# Purpose: One Space that offers six 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 with 54 voice options
# 5) Image Generation - HF serverless inference providers (Default: FLUX.1-Krea-dev)
# 6) Video Generation - HF serverless inference providers (Default: Wan2.2-T2V-A14B)
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 markdownify import markdownify as md
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:
> 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 _fullpage_markdown_from_soup(full_soup: BeautifulSoup, base_url: str) -> str:
"""
Convert the page's main content (or body fallback) to Markdown, similar to
web-scraper's Content Scraper tool, but without any file download side-effects.
Steps:
- Remove noisy elements (script/style/nav/footer/header/aside)
- Prefer , , or common content containers; fallback to
- Convert to Markdown with ATX headings
- Clean up excessive newlines, empty links, and whitespace
- Prepend a title header when available
"""
# Remove unwanted elements globally first
for element in full_soup.select("script, style, nav, footer, header, aside"):
element.decompose()
# Try common main-content containers, then fallback to body
main = (
full_soup.find("main")
or full_soup.find("article")
or full_soup.find("div", class_=re.compile(r"content|main|post|article", re.I))
or full_soup.find("body")
)
if not main:
return "No main content found on the webpage."
# Convert selected HTML to Markdown
markdown_text = md(str(main), heading_style="ATX")
# Clean up the markdown similar to web-scraper
markdown_text = re.sub(r"\n{3,}", "\n\n", markdown_text)
markdown_text = re.sub(r"\[\s*\]\([^)]*\)", "", markdown_text) # empty links
markdown_text = re.sub(r"[ \t]+", " ", markdown_text)
markdown_text = markdown_text.strip()
# Add title if present
title = full_soup.find("title")
if title and title.get_text(strip=True):
markdown_text = f"# {title.get_text(strip=True)}\n\n{markdown_text}"
return markdown_text or "No content could be extracted."
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,
full_page_markdown: Annotated[bool, "If true, return the page as full Markdown (Content Scraper mode) instead of a compact summary."] = False,
) -> 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.
full_page_markdown: If True, return the page converted to full Markdown (Content Scraper mode)
instead of the compact summary. This ignores verbosity/include_* and max_* limits and
attempts to convert the main content area to Markdown with headings preserved.
Returns:
str: Markdown that may contain the following sections:
- Title (H1)
- Metadata (optional)
- Text (optional, may be trimmed)
- Links (optional, deduped and absolute)
Special mode:
If full_page_markdown=True, the function returns the page converted to Markdown,
similar to the "Content Scraper" tool, ignoring verbosity/include_* limits.
"""
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 (and potential Markdown conversion)
full_soup = BeautifulSoup(html, "lxml")
meta = _extract_metadata(full_soup, final_url)
# Content Scraper mode: return full-page Markdown early
if full_page_markdown:
return _fullpage_markdown_from_soup(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 get_kokoro_voices():
"""Get comprehensive list of available Kokoro voice IDs (54 total)."""
try:
from huggingface_hub import list_repo_files
# Get voice files from the Kokoro repository
files = list_repo_files('hexgrad/Kokoro-82M')
voice_files = [f for f in files if f.endswith('.pt') and f.startswith('voices/')]
voices = [f.replace('voices/', '').replace('.pt', '') for f in voice_files]
return sorted(voices) if voices else _get_fallback_voices()
except Exception:
return _get_fallback_voices()
def _get_fallback_voices():
"""Return comprehensive fallback list of known Kokoro voices (54 total)."""
return [
# American Female (11 voices)
"af_alloy", "af_aoede", "af_bella", "af_heart", "af_jessica",
"af_kore", "af_nicole", "af_nova", "af_river", "af_sarah", "af_sky",
# American Male (9 voices)
"am_adam", "am_echo", "am_eric", "am_fenrir", "am_liam",
"am_michael", "am_onyx", "am_puck", "am_santa",
# British Female (4 voices)
"bf_alice", "bf_emma", "bf_isabella", "bf_lily",
# British Male (4 voices)
"bm_daniel", "bm_fable", "bm_george", "bm_lewis",
# European Female/Male (3 voices)
"ef_dora", "em_alex", "em_santa",
# French Female (1 voice)
"ff_siwis",
# Hindi Female/Male (4 voices)
"hf_alpha", "hf_beta", "hm_omega", "hm_psi",
# Italian Female/Male (2 voices)
"if_sara", "im_nicola",
# Japanese Female/Male (5 voices)
"jf_alpha", "jf_gongitsune", "jf_nezumi", "jf_tebukuro", "jm_kumo",
# Portuguese Female/Male (3 voices)
"pf_dora", "pm_alex", "pm_santa",
# Chinese Female/Male (8 voices)
"zf_xiaobei", "zf_xiaoni", "zf_xiaoxiao", "zf_xiaoyi",
"zm_yunjian", "zm_yunxi", "zm_yunxia", "zm_yunyang"
]
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 List_Kokoro_Voices() -> List[str]:
"""
Get a list of all available Kokoro voice identifiers.
