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import gradio as gr
from huggingface_hub import InferenceClient
import requests
from bs4 import BeautifulSoup
from bs4.element import Comment
def get_text_from_url(url):
response = requests.get(url)
soup = BeautifulSoup(response.text, 'html.parser')
texts = soup.find_all(text=True)
visible_texts = filter(tag_visible, texts)
return u"\n".join(t.strip() for t in visible_texts)
def tag_visible(element):
if element.parent.name in ['style', 'script', 'head', 'title', 'meta', '[document]']:
return False
if isinstance(element, Comment):
return False
return True
text_list = []
homepage_url = "https://sites.google.com/view/abhilashnandy/home/"
extensions = ["", "about", "curriculum-vitae", "pmrf-profile-page", "publications"]
for ext in extensions:
url_text = get_text_from_url(homepage_url+ext)
text_list.append(url_text)
# Repeat for sub-links if necessary
"""
For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
"""
client = InferenceClient("QuantFactory/Meta-Llama-3-8B-Instruct-GGUF")#("HuggingFaceH4/zephyr-7b-beta")
SYSTEM_MESSAGE = "You are a QA chatbot to answer queries on my homepage that has the following information -\n\n" + "\n\n".join(text_list)
def respond(
message,
history: list[tuple[str, str]],
system_message=SYSTEM_MESSAGE,
max_tokens=200,
temperature=0.7,
top_p=0.95,
):
messages = [{"role": "system", "content": system_message}]
for val in history:
# if val[0]:
if len(val)>=1:
messages.append({"role": "user", "content": val[0]})
# if val[1]:
if len(val)>=2:
messages.append({"role": "assistant", "content": val[1]})
messages.append({"role": "user", "content": message})
response = ""
for message in client.chat_completion(
messages,
max_tokens=max_tokens,
stream=True,
temperature=temperature,
top_p=top_p,
):
token = message.choices[0].delta.content
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=8192, 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() |