
    text: Annotated[str, "The text to synthesize (English)."],
    speed: Annotated[float, "Speech speed multiplier in 0.5–2.0; 1.0 = normal speed."] = 1.0,
    voice: Annotated[str, "Voice identifier. Example: 'af_heart' (US English, female, Heart)."] = "af_heart",
) -> Tuple[int, np.ndarray]:
    """
    Synthesize speech from text using the Kokoro-82M model.

    This function returns raw audio suitable for a Gradio Audio component and is
    also exposed as an MCP tool (per the latest Hugging Face/Gradio MCP docs, a
    tool is created for each function wired into your app; docstrings and type
    hints are used to describe the tool).

    Args:
        text: The text to synthesize (English).
        speed: Speech speed multiplier in 0.5–2.0; 1.0 = normal speed.
        voice: Voice identifier. Example: 'af_heart' (US English, female, Heart).

    Returns:
        A tuple of (sample_rate_hz, audio_waveform) where:
        - sample_rate_hz: int sample rate in Hz (24_000)
        - audio_waveform: numpy.ndarray float32 mono waveform in range [-1, 1]

    Notes:
        - Requires the 'kokoro' package (>=0.9.4). If unavailable, an error is
          raised with installation guidance.
        - Runs on CUDA if available; otherwise CPU.
    """
    if not text or not text.strip():
        raise gr.Error("Please provide non-empty text to synthesize.")

    _init_kokoro()
    model = _KOKORO_STATE["model"]
    pipelines = _KOKORO_STATE["pipelines"]

    pipeline = pipelines.get("a")
    if pipeline is None:
        raise gr.Error("Kokoro English pipeline not initialized.")

    pack = pipeline.load_voice(voice)
    # Generate using the last reference state from the current phoneme sequence
    for _, ps, _ in pipeline(text, voice, speed):
        ref_s = pack[len(ps) - 1]
        try:
            audio = model(ps, ref_s, float(speed))
        except Exception as e:  # propagate as UI-friendly error
            raise gr.Error(f"Error generating audio: {str(e)}")
        # Return 24 kHz mono waveform
        return 24_000, audio.detach().cpu().numpy()

    # If pipeline produced no segments
    raise gr.Error("No audio was generated (empty synthesis result).")


# ======================
# UI: four-tab interface
# ======================

# --- Fetch tab (compact controllable extraction) ---
fetch_interface = gr.Interface(
    fn=Fetch_Webpage,  # connect the function to the UI
    inputs=[
        gr.Textbox(label="URL", placeholder="https://example.com/article"),
        gr.Dropdown(label="Verbosity", choices=["Brief", "Standard", "Full"], value="Standard"),
        gr.Checkbox(value=True, label="Include Metadata"),
        gr.Checkbox(value=True, label="Include Main Text"),
        gr.Checkbox(value=True, label="Include Links"),
        gr.Slider(400, 12000, value=3000, step=100, label="Max Characters (body text)"),
        gr.Slider(0, 100, value=20, step=1, label="Max Links"),
    ],
    outputs=gr.Markdown(label="Extracted Summary"),
    title="Fetch Webpage",
    description=(
        "<div style=\"text-align:center\">Extract title, key metadata, readable text, and links from webpages. No noisy HTML.</div>"
    ),
    api_description=(
        "Fetch a web page and return a compact Markdown summary with title, key "
        "metadata, readable body text, and outbound links. Parameters let you "
        "control verbosity, whether to include metadata/text/links, and limits "
        "for characters and number of links."
    ),
    allow_flagging="never",
)

# --- Concise DDG tab (JSONL with short keys, minimal tokens) ---
concise_interface = gr.Interface(
    fn=Search_DuckDuckGo,
    inputs=[
        gr.Textbox(label="Query", placeholder="topic OR site:example.com"),
        gr.Slider(minimum=1, maximum=20, value=5, step=1, label="Max results"),
        gr.Checkbox(value=False, label="Include snippets (adds tokens)"),
        gr.Slider(minimum=20, maximum=200, value=80, step=5, label="Max snippet chars"),
        gr.Checkbox(value=True, label="Dedupe by domain"),
        gr.Slider(minimum=20, maximum=120, value=80, step=5, label="Max title chars"),
    ],
    outputs=gr.Textbox(label="Results (JSONL)", interactive=False),
    title="DuckDuckGo Search",
    description=(
        "<div style=\"text-align:center\">Very concise web search to avoid unnecessary context. Emits JSONL with short keys (t,u[,s]). Defaults avoid snippets and duplicate domains.</div>"
    ),
    api_description=(
        "Run a DuckDuckGo search and return newline-delimited JSON with short keys: "
        "t=title, u=url, optional s=snippet. Options control result count, "
        "snippet inclusion and length, domain deduping, and title length."
    ),
    allow_flagging="never",
    submit_btn="Search",
)

