Spaces:
Runtime error
Runtime error
File size: 2,805 Bytes
bbd5c76 06323bb 3421ed4 6761a81 3421ed4 6b7515b 06323bb 8735569 06323bb 717d3b5 6b7515b 717d3b5 bbd5c76 3421ed4 717d3b5 3421ed4 bbd5c76 6b7515b bbd5c76 3421ed4 717d3b5 6761a81 717d3b5 6761a81 f5e2959 6b7515b 717d3b5 6761a81 f5e2959 717d3b5 f5e2959 717d3b5 f5e2959 3421ed4 fdefcb0 6761a81 9eb3d05 619f1a8 6761a81 beacea3 6b7515b 3421ed4 bbd5c76 |
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 |
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 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 first 1000 characters to keep prompt short
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)
)
# Switch to a model optimized for low-latency CPU inference.
# Here we use a GPT4All model (assuming one is available via the Inference API).
client = InferenceClient("nomic-ai/gpt4all-lora")
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:
# Use streaming mode to return tokens as they are generated
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() |