This MCP tool helps clients discover the 54 available voice options
for the Generate_Speech tool.
Returns:
List[str]: A list of voice identifiers (e.g., ["af_heart", "am_adam", "bf_alice", ...])
Voice naming convention:
- First 2 letters: Language/Region (af=American Female, am=American Male, bf=British Female, etc.)
- Following letters: Voice name (heart, adam, alice, etc.)
Available categories:
- American Female/Male (20 voices)
- British Female/Male (8 voices)
- European Female/Male (3 voices)
- French Female (1 voice)
- Hindi Female/Male (4 voices)
- Italian Female/Male (2 voices)
- Japanese Female/Male (5 voices)
- Portuguese Female/Male (3 voices)
- Chinese Female/Male (8 voices)
"""
return get_kokoro_voices()
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."] = 1.25,
voice: Annotated[str, "Voice identifier from 54 available options. Use List_Kokoro_Voices() to see all choices. Examples: 'af_heart' (US female), 'am_adam' (US male), 'bf_alice' (British female), 'jf_alpha' (Japanese female)."] = "af_heart",
) -> Tuple[int, np.ndarray]:
"""
Synthesize speech from text using the Kokoro-82M model with 54 voice options.
This function returns raw audio suitable for a Gradio Audio component and is
also exposed as an MCP tool. It supports 54 different voices across multiple
languages and accents including American, British, European, Hindi, Italian,
Japanese, Portuguese, and Chinese speakers.
Enhanced for longer audio generation:
- Processes ALL text segments (not just the first one)
- Can generate audio of any length based on input text
- Concatenates multiple segments for seamless longer audio
Default behavior:
- Speed defaults to 1.25 (slightly brisk cadence) for clearer, snappier delivery.
- Voice defaults to "af_heart" (American Female, Heart voice)
Args:
text: The text to synthesize. Works best with English but supports multiple languages.
speed: Speech speed multiplier in 0.5–2.0; 1.0 = normal speed. Default: 1.25 (slightly brisk).
voice: Voice identifier from 54 available options. Use List_Kokoro_Voices() to see all choices. Default: 'af_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.
- Runs on CUDA if available; otherwise CPU.
- Supports 54 voices across 9 language/accent categories.
- Can generate audio of any length - no 30 second limit!
- Use List_Kokoro_Voices() MCP tool to discover all available voice options.
"""
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.")
# Process ALL segments for longer audio generation
audio_segments = []
pack = pipeline.load_voice(voice)
try:
# Get all segments first to show progress for long text
segments = list(pipeline(text, voice, speed))
total_segments = len(segments)
# Iterate through ALL segments instead of just the first one
for segment_idx, (text_chunk, ps, _) in enumerate(segments):
ref_s = pack[len(ps) - 1]
try:
audio = model(ps, ref_s, float(speed))
audio_segments.append(audio.detach().cpu().numpy())
# For very long text (>10 segments), show progress every few segments
if total_segments > 10 and (segment_idx + 1) % 5 == 0:
print(f"Progress: Generated {segment_idx + 1}/{total_segments} segments...")
except Exception as e:
raise gr.Error(f"Error generating audio for segment {segment_idx + 1}: {str(e)}")
if not audio_segments:
raise gr.Error("No audio was generated (empty synthesis result).")