##

# --- Execute Python tab (simple code interpreter) ---
code_interface = gr.Interface(
    fn=Execute_Python,
    inputs=gr.Code(label="Python Code", language="python"),
    outputs=gr.Textbox(label="Output"),
    title="Python Code Executor",
    description=(
        "<div style=\"text-align:center\">Execute Python code and see the output.</div>"
    ),
    api_description=(
        "Execute arbitrary Python code and return captured stdout or an error message.\n\n"
        "Parameters:\n"
        "- code (string): The Python source code to run.\n\n"
        "Returns:\n"
        "- string: Combined stdout produced by the code, or the exception text if execution failed."
    ),
    allow_flagging="never",
)

CSS_STYLES = """
    .gradio-container h1 {
        text-align: center;
    }
    /* Default: add subtitle under titles */
    .gradio-container h1::after {
    content: "Fetch Webpage | Search DuckDuckGo | Code Interpreter | Kokoro TTS | Image Generation | Video Generation";
        display: block;
        font-size: 1rem;
        font-weight: 500;
        opacity: 0.9;
        margin-top: 6px;
    }

    /* But remove it inside tab panels so it doesn't duplicate under each tool title */
    .gradio-container [role=\"tabpanel\"] h1::after {
        content: none !important;
    }
"""

# --- Kokoro TTS tab (text to speech) ---
kokoro_interface = gr.Interface(
    fn=Generate_Speech,
    inputs=[
        gr.Textbox(label="Text", placeholder="Type text to synthesize…", lines=4),
        gr.Slider(minimum=0.5, maximum=2.0, value=1.0, step=0.1, label="Speed"),
        gr.Textbox(label="Voice", value="af_heart", placeholder="e.g., af_heart"),
    ],
    outputs=gr.Audio(label="Audio", type="numpy"),
    title="Kokoro TTS",
    description=(
        "<div style=\"text-align:center\">Generate English speech with Kokoro-82M. 30 second max output. Runs on CPU or CUDA if available.</div>"
    ),
    api_description=(
        "Synthesize speech from text using Kokoro-82M. Returns (sample_rate, waveform) suitable for playback. "
        "Parameters: text (str), speed (float 0.5–2.0), voice (str). "
        "Return the generated image to the user."
    ),
    allow_flagging="never",
)

# ==========================
# Image Generation (Serverless)
# ==========================

HF_API_TOKEN = os.getenv("HF_READ_TOKEN")


def Generate_Image(  # <-- MCP tool #5 (Generate Image)
    prompt: Annotated[str, "Text description of the image to generate."],
    model_id: Annotated[str, "Hugging Face model id in the form 'creator/model-name' (e.g., black-forest-labs/FLUX.1-Krea-dev)."] = "black-forest-labs/FLUX.1-Krea-dev",
    negative_prompt: Annotated[str, "What should NOT appear in the image." ] = (
        "(deformed, distorted, disfigured), poorly drawn, bad anatomy, wrong anatomy, extra limb, "
        "missing limb, floating limbs, (mutated hands and fingers), disconnected limbs, mutation, "
        "mutated, ugly, disgusting, blurry, amputation, misspellings, typos"
    ),
    steps: Annotated[int, "Number of denoising steps (1–100). Higher = slower, potentially higher quality."] = 35,
    cfg_scale: Annotated[float, "Classifier-free guidance scale (1–20). Higher = follow the prompt more closely."] = 7.0,
    sampler: Annotated[str, "Sampling method label (UI only). Common options: 'DPM++ 2M Karras', 'DPM++ SDE Karras', 'Euler', 'Euler a', 'Heun', 'DDIM'."] = "DPM++ 2M Karras",
    seed: Annotated[int, "Random seed for reproducibility. Use -1 for a random seed per call."] = -1,
    width: Annotated[int, "Output width in pixels (64–1216, multiple of 32 recommended)."] = 1024,
    height: Annotated[int, "Output height in pixels (64–1216, multiple of 32 recommended)."] = 1024,
) -> Image.Image:
    """
    Generate a single image from a text prompt using a Hugging Face model via
    serverless Inference. Returns a PIL image. By default, the model is
    black-forest-labs/FLUX.1-Krea-dev.

