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| import os | |
| import pprint | |
| import requests | |
| from bs4 import BeautifulSoup | |
| from gnews import GNews | |
| from datetime import datetime | |
| import edge_tts | |
| import arxiv | |
| import subprocess | |
| import base64 | |
| import openai | |
| import streamlit as st | |
| from langchain.utilities import GoogleSerperAPIWrapper | |
| from langchain.utilities import GoogleSerperAPIWrapper | |
| from langchain.llms.openai import OpenAI | |
| from youtubesearchpython import * | |
| from youtube_transcript_api import YouTubeTranscriptApi | |
| from langchain.text_splitter import CharacterTextSplitter, RecursiveCharacterTextSplitter | |
| from langchain.docstore.document import Document | |
| from langchain.llms.openai import OpenAI | |
| from langchain.chains.summarize import load_summarize_chain | |
| from langchain.chat_models import ChatOpenAI | |
| from langchain.agents import initialize_agent, Tool | |
| from langchain.agents import AgentType | |
| from langchain.chat_models import ChatOpenAI | |
| from langchain.document_loaders import WebBaseLoader | |
| from langchain.chains.summarize import load_summarize_chain | |
| os.environ["OPENAI_API_KEY"]= st.secrets["OPENAI_API_KEY"] | |
| openai.api_key = os.environ["OPENAI_API_KEY"] | |
| system_message = ''' | |
| You are a very talented editor, skilled at consolidating | |
| fragmented information and introductions into a cohesive script, without missing any details. | |
| Compile the news article based on the information in 【】. | |
| ''' | |
| system_message_2 = ''' | |
| You are a linguist, skilled in summarizing textual content and presenting it in 3 bullet points using markdown. | |
| ''' | |
| system_message_3 = ''' | |
| 你是个语言学家，擅长把英文翻译成中文。要注意表达的流畅和使用中文的表达习惯。不要返回多余的信息，只把文字翻译成中文。 | |
| ''' | |
| def find_next_link_text(url, target_link, target_text): | |
| """ | |
| Find the first link and text after the given target link and text on the specified URL. | |
| Parameters: | |
| url (str): The URL of the webpage to scrape. | |
| target_link (str): The specific link to be found. | |
| target_text (str): The specific link text to be found. | |
| Returns: | |
| tuple: A tuple containing the next link and its text. Returns (None, None) if not found. | |
| """ | |
| # Send a GET request | |
| response = requests.get(url) | |
| response.raise_for_status() # This will raise an exception if there's an error | |
| # Parse the content using BeautifulSoup | |
| soup = BeautifulSoup(response.content, 'html.parser') | |
| # Find all the <ul> elements | |
| ul_elems = soup.find_all('ul') | |
| # Initialize a list to store all links and their texts | |
| all_links = [] | |
| # Extract links and texts from all <ul> elements | |
| for ul_elem in ul_elems: | |
| links = [(link.get('href'), link.text) for link in ul_elem.find_all('a')] | |
| all_links.extend(links) | |
| # Extract the first link and text after the specified link-text pair | |
| found = False | |
| for link, text in all_links: | |
| if found: | |
| return link, text | |
| if link == target_link and text == target_text: | |
| found = True | |
| return None, None | |
| def is_link_accessible(url): | |
| """Check if a link is accessible.""" | |
| try: | |
| response = requests.get(url, timeout=10) # setting a timeout to avoid waiting indefinitely | |
| # Check if the status code is 4xx or 5xx | |
| if 400 <= response.status_code < 600: | |
| return False | |
| return True | |
| except requests.RequestException: | |
| return False | |
| def get_latest_aws_ml_blog(): | |
| url = 'https://aws.amazon.com/blogs/machine-learning/' | |
| response = requests.get(url) | |
| if response.status_code != 200: | |
| print(f"Failed to retrieve webpage. Status code: {response.status_code}") | |
| return None, None | |
| soup = BeautifulSoup(response.text, 'html.parser') | |
| articles = soup.find_all('div', class_='lb-col lb-mid-18 lb-tiny-24') | |
