Spaces:
Sleeping
Sleeping
File size: 5,312 Bytes
5cd5547 21bdb2c 7cbf423 21bdb2c 7cbf423 21bdb2c 5cd5547 21bdb2c 5cd5547 21bdb2c 5cd5547 21bdb2c 5cd5547 21bdb2c 5cd5547 21bdb2c 5cd5547 21bdb2c 5cd5547 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 |
import streamlit as st
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin
import re
from typing import List, Tuple
from collections import deque
import time
import numpy as np
from sentence_transformers import SentenceTransformer
from transformers import T5Tokenizer, T5ForConditionalGeneration
import torch
def crawl(start_url: str, max_depth: int = 1, delay: float = 0.1) :
visited = set()
results = []
queue = deque([(start_url, 0)])
crawled_urls = []
while queue:
url, depth = queue.popleft()
if depth > max_depth or url in visited:
continue
visited.add(url)
crawled_urls.append(url)
try:
time.sleep(delay)
response = requests.get(url)
soup = BeautifulSoup(response.text, 'html.parser')
text = soup.get_text()
text = re.sub(r'\s+', ' ', text).strip()
results.append((url, text))
if depth < max_depth:
for link in soup.find_all('a', href=True):
next_url = urljoin(url, link['href'])
if next_url.startswith('https://docs.nvidia.com/cuda/') and next_url not in visited:
queue.append((next_url, depth + 1))
except Exception as e:
print(f"Error crawling {url}: {e}")
return results[:10], crawled_urls[:10]
def chunk_text(text: str, max_chunk_size: int = 1000) -> List[str]:
chunks = []
current_chunk = ""
for sentence in re.split(r'(?<=[.!?])\s+', text):
if len(current_chunk) + len(sentence) <= max_chunk_size:
current_chunk += sentence + " "
else:
chunks.append(current_chunk.strip())
current_chunk = sentence + " "
if current_chunk:
chunks.append(current_chunk.strip())
return chunks
class InMemoryStorage:
def __init__(self):
self.embeddings = np.array([])
self.texts = []
self.urls = []
def insert(self, embeddings, texts, urls):
if self.embeddings.size == 0:
self.embeddings = embeddings
else:
self.embeddings = np.vstack((self.embeddings, embeddings))
self.texts.extend(texts)
self.urls.extend(urls)
def search(self, query_embedding, top_k=5):
if self.embeddings.size == 0:
return []
similarities = np.dot(self.embeddings, query_embedding)
top_indices = np.argsort(similarities)[-top_k:][::-1]
return [(self.texts[i], self.urls[i]) for i in top_indices]
def get_sentence_transformer():
return SentenceTransformer('distilbert-base-nli-mean-tokens')
def insert_chunks(storage, chunks: List[str], urls: List[str]):
model = get_sentence_transformer()
embeddings = model.encode(chunks)
storage.insert(embeddings, chunks, urls)
def vector_search(storage, query: str, top_k: int = 5):
model = get_sentence_transformer()
query_embedding = model.encode([query])[0]
return storage.search(query_embedding, top_k)
class QuestionAnsweringSystem:
def __init__(self):
self.tokenizer = T5Tokenizer.from_pretrained("t5-small")
self.model = T5ForConditionalGeneration.from_pretrained("t5-small")
self.tokenizer.model_max_length = 1024
self.model.config.max_length = 1024
def answer_question(self, question: str, context: str) -> str:
input_text = f"question: {question} context: {context}"
inputs = self.tokenizer(input_text, return_tensors="pt", max_length=1024, truncation=True)
outputs = self.model.generate(inputs.input_ids,
max_length=1024,
num_beams=4,
early_stopping=True)
answer = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
return answer
def get_answer(storage, qa_system: QuestionAnsweringSystem, query: str) -> Tuple[str, str]:
results = vector_search(storage, query)
if not results:
return "No relevant documents found.", ""
context = " ".join([result[0] for result in results])
answer = qa_system.answer_question(query, context)
source_url = results[0][1] if results else ""
return answer, source_url
# Streamlit UI
st.title("CUDA Documentation QA System")
storage = InMemoryStorage()
qa_system = QuestionAnsweringSystem()
# Crawling and processing the data
if st.button('Crawl CUDA Documentation'):
with st.spinner('Crawling CUDA documentation...'):
crawled_data, crawled_urls = crawl("https://docs.nvidia.com/cuda/", max_depth=1, delay=0.1)
st.write(f"Processed {len(crawled_data)} pages.")
for url, text in crawled_data:
chunks = chunk_text(text, max_chunk_size=1024)
insert_chunks(storage, chunks, [url] * len(chunks))
st.success("Crawling and processing completed.")
# Asking questions
query = st.text_input("Enter your question about CUDA:")
if query:
with st.spinner('Searching for an answer...'):
answer, source_url = get_answer(storage, qa_system, query)
st.write("**Answer:**")
st.write(answer)
st.write("**Source:**")
st.write(source_url)
|