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import gradio as gr
from huggingface_hub import InferenceClient
import requests
from bs4 import BeautifulSoup
import urllib
import random
# List of user agents to choose from for requests
_useragent_list = [
'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:66.0) Gecko/20100101 Firefox/66.0',
'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/111.0.0.0 Safari/537.36',
'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/111.0.0.0 Safari/537.36',
'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/109.0.0.0 Safari/537.36',
'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/111.0.0.0 Safari/537.36',
'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/111.0.0.0 Safari/537.36 Edg/111.0.1661.62',
'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:109.0) Gecko/20100101 Firefox/111.0'
]
def get_useragent():
"""Returns a random user agent from the list."""
return random.choice(_useragent_list)
def extract_text_from_webpage(html_content):
"""Extracts visible text from HTML content using BeautifulSoup."""
soup = BeautifulSoup(html_content, "html.parser")
# Remove unwanted tags
for tag in soup(["script", "style", "header", "footer", "nav"]):
tag.extract()
# Get the remaining visible text
visible_text = soup.get_text(strip=True)
return visible_text
def search(term, num_results=1, lang="en", advanced=True, sleep_interval=0, timeout=5, safe="active", ssl_verify=None):
"""Performs a Google search and returns the results."""
escaped_term = urllib.parse.quote_plus(term)
start = 0
all_results = []
# Fetch results in batches
while start < num_results:
resp = requests.get(
url="https://www.google.com/search",
headers={"User-Agent": get_useragent()}, # Set random user agent
params={
"q": term,
"num": num_results - start, # Number of results to fetch in this batch
"hl": lang,
"start": start,
"safe": safe,
},
timeout=timeout,
verify=ssl_verify,
)
resp.raise_for_status() # Raise an exception if request fails
soup = BeautifulSoup(resp.text, "html.parser")
result_block = soup.find_all("div", attrs={"class": "g"})
# If no results, continue to the next batch
if not result_block:
start += 1
continue
# Extract link and text from each result
for result in result_block:
link = result.find("a", href=True)
if link:
link = link["href"]
try:
# Fetch webpage content
webpage = requests.get(link, headers={"User-Agent": get_useragent()})
webpage.raise_for_status()
# Extract visible text from webpage
visible_text = extract_text_from_webpage(webpage.text)
all_results.append({"link": link, "text": visible_text})
except requests.exceptions.RequestException as e:
# Handle errors fetching or processing webpage
print(f"Error fetching or processing {link}: {e}")
all_results.append({"link": link, "text": None})
else:
all_results.append({"link": None, "text": None})
start += len(result_block) # Update starting index for next batch
return all_results
client = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1")
def respond(
message,
history: list[tuple[str, str]],
system_message,
max_tokens,
temperature,
top_p,
):
messages = "<s>[SYSTEM] Your name is Chatchat.Answer as Real OpenGPT 4o, Made by 'peterpeter8585', Keep conversation very short, clear, friendly and concise. The text provided is a request for a specific type of response from you, the virtual assistant. The request asks you to provide friendly responses. The expectation is that I will avoid introductions and start answering the query directly, elaborating on all aspects of the request. As an AI-powered assistant, your task is to generate responses that appear as if they are created by the user and not an AI-powered assistant. [USER]"+system_message
web_results = search(text)
web2 = ' '.join([f"Link: {res['link']}\nText: {res['text']}\n\n" for res in web_results])
formatted_prompt = messages + text + "[WEB]" + str(web2) + "[OpenGPT 4o]"
response = ""
stream = client.text_generation(formatted_prompt, max_new_tokens=512, stream=True, details=True, return_full_text=False)
token="".join([response.token.text for response in stream if response.token.text != "</s>"])
response += token
yield response
"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
"""
demo = gr.ChatInterface(
respond,
additional_inputs=[
gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
gr.Slider(
minimum=0.1,
maximum=1.0,
value=0.95,
step=0.05,
label="Top-p (nucleus sampling)",
),
],
)
if __name__ == "__main__":
demo.launch() |