| if not articles: | |
| print("No articles found.") | |
| return None, None | |
| title = articles[0].find('h2').text | |
| link = articles[0].find('a')['href'] | |
| return title, link | |
| def fetch_videos_from_channel(channel_id): | |
| playlist = Playlist(playlist_from_channel_id(channel_id)) | |
| while playlist.hasMoreVideos: | |
| playlist.getNextVideos() | |
| return playlist.videos | |
| def get_h1_text(url): | |
| """Fetches the text content of the first h1 element from the given URL.""" | |
| # Get the HTML content of the URL | |
| response = requests.get(url) | |
| soup = BeautifulSoup(response.content, 'html.parser') | |
| # Find the first h1 element and get its text | |
| h1_element = soup.find('h1', class_='entry-title') | |
| if h1_element: | |
| return h1_element.text.strip() # Remove any extra whitespaces | |
| else: | |
| return None | |
| def get_transcript(video_id): | |
| raw_data = YouTubeTranscriptApi.get_transcript(video_id) | |
| texts = [item['text'] for item in raw_data] | |
| return ' '.join(texts) | |
| def extract_data_from_url(url, class_name): | |
| """ | |
| 从指定的URL中提取特定类名的<a>标签的href属性和文本内容。 | |
| 参数: | |
| - url (str): 要提取数据的网页URL。 | |
| - class_name (str): 要查找的<a>标签的类名。 | |
| """ | |
| response = requests.get(url) | |
| if response.status_code == 200: | |
| soup = BeautifulSoup(response.content, 'html.parser') | |
| target_a = soup.find('a', class_=class_name) | |
| if target_a: | |
| data_mrf_link = target_a.get('href') | |
| text = target_a.get_text().strip() | |
| return (data_mrf_link, text) | |
| else: | |
| raise ValueError("找不到目标元素。") | |
| else: | |
| raise ConnectionError("请求失败。") | |
| def split_text_into_documents(long_string, max_docs=20): | |
| text_splitter = RecursiveCharacterTextSplitter( | |
| chunk_size=500, | |
| chunk_overlap=20, | |
| length_function=len, | |
| ) | |
| texts = text_splitter.split_text(long_string) | |
| docs = [Document(page_content=t) for t in texts[:max_docs]] | |
| text_splitter = CharacterTextSplitter.from_tiktoken_encoder( | |
| chunk_size=500, chunk_overlap=0 | |
| ) | |
| split_docs = text_splitter.split_documents(docs) | |
| return split_docs | |
| def autoplay_audio(file_path: str): | |
| with open(file_path, "rb") as f: | |
| data = f.read() | |
| b64 = base64.b64encode(data).decode() | |
| md = f""" | |
| <audio controls autoplay style="width: 100%;"> | |
| <source src="data:audio/mp3;base64,{b64}" type="audio/mp3"> | |
| </audio> | |
| """ | |
| st.markdown( | |
| md, | |
| unsafe_allow_html=True, | |
| ) | |
| def get_h1_from_url(url): | |
| response = requests.get(url) | |
| if response.status_code == 200: | |
| soup = BeautifulSoup(response.content, 'html.parser') | |
| # 根据class查找<h1>标签 | |
| h1_tag = soup.find("h1", class_="f-display-2") | |
| if h1_tag: | |
| return h1_tag.text | |
| else: | |
| print("Couldn't find the <h1> tag with the specified class on the page.") | |
| return None | |
| else: | |
| print(f"Failed to fetch the webpage. Status code: {response.status_code}") | |
| return None | |
| def summarize_documents(split_docs): | |
| llm = ChatOpenAI(temperature=1, model_name="gpt-3.5-turbo-16k") | |
| chain = load_summarize_chain(llm, chain_type="map_reduce") | |
| summary = chain.run(split_docs) | |
| return summary | |
| def get_completion_from_messages(messages, | |
| model="gpt-3.5-turbo-16k", | |
| temperature=1.5, max_tokens=7000): | |
| response = openai.ChatCompletion.create( | |
| model=model, | |
| messages=messages, | |
| temperature=temperature, | |
| max_tokens=max_tokens, | |
| ) | |
| return response.choices[0].message["content"] | |
| def fetch_gnews_links(query, language='en', country='US', period='1d', start_date=None, end_date=None, max_results=5, exclude_websites=None): | |
| """ | |
| Fetch news links from Google News based on the provided query. | |
| Parameters: | |
| - query (str): The search query for fetching news. | |
