mirror of
https://github.com/xtekky/gpt4free.git
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89e096334d
Add new default HuggingFace provider Add format_image_prompt and get_last_user_message helper Add stop_browser callable to get_nodriver function Fix content type response in images route
273 lines
9.4 KiB
Python
273 lines
9.4 KiB
Python
from __future__ import annotations
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from aiohttp import ClientSession, ClientTimeout, ClientError
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import json
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import hashlib
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from pathlib import Path
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from urllib.parse import urlparse, quote_plus
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from datetime import datetime
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import datetime
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import asyncio
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try:
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from duckduckgo_search import DDGS
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from duckduckgo_search.exceptions import DuckDuckGoSearchException
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from bs4 import BeautifulSoup
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has_requirements = True
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except ImportError:
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has_requirements = False
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try:
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import spacy
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has_spacy = True
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except:
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has_spacy = False
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from typing import Iterator
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from ..cookies import get_cookies_dir
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from ..providers.response import format_link
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from ..errors import MissingRequirementsError
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from .. import debug
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DEFAULT_INSTRUCTIONS = """
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Using the provided web search results, to write a comprehensive reply to the user request.
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Make sure to add the sources of cites using [[Number]](Url) notation after the reference. Example: [[0]](http://google.com)
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"""
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class SearchResults():
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def __init__(self, results: list, used_words: int):
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self.results = results
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self.used_words = used_words
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def __iter__(self):
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yield from self.results
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def __str__(self):
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search = ""
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for idx, result in enumerate(self.results):
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if search:
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search += "\n\n\n"
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search += f"Title: {result.title}\n\n"
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if result.text:
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search += result.text
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else:
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search += result.snippet
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search += f"\n\nSource: [[{idx}]]({result.url})"
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return search
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def __len__(self) -> int:
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return len(self.results)
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class SearchResultEntry():
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def __init__(self, title: str, url: str, snippet: str, text: str = None):
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self.title = title
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self.url = url
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self.snippet = snippet
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self.text = text
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def set_text(self, text: str):
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self.text = text
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def scrape_text(html: str, max_words: int = None, add_source=True, count_images: int = 2) -> Iterator[str]:
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source = BeautifulSoup(html, "html.parser")
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soup = source
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for selector in [
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"main",
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".main-content-wrapper",
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".main-content",
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".emt-container-inner",
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".content-wrapper",
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"#content",
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"#mainContent",
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]:
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select = soup.select_one(selector)
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if select:
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soup = select
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break
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# Zdnet
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for remove in [".c-globalDisclosure"]:
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select = soup.select_one(remove)
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if select:
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select.extract()
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image_select = "img[alt][src^=http]:not([alt=''])"
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image_link_select = f"a:has({image_select})"
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yield_words = []
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for paragraph in soup.select(f"h1, h2, h3, h4, h5, h6, p, table:not(:has(p)), ul:not(:has(p)), {image_link_select}"):
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if count_images > 0:
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image = paragraph.select_one(image_select)
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if image:
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title = str(paragraph.get("title", paragraph.text))
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if title:
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yield f"!{format_link(image['src'], title)}\n"
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if max_words is not None:
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max_words -= 10
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count_images -= 1
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continue
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for line in paragraph.get_text(" ").splitlines():
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words = [word for word in line.split() if word]
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count = len(words)
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if not count:
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continue
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words = " ".join(words)
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if words in yield_words:
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continue
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if max_words:
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max_words -= count
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if max_words <= 0:
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break
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yield words + "\n"
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yield_words.append(words)
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if add_source:
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canonical_link = source.find("link", rel="canonical")
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if canonical_link and "href" in canonical_link.attrs:
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link = canonical_link["href"]
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domain = urlparse(link).netloc
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yield f"\nSource: [{domain}]({link})"
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async def fetch_and_scrape(session: ClientSession, url: str, max_words: int = None, add_source: bool = False) -> str:
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try:
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bucket_dir: Path = Path(get_cookies_dir()) / ".scrape_cache" / "fetch_and_scrape"
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bucket_dir.mkdir(parents=True, exist_ok=True)
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md5_hash = hashlib.md5(url.encode(errors="ignore")).hexdigest()
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cache_file = bucket_dir / f"{quote_plus(url.split('?')[0].split('//')[1].replace('/', ' ')[:48])}.{datetime.date.today()}.{md5_hash[:16]}.cache"
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if cache_file.exists():
