Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

pull the sotu transcript from UCSB #1053

Merged
merged 1 commit into from
Jan 22, 2025
Merged

pull the sotu transcript from UCSB #1053

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
23 changes: 12 additions & 11 deletions 06_gpu_and_ml/langchains/potus_speech_qanda.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -55,7 +55,7 @@

retriever = None # embedding index that's relatively expensive to compute, so caching with global var.

# ## Scraping the speech from whitehouse.gov
# ## Scraping the speech

# It's super easy to scrape the transcipt of Biden's speech using `httpx` and `BeautifulSoup`.
# This speech is just one document and it's relatively short, but it's enough to demonstrate
Expand All @@ -66,7 +66,7 @@ def scrape_state_of_the_union() -> str:
import httpx
from bs4 import BeautifulSoup

url = "https://www.whitehouse.gov/state-of-the-union-2022/"
url = "https://www.presidency.ucsb.edu/documents/address-before-joint-session-the-congress-the-state-the-union-28"

# fetch article; simulate desktop browser
headers = {
Expand All @@ -75,16 +75,17 @@ def scrape_state_of_the_union() -> str:
response = httpx.get(url, headers=headers)
soup = BeautifulSoup(response.text, "lxml")

# get all text paragraphs & construct string of article text
speech_text = ""
speech_section = soup.find_all(
"div", {"class": "sotu-annotations__content"}
)
if speech_section:
paragraph_tags = speech_section[0].find_all("p")
speech_text = "".join([p.get_text() for p in paragraph_tags])
# locate the div containing the speech
speech_div = soup.find("div", class_="field-docs-content")

if speech_div:
speech_text = speech_div.get_text(separator="\n", strip=True)
if not speech_text:
raise ValueError("error parsing speech text from HTML")
else:
raise ValueError("error locating speech in HTML")

return speech_text.replace("\t", "")
return speech_text


# ## Constructing the Q&A chain
Expand Down