| - ... (other params): Additional parameters for customizing the news fetch. | |
| Returns: | |
| - List[str]: List of URLs based on the search query. | |
| """ | |
| # Ensure that the exclude_websites parameter is a list | |
| content = {'title':[], 'summary':[], 'url':[]} | |
| # Initialize GNews | |
| google_news = GNews(language=language, country=country, period=period, start_date=start_date, end_date=end_date, max_results=max_results, exclude_websites=exclude_websites) | |
| # Fetch news based on the query | |
| news_items = google_news.get_news(query) | |
| print(news_items) | |
| # Extract URLs | |
| urls = [item['url'] for item in news_items] | |
| content['title'] = [item['title'] for item in news_items] | |
| for url in urls: | |
| content['url'].append(url) | |
| content['summary'].append(summarize_website_content(url)) | |
| return content | |
| def summarize_website_content(url, temperature=1, model_name="gpt-3.5-turbo-16k", chain_type="stuff"): | |
| """ | |
| Summarize the content of a given website URL. | |
| Parameters: | |
| - url (str): The website URL to fetch and summarize. | |
| - temperature (float, optional): Temperature parameter for ChatOpenAI model. Default is 0. | |
| - model_name (str, optional): The model name for ChatOpenAI. Default is "gpt-3.5-turbo-16k". | |
| - chain_type (str, optional): The type of summarization chain to use. Default is "stuff". | |
| Returns: | |
| - The summarized content. | |
| """ | |
| if True: | |
| # Load the content from the given URL | |
| loader = WebBaseLoader(url) | |
| docs = loader.load() | |
| # Initialize the ChatOpenAI model | |
| llm = ChatOpenAI(temperature=temperature, model_name=model_name) | |
| # Load the summarization chain | |
| chain = load_summarize_chain(llm, chain_type=chain_type) | |
| # Run the chain on the loaded documents | |
| summarized_content = chain.run(docs) | |
| return summarized_content | |
| else: | |
| return 'No result' | |
| def get_transcript_link(url): | |
| """Fetches the first 'Transcript' link from the given URL.""" | |
| response = requests.get(url) | |
| soup = BeautifulSoup(response.content, 'html.parser') | |
| transcript_link_element = soup.find('a', string="Transcript") | |
| if transcript_link_element: | |
| return transcript_link_element['href'] | |
| else: | |
| return None | |
| def get_youtube_link(url): | |
| """Fetches the first 'Transcript' link from the given URL.""" | |
| response = requests.get(url) | |
| soup = BeautifulSoup(response.content, 'html.parser') | |
| transcript_link_element = soup.find('a', string="Video") | |
| if transcript_link_element: | |
| return transcript_link_element['href'] | |
| else: | |
| return None | |
| def get_latest_openai_blog_url(): | |
| base_url = "https://openai.com" | |
| blog_url = f"{base_url}/blog" | |
| response = requests.get(blog_url) | |
| if response.status_code == 200: | |
| soup = BeautifulSoup(response.content, 'html.parser') | |
| # 查找具有特定类名的<a>标签 | |
| target_link = soup.find("a", class_="ui-link group relative cursor-pointer") | |
| if target_link: | |
| # Combining base URL with the relative path | |
| post_url = base_url + target_link['href'] | |
| return post_url | |
| else: | |
| print("Couldn't find the target post URL.") | |
| return None | |
| else: | |
| print(f"Failed to fetch the webpage. Status code: {response.status_code}") | |
| return None | |
| def extract_blog_link_info(url): | |
| headers = { | |
| 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.3' | |
| } | |
| response = requests.get(url, headers=headers) | |
| if response.status_code != 200: | |
| return None, None | |
| soup = BeautifulSoup(response.content, 'html.parser') | |
| # 由于网站可能有多个这样的链接，我们只选择第一个匹配的项 | |
| link_element = soup.find('a', class_='f-post-link') | |
| if link_element: | |
| text_content = link_element.h3.text.strip() | |
| href_link = link_element['href'] | |
| return text_content, href_link | |
