anything malicious in this webui tool code?

"""
title: Deep Web Research Tool
author: Assistant
author_url: https://github.com/assistant
git_url: https://github.com/open-webui/open-webui
description: Performs comprehensive web research with multiple search engines, content analysis, and structured reporting
required_open_webui_version: 0.4.0
requirements: requests beautifulsoup4 urllib3
version: 1.0.0
license: MIT
"""

import requests
import json
import logging
import re
import time
from typing import Callable, Any, Optional, List, Dict
from pydantic import BaseModel, Field
from datetime import datetime
from urllib.parse import quote_plus
from bs4 import BeautifulSoup
import asyncio

# Configure loggisng
logging.basicConfig(
    level=logging.INFO,
    format="%(asctime)s - %(levelname)s - [DeepResearch] - %(message)s",
)

class Tools:
    class Valves(BaseModel):
        MAX_SEARCH_RESULTS: int = Field(default=10)
        MAX_ITERATIONS: int = Field(default=3)
        REQUEST_TIMEOUT: int = Field(default=30)
        USER_AGENT: str = Field(default="Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36")
        ENABLE_CONTENT_SCRAPING: bool = Field(default=True)
        MAX_CONTENT_LENGTH: int = Field(default=5000)

    def __init__(self):
        self.valves = self.Valves()
        self.session = requests.Session()
        self.session.headers.update({'User-Agent': self.valves.USER_AGENT})
        logging.info("Deep Web Research Tool initialized")

    def _clean_text(self, text: str) -> str:
        text = re.sub(r'\s+', ' ', text.strip())
        text = re.sub(r'[^\w\s\-.,;:!?()\[\]{}"\']', '', text)
        return text[:self.valves.MAX_CONTENT_LENGTH]

    def _extract_content(self, url: str) -> Dict[str, str]:
        try:
            response = self.session.get(url, timeout=self.valves.REQUEST_TIMEOUT, allow_redirects=True)
            response.raise_for_status()
            soup = BeautifulSoup(response.content, 'html.parser')
            for tag in soup(["script", "style", "nav", "footer", "header"]):
                tag.decompose()
            title = soup.find('title').get_text() if soup.find('title') else "No title"
            selectors = ['article', 'main', '.content', '#content', '.post-content', '.entry-content', 'p']
            content = ''
            for selector in selectors:
                elements = soup.select(selector)
                if elements:
                    content = ' '.join([e.get_text() for e in elements[:3]])
                    break
            if not content:
                content = ' '.join([p.get_text() for p in soup.find_all('p')[:5]])
            return {
                'title': self._clean_text(title),
                'content': self._clean_text(content),
                'url': url
            }
        except Exception as e:
            logging.warning(f"Failed to extract content from {url}: {str(e)}")
            return {'title': "Content extraction failed", 'content': f"Unable to extract content: {str(e)}", 'url': url}

    def _search_duckduckgo(self, query: str) -> List[Dict[str, str]]:
        try:
            search_url = "https://api.duckduckgo.com/"
            params = {
                'q': query,
                'format': 'json',
                'no_redirect': '1',
                'no_html': '1',
                'skip_disambig': '1'
            }
            response = self.session.get(search_url, params=params, timeout=self.valves.REQUEST_TIMEOUT)
            response.raise_for_status()
            data = response.json()
            results = []
            if data.get('AbstractText'):
                results.append({
                    'title': data.get('Heading', 'DuckDuckGo Instant Answer'),
                    'content': data.get('AbstractText'),
                    'url': data.get('AbstractURL', ''),
                    'source': 'DuckDuckGo Instant Answer'
                })
            for topic in data.get('RelatedTopics', [])[:3]:
                if isinstance(topic, dict) and topic.get('Text'):
                    results.append({
                        'title': topic.get('Text', '').split(' - ')[0],
                        'content': topic.get('Text', ''),
                        'url': topic.get('FirstURL', ''),
                        'source': 'DuckDuckGo Related Topic'
                    })
            return results[:self.valves.MAX_SEARCH_RESULTS]
        except Exception as e:
            logging.error(f"DuckDuckGo search failed: {str(e)}")
            return []

    async def perform_deep_research(self, research_topic: str, __event_emitter__: Callable[[dict], Any]) -> str:
        logging.info(f"Starting deep research on: {research_topic}")
        await __event_emitter__({"type": "status", "data": {"description": "🔍 Initializing deep research...", "done": False, "hidden": False}})
        all_findings = []
        queries = [research_topic, f"{research_topic} latest news", f"{research_topic} analysis report", f"{research_topic} expert opinion"]

        for i, query in enumerate(queries[:self.valves.MAX_ITERATIONS]):
            await __event_emitter__({"type": "status", "data": {"description": f"📊 Research iteration {i+1}: Searching for '{query}'", "done": False, "hidden": False}})
            search_results = self._search_duckduckgo(query)
            await __event_emitter__({"type": "status", "data": {"description": f"📄 Found {len(search_results)} results, extracting content...", "done": False, "hidden": False}})
            if self.valves.ENABLE_CONTENT_SCRAPING:
                for result in search_results:
                    if result.get('url', '').startswith('http'):
                        detailed = self._extract_content(result['url'])
                        if detailed['content']:
                            result['detailed_content'] = detailed['content']
            all_findings.extend(search_results)
            if i < len(queries) - 1:
                await asyncio.sleep(1)

        await __event_emitter__({"type": "status", "data": {"description": "📝 Compiling comprehensive research report...", "done": False, "hidden": False}})
        return self._compile_report(research_topic, all_findings)

    def _compile_report(self, topic: str, findings: List[Dict[str, str]]) -> str:
        if not findings:
            return f"# Research Report: {topic}\n\n⚠️ No significant findings."
        report = f"""# Deep Research Report: {topic}

*Generated on {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}*

---

## Executive Summary

{len(findings)} sources analyzed over {self.valves.MAX_ITERATIONS} iterations.

"""
        for i, f in enumerate(findings, 1):
            report += f"### Finding {i}: {f['title']}\n\n"
            report += f"{f.get('detailed_content', f['content'])}\n\n"
            report += f"**Source:** [{f['url']}]({f['url']})\n\n---\n\n"
        report += f"""## Methodology

- Search Engine: DuckDuckGo Instant Answer API
- Iterations: {self.valves.MAX_ITERATIONS}
- Content Scraping: {'Enabled' if self.valves.ENABLE_CONTENT_SCRAPING else 'Disabled'}

---
*Automated deep research tool output.*"""
        return report