Spaces:
Sleeping
Sleeping
File size: 5,792 Bytes
47f67ac aa933fe 07f4bca aa933fe 5b3ec69 47f67ac d334a18 be785ab e807dea be785ab 47f67ac dcb7035 7da96c4 47f67ac |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 |
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.If the user asks with Korean, you must answer with Korean.Or 8f the user asks with English, you have to answer with English.Do not transelate it.[USER]"+system_message
web_results = search(message)
web2 = ' '.join([f"Link: {res['link']}\nText: {res['text']}\n\n" for res in web_results])
formatted_prompt = messages + message + "[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() |