| else: | |
| return None, None | |
| def get_all_text_from_url(url): | |
| # Fetch the content using requests | |
| response = requests.get(url) | |
| response.raise_for_status() # Raise an error if the request failed | |
| # Parse the HTML using BeautifulSoup | |
| soup = BeautifulSoup(response.text, 'html.parser') | |
| # Extract all text | |
| return ' '.join(soup.stripped_strings) # `stripped_strings` generates strings by stripping extra whitespaces | |
| def contains_keywords(s): | |
| keywords = ["AI", "GPT", "LLM"] | |
| return any(keyword in s for keyword in keywords) | |
| def input_page(st, **state): | |
| # Include Font Awesome CSS | |
| st.markdown( | |
| """ | |
| <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.15.3/css/all.min.css"> | |
| """, | |
| unsafe_allow_html=True, | |
| ) | |
| # Style and position the GitHub and Twitter icons at the bottom left corner | |
| st.markdown( | |
| """ | |
| <style> | |
| .social-icons { | |
| gap: 20px; # Space between icons | |
| } | |
| .social-icons a { | |
| margin-left: 7px; | |
| margin-right: 7px; | |
| } | |
| .social-icons .fa-github { | |
| color: gray; | |
| } | |
| .social-icons .fa-twitter { | |
| color: gray; | |
| } | |
| .social-icons .fa-discord { | |
| color: gray; | |
| font-size: 2em; | |
| } | |
| </style> | |
| """, | |
| unsafe_allow_html=True, | |
| ) | |
| # Add the GitHub and Twitter icons with hyperlinks | |
| st.markdown( | |
| f""" | |
| <h1 style='text-align: center; color: black; display: flex; align-items: center; justify-content: center;'> | |
| <span style='background-color: #FF4B4B; border-radius: 50%; display: inline-flex; align-items: center; justify-content: center; width: 40px; height: 40px; margin-right: 10px; position: relative; top: -8px;'> | |
| <i class='fas fa-play' style='color: white; font-size: 18px; position: relative; left: 1px;'></i> | |
| </span> | |
| Your Personal <span style='color: #FF4B4B; font-size: 1.25em;'>AI News</span> Podcast | |
| </h1> | |
| <div class="social-icons" style='text-align: center; color: black;'> | |
| <a href="https://github.com/xl631212/llm_newsletter/tree/main" target="_blank"><i class="fab fa-github fa-2x"></i></a> | |
| <a href="https://twitter.com/xuying_lee" target="_blank"><i class="fab fa-twitter fa-2x"></i></a> | |
| <a href="https://discord.gg/ZSMNztFE"><i class="fab fa-discord"></i></a> | |
| </div> | |
| """, | |
| unsafe_allow_html=True | |
| ) | |
| st.markdown( | |
| "<h3 style='text-align: center; color: black;'>🎉 We move to a new website! 🎉</h3>", | |
| unsafe_allow_html=True | |
| ) | |
| button_placeholder = st.empty() | |
| button_placeholder_1 = st.empty() | |
| st.markdown("<br>", unsafe_allow_html=True) | |
| st.markdown(""" | |
| <style> | |
| .stButton > button { | |
| font-size: 100px; | |
| width: 35%; /* 设置一个固定的宽度 */ | |
| height: 50px; /* 设置一个固定的高度 */ | |
| color: white; | |
| background-color: #FF4B4B; | |
| border: none; | |
| border-radius: 15px; | |
| margin: auto; | |
| font-weight: bold; | |
| display: flex; | |
| justify-content: center; | |
| align-items: center; | |
| } | |
| .stButton > button:hover { | |
| background-color: #EFEFEF; /* 为按钮添加简单的悬停效果 */ | |
| color: #9A9A9A; | |
| } | |
| .stButton > button div p { | |
| font-size: 24px; /* 改变按钮文本的字号 */ | |
| margin: 0; /* 移除段落的默认边距 */ | |
| } | |
| .stButton > button div p:hover { | |
| font-size: 20px; | |
| } | |
| </style> | |
| """, unsafe_allow_html=True) | |
| with button_placeholder: | |
| # 创建按钮 | |
| if st.button('Go to ai-dailynews.com'): | |
| webbrowser.open_new_tab('http://ai-dailynews.com/') | |
| with button_placeholder_1: | |
| html_code = ''' | |
| <div style="display: flex; justify-content: center;"> | |
| <iframe style="border: none;" src="https://cards.producthunt.com/cards/posts/418805?v=1" width="500" height="405" frameborder="0" scrolling="no" allowfullscreen></iframe> | |
| </div> | |
| ''' | |