    Notes (MCP):
    - Per the latest Gradio MCP docs, images returned from tools are handled by the server and
      converted to file URLs automatically for MCP clients. Ensure type hints and this docstring
      "Args:" block are present so the tool schema is accurate.

    Args:
        prompt (str): Text description of the image to generate.
        model_id (str): The Hugging Face model id (creator/model-name). Defaults to "black-forest-labs/FLUX.1-Krea-dev".
        negative_prompt (str): What should NOT appear in the image.
        steps (int): Number of denoising steps (1–100). Higher can improve quality.
        cfg_scale (float): Guidance scale (1–20). Higher = follow the prompt more closely.
        sampler (str): Sampling method label for UI; not all providers expose this control.
        seed (int): Random seed. Use -1 to randomize on each call.
        width (int): Output width in pixels (64–1216; multiples of 32 recommended).
        height (int): Output height in pixels (64–1216; multiples of 32 recommended).

    Returns:
        PIL.Image.Image: The generated image.

    Error modes:
        - Raises gr.Error with a user-friendly message on auth/model/load errors.
    """
    if not prompt or not prompt.strip():
        raise gr.Error("Please provide a non-empty prompt.")

    # Slightly enhance prompt for quality (kept consistent with Serverless space)
    enhanced_prompt = f"{prompt} | ultra detail, ultra elaboration, ultra quality, perfect."

    # Try multiple providers for resilience
    providers = ["auto", "replicate", "fal-ai"]
    last_error: Exception | None = None

    for provider in providers:
        try:
            client = InferenceClient(api_key=HF_API_TOKEN, provider=provider)
            image = client.text_to_image(
                prompt=enhanced_prompt,
                negative_prompt=negative_prompt,
                model=model_id,
                width=width,
                height=height,
                num_inference_steps=steps,
                guidance_scale=cfg_scale,
                seed=seed if seed != -1 else random.randint(1, 1_000_000_000),
            )
            return image
        except Exception as e:  # try next provider, transform last one to friendly error
            last_error = e
            continue

    # If we reach here, all providers failed
    msg = str(last_error) if last_error else "Unknown error"
    if "404" in msg:
        raise gr.Error(f"Model not found or unavailable: {model_id}. Check the id and your HF token access.")
    if "503" in msg:
        raise gr.Error("The model is warming up. Please try again shortly.")
    if "401" in msg or "403" in msg:
        raise gr.Error("Authentication failed. Set HF_READ_TOKEN environment variable with access to the model.")
    raise gr.Error(f"Image generation failed: {msg}")


image_generation_interface = gr.Interface(
    fn=Generate_Image,
    inputs=[
        gr.Textbox(label="Prompt", placeholder="Enter a prompt", lines=2),
        gr.Textbox(label="Model", value="black-forest-labs/FLUX.1-Krea-dev", placeholder="creator/model-name"),
        gr.Textbox(
            label="Negative Prompt",
            value=(
                "(deformed, distorted, disfigured), poorly drawn, bad anatomy, wrong anatomy, extra limb, "
                "missing limb, floating limbs, (mutated hands and fingers), disconnected limbs, mutation, "
                "mutated, ugly, disgusting, blurry, amputation, misspellings, typos"
            ),
            lines=2,
        ),
        gr.Slider(minimum=1, maximum=100, value=35, step=1, label="Steps"),
        gr.Slider(minimum=1.0, maximum=20.0, value=7.0, step=0.1, label="CFG Scale"),
        gr.Radio(label="Sampler", value="DPM++ 2M Karras", choices=[
            "DPM++ 2M Karras", "DPM++ SDE Karras", "Euler", "Euler a", "Heun", "DDIM"
        ]),
        gr.Slider(minimum=-1, maximum=1_000_000_000, value=-1, step=1, label="Seed (-1 = random)"),
        gr.Slider(minimum=64, maximum=1216, value=1024, step=32, label="Width"),
        gr.Slider(minimum=64, maximum=1216, value=1024, step=32, label="Height"),
    ],
    outputs=gr.Image(label="Generated Image"),
    title="Image Generation",
    description=(
        "<div style=\"text-align:center\">Generate images via Hugging Face Inference. "
        "Default model is FLUX.1-Krea</div>"
    ),
    api_description=(
        "Generate a single image from a text prompt using a Hugging Face model (serverless Inference). "
        "Parameters: prompt (str), model_id (str, creator/model-name), negative_prompt (str), steps (int, 1–100), cfg_scale (float, 1–20), "
        "sampler (str, label only), seed (int, -1=random), width/height (int, 64–1216). Returns a PIL.Image. "
        "Return the generated image to the user in this format `![Alt text](URL)`"
    ),
    allow_flagging="never",
)

