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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 tag_visible(element):
if element.parent.name in ['style', 'script', 'head', 'title', 'meta', '[document]']:
return False
if isinstance(element, Comment):
return False
return True
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 "\n".join(t.strip() for t in visible_texts)
# Pre-fetch and truncate homepage text to reduce the prompt length.
text_list = []
homepage_url = "https://sites.google.com/view/abhilashnandy/home/"
extensions = ["", "pmrf-profile-page"]
for ext in extensions:
full_text = get_text_from_url(homepage_url + ext)
truncated_text = full_text[:1000] # using only the first 1000 characters per extension
text_list.append(truncated_text)
SYSTEM_MESSAGE = (
"You are a QA chatbot to answer queries (in less than 30 words) on my homepage. "
"Context: " + " ".join(text_list)
)
# Use a model that is both lightweight and includes a proper chat configuration.
client = InferenceClient("TheBloke/TinyLlama-1.1B-Chat-v1.0-GPTQ")
def respond(message, history: list[tuple[str, str]], system_message=SYSTEM_MESSAGE,
max_tokens=100, temperature=0.7, top_p=0.95):
messages = [{"role": "system", "content": system_message}]
for q, a in history:
messages.append({"role": "user", "content": "Question: " + q})
messages.append({"role": "assistant", "content": "Answer: " + a})
messages.append({"role": "user", "content": message})
try:
# Enable streaming mode to receive output faster.
response_stream = client.chat_completion(
messages,
max_tokens=max_tokens,
temperature=temperature,
top_p=top_p,
stream=True,
)
output = ""
for chunk in response_stream:
if hasattr(chunk, "choices") and chunk.choices:
part = chunk.choices[0].message.get("content", "")
output += part
return output.strip()
except Exception as e:
print(f"An error occurred: {e}")
return str(e)
initial_message = [("user", "Yo who dis Abhilash?")]
markdown_note = "## Ask Anything About Me! (Might show a tad bit of hallucination!)"
demo = gr.Blocks()
with demo:
gr.Markdown(markdown_note)
gr.ChatInterface(
fn=respond,
examples=["Yo who dis Abhilash?", "What is Abhilash's most recent publication?"],
additional_inputs=[],
)
if __name__ == "__main__":
demo.launch() |