# Concatenate all segments to create the complete audio
if len(audio_segments) == 1:
final_audio = audio_segments[0]
else:
final_audio = np.concatenate(audio_segments, axis=0)
# For multi-segment audio, provide completion info
duration = len(final_audio) / 24_000
if total_segments > 1:
print(f"Completed: {total_segments} segments concatenated into {duration:.1f} seconds of audio")
# Return 24 kHz mono waveform
return 24_000, final_audio
except gr.Error:
raise # Re-raise Gradio errors as-is
except Exception as e:
raise gr.Error(f"Error during speech generation: {str(e)}")
# ======================
# 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"),
gr.Checkbox(value=False, label="Full-page Markdown (Content Scraper mode)"),
],
outputs=gr.Markdown(label="Extracted Summary"),
title="Fetch Webpage",
description=(
"Extract title, key metadata, readable text, and links from webpages — or toggle full-page Markdown.
"
),
api_description=(
"Fetch a web page and return a compact Markdown summary with title, key "
"metadata, readable body text, and outbound links. Or, enable the "
"'Full-page Markdown (Content Scraper mode)' option to return the page "
"converted to Markdown."
),
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=(
"Very concise web search to avoid unnecessary context. Emits JSONL with short keys (t,u[,s]). Defaults avoid snippets and duplicate domains.
"
),
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=(
"Execute Python code and see the output.
"
),
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;
/* Ensure main title appears first, then our two subtitle lines */
display: grid;
justify-items: center;
}
/* Place bold tools list on line 2, normal auth note on line 3 (below title) */
.gradio-container h1::before {
grid-row: 2;
content: "Fetch Webpage | Search DuckDuckGo | Code Interpreter | Kokoro TTS (54 voices) | Image Generation | Video Generation";
display: block;
font-size: 1rem;
font-weight: 700;
opacity: 0.9;
margin-top: 6px;
white-space: pre-wrap;
}
.gradio-container h1::after {
grid-row: 3;
content: "Authentication is optional but Image/Video Generation require a `HF_READ_TOKEN` in env variables. They are hidden otherwise.";
display: block;
font-size: 1rem;
font-weight: 400;
opacity: 0.9;
margin-top: 2px;
white-space: pre-wrap;
}
/* Remove inside tab panels so it doesn't duplicate under each tool title */
.gradio-container [role=\"tabpanel\"] h1::before,
.gradio-container [role=\"tabpanel\"] h1::after {
content: none !important;
}
"""
# --- Kokoro TTS tab (text to speech) ---
available_voices = get_kokoro_voices()
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=1.25, step=0.1, label="Speed"),
gr.Dropdown(
label="Voice",
choices=available_voices,
value="af_heart",
info="Select from 54 available voices across multiple languages and accents"
),
],
outputs=gr.Audio(label="Audio", type="numpy"),
title="Kokoro TTS",
description=(
"Generate speech with Kokoro-82M using 54 different voices. Supports multiple languages and accents. Can generate audio of any length! Runs on CPU or CUDA if available.
"
),
api_description=(
"Synthesize speech from text using Kokoro-82M with 54 voice options. Returns (sample_rate, waveform) suitable for playback. "
"Parameters: text (str), speed (float 0.5–2.0, default 1.25x), voice (str from 54 available options). "
"Default voice: `af_heart`. "
"Can generate audio of unlimited length by processing all text segments. "
"Return the generated media to the user in this format ``"
),
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=(
"Generate images via Hugging Face Inference. "
"Default model is FLUX.1-Krea
"
),
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 media to the user in this format ``"
),
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=(
"Generate short videos via Hugging Face Inference Providers. "
"Default model is Wan2.2-T2V-A14B.
"
),
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). Return the generated media to the user in this format ``"
),
allow_flagging="never",
)
# Build tabbed app; disable Image/Video tools if no HF token is present
HAS_HF_TOKEN = bool(HF_API_TOKEN or HF_VIDEO_TOKEN)
_interfaces = [
fetch_interface,
concise_interface,
code_interface,
kokoro_interface,
]
_tab_names = [
"Fetch Webpage",
"DuckDuckGo Search",
"Python Code Executor",
"Kokoro TTS",
]
if HAS_HF_TOKEN:
_interfaces.extend([image_generation_interface, video_generation_interface])
_tab_names.extend(["Image Generation", "Video Generation"])
demo = gr.TabbedInterface(
interface_list=_interfaces,
tab_names=_tab_names,
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)