# ==========================
# Video Generation (Serverless)
# ==========================

def _write_video_tmp(data_iter_or_bytes: object, suffix: str = ".mp4") -> str:
    """Write video bytes or iterable of bytes to a temporary file and return its path."""
    os.makedirs("outputs", exist_ok=True)
    fname = f"outputs/video_{int(time.time())}_{random.randint(1000,9999)}{suffix}"
    mode = "wb"
    with open(fname, mode) as f:
        # bytes-like
        if isinstance(data_iter_or_bytes, (bytes, bytearray)):
            f.write(data_iter_or_bytes)  # type: ignore[arg-type]
        # file-like with read()
        elif hasattr(data_iter_or_bytes, "read"):
            f.write(data_iter_or_bytes.read())  # type: ignore[call-arg]
        # response-like with content
        elif hasattr(data_iter_or_bytes, "content"):
            f.write(data_iter_or_bytes.content)  # type: ignore[attr-defined]
        # iterable of chunks
        elif hasattr(data_iter_or_bytes, "__iter__") and not isinstance(data_iter_or_bytes, (str, dict)):
            for chunk in data_iter_or_bytes:  # type: ignore[assignment]
                if chunk:
                    f.write(chunk)
        else:
            raise gr.Error("Unsupported video data type returned by provider.")
    return fname


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


def Generate_Video(  # <-- MCP tool #6 (Generate Video)
    prompt: Annotated[str, "Text description of the video to generate (e.g., 'a red fox running through a snowy forest at sunrise')."],
    model_id: Annotated[str, "Hugging Face model id in the form 'creator/model-name'. Defaults to Wan-AI/Wan2.2-T2V-A14B."] = "Wan-AI/Wan2.2-T2V-A14B",
    negative_prompt: Annotated[str, "What should NOT appear in the video."] = "",
    steps: Annotated[int, "Number of denoising steps (1–100). Higher can improve quality but is slower."] = 25,
    cfg_scale: Annotated[float, "Guidance scale (1–20). Higher = follow the prompt more closely, lower = more creative."] = 3.5,
    seed: Annotated[int, "Random seed for reproducibility. Use -1 for a random seed per call."] = -1,
    width: Annotated[int, "Output width in pixels (multiples of 8 recommended)."] = 768,
    height: Annotated[int, "Output height in pixels (multiples of 8 recommended)."] = 768,
    fps: Annotated[int, "Frames per second of the output video (e.g., 24)."] = 24,
    duration: Annotated[float, "Target duration in seconds (provider/model dependent, commonly 2–6s)."] = 4.0,
) -> str:
    """
    Generate a short video from a text prompt using Hugging Face Inference Providers (Serverless Inference).

    This tool follows the latest MCP guidance for Gradio-based MCP servers: clear type hints and
    docstrings define the tool schema automatically. The returned file path will be converted to a file URL
    for MCP clients.

    Args:
        prompt (str): Text description of the video to generate.
    model_id (str): The Hugging Face model id (creator/model-name). Defaults to "Wan-AI/Wan2.2-T2V-A14B".
        negative_prompt (str): What should NOT appear in the video.
        steps (int): Number of denoising steps (1–100). Higher can improve quality but is slower.
        cfg_scale (float): Guidance scale (1–20). Higher = follow the prompt more closely.
        seed (int): Random seed. Use -1 to randomize on each call.
        width (int): Output width in pixels.
        height (int): Output height in pixels.
        fps (int): Frames per second.
        duration (float): Target duration in seconds.

    Returns:
        str: Path to an MP4 file on disk (Gradio will serve this file; MCP converts it to a file URL).

    Error modes:
        - Raises gr.Error with a user-friendly message on auth/model/load errors or unsupported parameters.
    """
    if not prompt or not prompt.strip():
        raise gr.Error("Please provide a non-empty prompt.")

    if not HF_VIDEO_TOKEN:
        # Still attempt without a token (public models), but warn earlier if it fails.
        pass

    providers = ["auto", "replicate", "fal-ai"]
    last_error: Exception | None = None

