import requests
from bs4 import BeautifulSoup
from tqdm import tqdm
import chainlit as cl
from langchain import PromptTemplate
import requests
from bs4 import BeautifulSoup
from urllib.parse import urlparse, urljoin, urldefrag
import asyncio
import aiohttp
from aiohttp import ClientSession
try:
from modules.constants import *
except:
from constants import *
"""
Ref: https://python.plainenglish.io/scraping-the-subpages-on-a-website-ea2d4e3db113
"""
class WebpageCrawler:
def __init__(self):
self.dict_href_links = {}
async def fetch(self, session: ClientSession, url: str) -> str:
async with session.get(url) as response:
try:
return await response.text()
except UnicodeDecodeError:
return await response.text(encoding="latin1")
def url_exists(self, url: str) -> bool:
try:
response = requests.head(url)
return response.status_code == 200
except requests.ConnectionError:
return False
async def get_links(self, session: ClientSession, website_link: str, base_url: str):
html_data = await self.fetch(session, website_link)
soup = BeautifulSoup(html_data, "html.parser")
list_links = []
for link in soup.find_all("a", href=True):
href = link["href"].strip()
full_url = urljoin(base_url, href)
normalized_url = self.normalize_url(full_url) # sections removed
if (
normalized_url not in self.dict_href_links
and self.is_child_url(normalized_url, base_url)
and self.url_exists(normalized_url)
):
self.dict_href_links[normalized_url] = None
list_links.append(normalized_url)
return list_links
async def get_subpage_links(
self, session: ClientSession, urls: list, base_url: str
):
tasks = [self.get_links(session, url, base_url) for url in urls]
results = await asyncio.gather(*tasks)
all_links = [link for sublist in results for link in sublist]
return all_links
async def get_all_pages(self, url: str, base_url: str):
async with aiohttp.ClientSession() as session:
dict_links = {url: "Not-checked"}
counter = None
while counter != 0:
unchecked_links = [
link
for link, status in dict_links.items()
if status == "Not-checked"
]
if not unchecked_links:
break
new_links = await self.get_subpage_links(
session, unchecked_links, base_url
)
for link in unchecked_links:
dict_links[link] = "Checked"
print(f"Checked: {link}")
dict_links.update(
{
link: "Not-checked"
for link in new_links
if link not in dict_links
}
)
counter = len(
[
status
for status in dict_links.values()
if status == "Not-checked"
]
)
checked_urls = [
url for url, status in dict_links.items() if status == "Checked"
]
return checked_urls
def is_webpage(self, url: str) -> bool:
try:
response = requests.head(url, allow_redirects=True)
content_type = response.headers.get("Content-Type", "").lower()
return "text/html" in content_type
except requests.RequestException:
return False
def clean_url_list(self, urls):
files, webpages = [], []
for url in urls:
if self.is_webpage(url):
webpages.append(url)
else:
files.append(url)
return files, webpages
def is_child_url(self, url, base_url):
return url.startswith(base_url)
def normalize_url(self, url: str):
# Strip the fragment identifier
defragged_url, _ = urldefrag(url)
return defragged_url
def get_urls_from_file(file_path: str):
"""
Function to get urls from a file
"""
with open(file_path, "r") as f:
urls = f.readlines()
urls = [url.strip() for url in urls]
return urls
def get_base_url(url):
parsed_url = urlparse(url)
base_url = f"{parsed_url.scheme}://{parsed_url.netloc}/"
return base_url
def get_prompt(config):
if config["llm_params"]["use_history"]:
if config["llm_params"]["llm_loader"] == "local_llm":
custom_prompt_template = tinyllama_prompt_template_with_history
elif config["llm_params"]["llm_loader"] == "openai":
custom_prompt_template = openai_prompt_template_with_history
# else:
# custom_prompt_template = tinyllama_prompt_template_with_history # default
prompt = PromptTemplate(
template=custom_prompt_template,
input_variables=["context", "chat_history", "question"],
)
else:
if config["llm_params"]["llm_loader"] == "local_llm":
custom_prompt_template = tinyllama_prompt_template
elif config["llm_params"]["llm_loader"] == "openai":
custom_prompt_template = openai_prompt_template
# else:
# custom_prompt_template = tinyllama_prompt_template
prompt = PromptTemplate(
template=custom_prompt_template,
input_variables=["context", "question"],
)
return prompt
def get_sources(res, answer):
source_elements = []
source_dict = {} # Dictionary to store URL elements
for idx, source in enumerate(res["source_documents"]):
source_metadata = source.metadata
url = source_metadata["source"]
score = source_metadata.get("score", "N/A")
page = source_metadata.get("page", 1)
date = source_metadata.get("date", "N/A")
url_name = f"{url}_{page}"
if url_name not in source_dict:
source_dict[url_name] = {
"text": source.page_content,
"url": url,
"score": score,
"page": page,
"date": date,
}
else:
source_dict[url_name]["text"] += f"\n\n{source.page_content}"
# First, display the answer
full_answer = "**Answer:**\n"
full_answer += answer
# Then, display the sources
full_answer += "\n\n**Sources:**\n"
for idx, (url_name, source_data) in enumerate(source_dict.items()):
full_answer += f"\nSource {idx + 1} (Score: {source_data['score']}): {source_data['url']}\n"
name = f"Source {idx + 1} Text\n"
full_answer += name
source_elements.append(
cl.Text(name=name, content=source_data["text"], display="side")
)
# Add a PDF element if the source is a PDF file
if source_data["url"].lower().endswith(".pdf"):
name = f"Source {idx + 1} PDF\n"
full_answer += name
pdf_url = f"{source_data['url']}#page={source_data['page']+1}"
source_elements.append(cl.Pdf(name=name, url=pdf_url, display="side"))
full_answer += "\n**Metadata:**\n"
for idx, (url_name, source_data) in enumerate(source_dict.items()):
full_answer += f"Source {idx+1} Metadata\n"
source_elements.append(
cl.Text(
name=f"Source {idx+1} Metadata",
content=f"Page: {source_data['page']}\nDate: {source_data['date']}\n",
display="side",
)
)
return full_answer, source_elements
def get_metadata(file_names):
"""
Function to get any additional metadata from the files
Returns a dict with the file_name: {metadata: value}
"""
metadata_dict = {}
for file in file_names:
metadata_dict[file] = {
"source_type": "N/A",
}
return metadata_dict