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return cache_file.read_text()
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async with session.get(url) as response:
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if response.status == 200:
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html = await response.text(errors="replace")
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text = "".join(scrape_text(html, max_words, add_source))
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with open(cache_file, "wb") as f:
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f.write(text.encode(errors="replace"))
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return text
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except (ClientError, asyncio.TimeoutError):
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return
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async def search(query: str, max_results: int = 5, max_words: int = 2500, backend: str = "auto", add_text: bool = True, timeout: int = 5, region: str = "wt-wt") -> SearchResults:
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if not has_requirements:
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raise MissingRequirementsError('Install "duckduckgo-search" and "beautifulsoup4" package | pip install -U g4f[search]')
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with DDGS() as ddgs:
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results = []
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for result in ddgs.text(
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query,
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region=region,
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safesearch="moderate",
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timelimit="y",
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max_results=max_results,
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backend=backend,
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):
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if ".google." in result["href"]:
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continue
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results.append(SearchResultEntry(
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result["title"],
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result["href"],
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result["body"]
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))
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if add_text:
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requests = []
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async with ClientSession(timeout=ClientTimeout(timeout)) as session:
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for entry in results:
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requests.append(fetch_and_scrape(session, entry.url, int(max_words / (max_results - 1)), False))
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texts = await asyncio.gather(*requests)
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formatted_results = []
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used_words = 0
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left_words = max_words
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for i, entry in enumerate(results):
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if add_text:
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entry.text = texts[i]
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if max_words:
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left_words -= entry.title.count(" ") + 5
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if entry.text:
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left_words -= entry.text.count(" ")
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else:
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left_words -= entry.snippet.count(" ")
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if 0 > left_words:
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break
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used_words = max_words - left_words
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formatted_results.append(entry)
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return SearchResults(formatted_results, used_words)
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async def do_search(prompt: str, query: str = None, instructions: str = DEFAULT_INSTRUCTIONS, **kwargs) -> str:
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if instructions and instructions in prompt:
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return prompt # We have already added search results
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if query is None:
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query = spacy_get_keywords(prompt)
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json_bytes = json.dumps({"query": query, **kwargs}, sort_keys=True).encode(errors="ignore")
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md5_hash = hashlib.md5(json_bytes).hexdigest()
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bucket_dir: Path = Path(get_cookies_dir()) / ".scrape_cache" / f"web_search" / f"{datetime.date.today()}"
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bucket_dir.mkdir(parents=True, exist_ok=True)
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cache_file = bucket_dir / f"{quote_plus(query[:20])}.{md5_hash}.cache"
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if cache_file.exists():
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with cache_file.open("r") as f:
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search_results = f.read()
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else:
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search_results = await search(query, **kwargs)
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if search_results.results:
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with cache_file.open("wb") as f:
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f.write(str(search_results).encode(errors="replace"))
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if instructions:
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new_prompt = f"""
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{search_results}
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Instruction: {instructions}
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User request:
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{prompt}
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"""
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else:
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new_prompt = f"""
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{search_results}
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{prompt}
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"""
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debug.log(f"Web search: '{query.strip()[:50]}...'")
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if isinstance(search_results, SearchResults):
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debug.log(f"with {len(search_results.results)} Results {search_results.used_words} Words")
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return new_prompt
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def get_search_message(prompt: str, raise_search_exceptions=False, **kwargs) -> str:
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try:
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return asyncio.run(do_search(prompt, **kwargs))
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except (DuckDuckGoSearchException, MissingRequirementsError) as e:
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if raise_search_exceptions:
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raise e
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debug.log(f"Couldn't do web search: {e.__class__.__name__}: {e}")
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return prompt
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def spacy_get_keywords(text: str):
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if not has_spacy:
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return text
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# Load the spaCy language model
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nlp = spacy.load("en_core_web_sm")
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# Process the query
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doc = nlp(text)
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# Extract keywords based on POS and named entities
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keywords = []
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for token in doc:
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# Filter for nouns, proper nouns, and adjectives
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if token.pos_ in {"NOUN", "PROPN", "ADJ"} and not token.is_stop:
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keywords.append(token.lemma_)
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# Add named entities as keywords
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for ent in doc.ents:
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keywords.append(ent.text)
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# Remove duplicates and print keywords
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keywords = list(set(keywords))
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#print("Keyword:", keywords)
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#keyword_freq = Counter(keywords)
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#keywords = keyword_freq.most_common()
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#print("Keyword Frequencies:", keywords)
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keywords = [chunk.text for chunk in doc.noun_chunks if not chunk.root.is_stop]
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#print("Phrases:", keywords)
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return keywords |