    # Build a common parameters dict. Providers may ignore unsupported keys.
    parameters = {
        "negative_prompt": negative_prompt or None,
        "num_inference_steps": steps,
        "guidance_scale": cfg_scale,
        "seed": seed if seed != -1 else random.randint(1, 1_000_000_000),
        "width": width,
        "height": height,
        "fps": fps,
        # Some providers/models expect num_frames instead of duration; we pass both-friendly value
        # when supported; they may be ignored by the backend.
        "duration": duration,
    }

    for provider in providers:
        try:
            client = InferenceClient(api_key=HF_VIDEO_TOKEN, provider=provider)
            # Use the documented text_to_video API with correct parameters
            if hasattr(client, "text_to_video"):
                # Calculate num_frames from duration and fps if both provided
                num_frames = int(duration * fps) if duration and fps else None
                
                # Build extra_body for provider-specific parameters
                extra_body = {}
                if width:
                    extra_body["width"] = width
                if height:
                    extra_body["height"] = height
                if fps:
                    extra_body["fps"] = fps
                if duration:
                    extra_body["duration"] = duration
                
                result = client.text_to_video(
                    prompt=prompt,
                    model=model_id,
                    guidance_scale=cfg_scale,
                    negative_prompt=[negative_prompt] if negative_prompt else None,
                    num_frames=num_frames,
                    num_inference_steps=steps,
                    seed=parameters["seed"],
                    extra_body=extra_body if extra_body else None,
                )
            else:
                # Generic POST fallback for older versions
                result = client.post(
                    model=model_id,
                    json={
                        "inputs": prompt,
                        "parameters": {k: v for k, v in parameters.items() if v is not None},
                    },
                )

            # Save output to an .mp4
            path = _write_video_tmp(result, suffix=".mp4")
            return path
        except Exception as e:
            last_error = e
            continue

    msg = str(last_error) if last_error else "Unknown error"
    if "404" in msg:
        raise gr.Error(f"Model not found or unavailable: {model_id}. Check the id and HF token access.")
    if "503" in msg:
        raise gr.Error("The model is warming up. Please try again shortly.")
    if "401" in msg or "403" in msg:
        raise gr.Error("Authentication failed or not permitted. Set HF_READ_TOKEN/HF_TOKEN with inference access.")
    raise gr.Error(f"Video generation failed: {msg}")


video_generation_interface = gr.Interface(
    fn=Generate_Video,
    inputs=[
        gr.Textbox(label="Prompt", placeholder="Enter a prompt for the video", lines=2),
    gr.Textbox(label="Model", value="Wan-AI/Wan2.2-T2V-A14B", placeholder="creator/model-name"),
        gr.Textbox(label="Negative Prompt", value="", lines=2),
        gr.Slider(minimum=1, maximum=100, value=25, step=1, label="Steps"),
        gr.Slider(minimum=1.0, maximum=20.0, value=3.5, step=0.1, label="CFG Scale"),
        gr.Slider(minimum=-1, maximum=1_000_000_000, value=-1, step=1, label="Seed (-1 = random)"),
        gr.Slider(minimum=64, maximum=1920, value=768, step=8, label="Width"),
        gr.Slider(minimum=64, maximum=1920, value=768, step=8, label="Height"),
        gr.Slider(minimum=4, maximum=60, value=24, step=1, label="FPS"),
        gr.Slider(minimum=1.0, maximum=10.0, value=4.0, step=0.5, label="Duration (s)"),
    ],
    outputs=gr.Video(label="Generated Video"),
    title="Video Generation",
    description=(
    "<div style=\"text-align:center\">Generate short videos via Hugging Face Inference Providers. "
    "Default model is Wan2.2-T2V-A14B.</div>"
    ),
    api_description=(
        "Generate a short video from a text prompt using a Hugging Face model (Serverless Inference). "
        "Parameters: prompt (str), model_id (str), negative_prompt (str), steps (int), cfg_scale (float), seed (int), "
        "width/height (int), fps (int), duration (float). Returns a file path to an MP4 that MCP exposes as a file URL."
    ),
    allow_flagging="never",
)

# Build tabbed app including Image Generation
demo = gr.TabbedInterface(
    interface_list=[
        fetch_interface,
        concise_interface,
        code_interface,
        kokoro_interface,
        image_generation_interface,
        video_generation_interface,
    ],
    tab_names=[
        "Fetch Webpage",
        "DuckDuckGo Search",
        "Python Code Executor",
        "Kokoro TTS",
        "Image Generation",
        "Video Generation",
    ],
    title="Tools MCP",
    theme="Nymbo/Nymbo_Theme",
    css=CSS_STYLES,
)

# Launch the UI and expose all functions as MCP tools in one server
if __name__ == "__main__":
    demo.launch(mcp_server=True)