| st.write(html_code, unsafe_allow_html=True) | |
| st.markdown(""" | |
| <style> | |
| .footer { | |
| position: fixed; | |
| bottom: 0; | |
| left: 10px; | |
| width: auto; | |
| background-color: transparent; | |
| text-align: right; | |
| padding-right: 10px; | |
| padding-bottom: 10px; | |
| } | |
| </style> | |
| <div class="footer">Made with ❤️ by Xuying Li</div> | |
| """, unsafe_allow_html=True) | |
| def compute_page(st, **state): | |
| # Include Font Awesome CSS | |
| st.markdown( | |
| """ | |
| <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.15.3/css/all.min.css"> | |
| """, | |
| unsafe_allow_html=True, | |
| ) | |
| # Style and position the GitHub and Twitter icons at the bottom left corner | |
| st.markdown( | |
| """ | |
| <style> | |
| .social-icons { | |
| gap: 20px; # Space between icons | |
| } | |
| .social-icons a { | |
| margin-left: 7px; | |
| margin-right: 7px; | |
| } | |
| .social-icons .fa-github { | |
| color: gray; | |
| } | |
| .social-icons .fa-twitter { | |
| color: gray; | |
| } | |
| .social-icons .fa-discord { | |
| color: gray; | |
| font-size: 2em; | |
| } | |
| </style> | |
| """, | |
| unsafe_allow_html=True, | |
| ) | |
| # Add the GitHub and Twitter icons with hyperlinks | |
| st.markdown( | |
| f""" | |
| <h1 style='text-align: center; color: black; display: flex; align-items: center; justify-content: center;'> | |
| <span style='background-color: #FF4B4B; border-radius: 50%; display: inline-flex; align-items: center; justify-content: center; width: 40px; height: 40px; margin-right: 10px; position: relative; top: -8px;'> | |
| <i class='fas fa-play' style='color: white; font-size: 18px; position: relative; left: 1px;'></i> | |
| </span> | |
| Your Personal <span style='color: #FF4B4B; font-size: 1.25em;'>AI News</span> Podcast | |
| </h1> | |
| <div class="social-icons" style='text-align: center; color: black;'> | |
| <a href="https://github.com/xl631212/llm_newsletter/tree/main" target="_blank"><i class="fab fa-github fa-2x"></i></a> | |
| <a href="https://twitter.com/xuying_lee" target="_blank"><i class="fab fa-twitter fa-2x"></i></a> | |
| <a href="https://discord.gg/ZSMNztFE"><i class="fab fa-discord"></i></a> | |
| </div> | |
| """, | |
| unsafe_allow_html=True | |
| ) | |
| st.markdown(""" | |
| <style> | |
| /* This styles the main content excluding h1 and h2 */ | |
| #root .block-container { | |
| width: 75%; | |
| margin: auto; | |
| } | |
| </style> | |
| """, unsafe_allow_html=True) | |
| radio_placeholder = st.empty() | |
| progress_placeholder = st.empty() | |
| progress_text = "Searching for Openai Blog..." | |
| my_bar = progress_placeholder.progress(0, text=progress_text) | |
| openai_blog_url = get_latest_openai_blog_url() | |
| if openai_blog_url: | |
| openai_title = get_h1_from_url(openai_blog_url) | |
| openai_blog = summarize_website_content(openai_blog_url) | |
| my_bar.progress(10, text="Searching for Microsoft Blog...") | |
| url = "https://blogs.microsoft.com/" | |
| M_title, Microsoft_link = extract_blog_link_info(url) | |
| bair_blog = summarize_website_content(Microsoft_link) | |
| my_bar.progress(20, text="Searching for Amazon Blog...") | |
| A_title, A_link = get_latest_aws_ml_blog() | |
| mit_blog = summarize_website_content(A_link) | |
| my_bar.progress(30, text="Searching for Apple Blog...") | |
| url = 'https://machinelearning.apple.com/' | |
| response = requests.get(url) | |
| soup = BeautifulSoup(response.content, 'html.parser') | |
| # 根据提供的HTML片段，定位到文章的标题和链接 | |
| article = soup.select_one('h3.post-title a') | |
| apple_link = 'https://machinelearning.apple.com'+ article['href'] | |
| Apple_blog_title = article.text | |
| Apple_blog = summarize_website_content(apple_link) | |
| my_bar.progress(35, text='Searching for machine learning street talk...') | |
| channel_id = "UCMLtBahI5DMrt0NPvDSoIRQ" | |
| playlist = Playlist(playlist_from_channel_id(channel_id)) | |
| while playlist.hasMoreVideos: | |
