108 lines
3.5 KiB
Python
108 lines
3.5 KiB
Python
import curses
|
|
import feedparser
|
|
import requests
|
|
import unicodedata
|
|
import json
|
|
from newspaper import Article
|
|
from bs4 import BeautifulSoup
|
|
from nltk.tokenize import sent_tokenize, word_tokenize
|
|
import numpy as np
|
|
from sklearn.neighbors import NearestNeighbors
|
|
from mattsollamatools import chunker
|
|
|
|
# Create a dictionary to store topics and their URLs
|
|
topic_urls = {
|
|
"Mac": "https://9to5mac.com/guides/mac/feed",
|
|
"News": "http://www.npr.org/rss/rss.php?id=1001",
|
|
"Nvidia": "https://nvidianews.nvidia.com/releases.xml",
|
|
"Raspberry Pi": "https://www.raspberrypi.com/news/feed/",
|
|
"Music": "https://www.billboard.com/c/music/music-news/feed/"
|
|
}
|
|
|
|
# Use curses to create a menu of topics
|
|
def menu(stdscr):
|
|
chosen_topic = get_url_for_topic(stdscr)
|
|
url = topic_urls[chosen_topic] if chosen_topic in topic_urls else "Topic not found"
|
|
|
|
stdscr.addstr(len(topic_urls) + 3, 0, f"Selected URL for {chosen_topic}: {url}")
|
|
stdscr.refresh()
|
|
|
|
return chosen_topic
|
|
|
|
# You have chosen a topic. Now return the url for that topic
|
|
def get_url_for_topic(stdscr):
|
|
curses.curs_set(0) # Hide the cursor
|
|
stdscr.clear()
|
|
|
|
stdscr.addstr(0, 0, "Choose a topic using the arrow keys (Press Enter to select):")
|
|
|
|
# Create a list of topics
|
|
topics = list(topic_urls.keys())
|
|
current_topic = 0
|
|
|
|
while True:
|
|
for i, topic in enumerate(topics):
|
|
if i == current_topic:
|
|
stdscr.addstr(i + 2, 2, f"> {topic}")
|
|
else:
|
|
stdscr.addstr(i + 2, 2, f" {topic}")
|
|
|
|
stdscr.refresh()
|
|
|
|
key = stdscr.getch()
|
|
|
|
if key == curses.KEY_DOWN and current_topic < len(topics) - 1:
|
|
current_topic += 1
|
|
elif key == curses.KEY_UP and current_topic > 0:
|
|
current_topic -= 1
|
|
elif key == 10: # Enter key
|
|
return topic_urls[topics[current_topic]]
|
|
|
|
# Get the last N URLs from an RSS feed
|
|
def getUrls(feed_url, n=20):
|
|
feed = feedparser.parse(feed_url)
|
|
entries = feed.entries[-n:]
|
|
urls = [entry.link for entry in entries]
|
|
return urls
|
|
|
|
# Often there are a bunch of ads and menus on pages for a news article. This uses newspaper3k to get just the text of just the article.
|
|
def getArticleText(url):
|
|
article = Article(url)
|
|
article.download()
|
|
article.parse()
|
|
return article.text
|
|
|
|
def get_summary(text):
|
|
systemPrompt = "Write a concise summary of the text, return your responses with 5 lines that cover the key points of the text given."
|
|
prompt = text
|
|
|
|
url = "http://localhost:11434/api/generate"
|
|
|
|
payload = {
|
|
"model": "mistral-openorca",
|
|
"prompt": prompt,
|
|
"system": systemPrompt,
|
|
"stream": False
|
|
}
|
|
payload_json = json.dumps(payload)
|
|
headers = {"Content-Type": "application/json"}
|
|
response = requests.post(url, data=payload_json, headers=headers)
|
|
|
|
return json.loads(response.text)["response"]
|
|
|
|
# Perform K-nearest neighbors (KNN) search
|
|
def knn_search(question_embedding, embeddings, k=5):
|
|
X = np.array([item['embedding'] for article in embeddings for item in article['embeddings']])
|
|
source_texts = [item['source'] for article in embeddings for item in article['embeddings']]
|
|
|
|
# Fit a KNN model on the embeddings
|
|
knn = NearestNeighbors(n_neighbors=k, metric='cosine')
|
|
knn.fit(X)
|
|
|
|
# Find the indices and distances of the k-nearest neighbors
|
|
distances, indices = knn.kneighbors(question_embedding, n_neighbors=k)
|
|
|
|
# Get the indices and source texts of the best matches
|
|
best_matches = [(indices[0][i], source_texts[indices[0][i]]) for i in range(k)]
|
|
|
|
return best_matches
|