| playlist.getNextVideos() | |
| machine_title = playlist.videos[0]['title'] | |
| machine_link = playlist.videos[0]['link'] | |
| machine_learning_boardcast = summarize_website_content(machine_link) | |
| my_bar.progress(40, text='Searching for lex friman boardcast...') | |
| url = "https://lexfridman.com/podcast/" | |
| link = get_transcript_link(url) | |
| L_title = get_h1_text(link) | |
| youtube_link = get_youtube_link(url) | |
| lexi_boardcast = summarize_website_content(youtube_link) | |
| my_bar.progress(50, text="Searching for arxiv ...") | |
| search = arxiv.Search( | |
| query = "AI, LLM, machine learning, NLP", | |
| max_results = st.session_state.arxiv, | |
| sort_by = arxiv.SortCriterion.SubmittedDate | |
| ) | |
| ariv_essay = '' | |
| for result in search.results(): | |
| ariv_essay += result.summary | |
| my_bar.progress(60, text="Searching Google News...") | |
| google_news = fetch_gnews_links(query='AI, LLM, Machine learning', max_results = st.session_state.day) | |
| my_bar.progress(70, text="Searching Techcrunch...") | |
| url = 'https://techcrunch.com/category/artificial-intelligence/' | |
| response = requests.get(url) | |
| soup = BeautifulSoup(response.content, 'html.parser') | |
| articles = soup.select('.post-block__title a') | |
| data_mrf_link, h_title = articles[0]['href'],articles[0].text | |
| h_content = summarize_website_content(data_mrf_link) | |
| my_bar.progress(75, text="Nvidia Podcast...") | |
| url = "https://blogs.nvidia.com/ai-podcast/" | |
| target_link = "https://blogs.nvidia.com/ai-podcast/" | |
| target_text = "AI Podcast" | |
| next_link, Nvidia_title = find_next_link_text(url, target_link, target_text) | |
| n_content = summarize_website_content(next_link) | |
| my_bar.progress(80, text="Writing Newsletter...") | |
| query = n_content + str(google_news['summary']) + str(mit_blog) + str(h_content)\ | |
| + openai_blog + 'new arxiv essay' + ariv_essay | |
| query = query.replace('<|endoftext|>', '') | |
| messages = [ | |
| {'role':'system', | |
| 'content': system_message + "keep it equal to {} words.".format(st.session_state.audio_length) + st.session_state.tone}, | |
| {'role':'user', | |
| 'content': f"【{query}】"},] | |
| response = get_completion_from_messages(messages) | |
| my_bar.progress(90, text="Generating Podcast...") | |
| if st.session_state.language == 'English': | |
| updated = response.replace('-', '').replace('--', '').replace('"', '').replace('“', '') | |
| command = f'edge-tts --text "{updated}" --write-media hello.mp3' | |
| subprocess.run(command, shell=True) | |
| my_bar.progress(90, text="Generating Summary...") | |
| query = response | |
| messages = [ | |
| {'role':'system', | |
| 'content': system_message_2}, | |
| {'role':'user', | |
| 'content': f"【{query}】"},] | |
| summary = get_completion_from_messages(messages) | |
| else: | |
| before = response | |
| before = before.replace('<|endoftext|>', '') | |
| messages = [ | |
| {'role':'system', | |
| 'content': system_message_3}, | |
| {'role':'user', | |
| 'content': f"【{before}】"},] | |
| after = get_completion_from_messages(messages) | |
| # 构建 edge-tts 命令 | |
| command = f'edge-tts --voice zh-CN-XiaoyiNeural --text "{after}" --write-media hello2.mp3' | |
| # 使用 subprocess 运行命令 | |
| subprocess.run(command, shell=True) | |
| my_bar.progress(100, text="Almost there...") | |
| with radio_placeholder: | |
| #audio_file = open('hello.mp3', 'rb') | |
| #audio_bytes = audio_file.read() | |
| #st.audio(audio_bytes, format='wav') | |
| if st.session_state.language == 'English': | |
| autoplay_audio("hello.mp3") | |
| else: | |
| autoplay_audio("hello2.mp3") | |
| my_bar.empty() | |
| if st.session_state.language == 'English': | |
| st.subheader('Summary and Commentary', divider='rainbow') | |
| st.markdown(summary) | |
| st.subheader('Technology News', divider='red') | |
| for i in range(len(google_news['title'])): | |
| if len(google_news['summary'][i]) > 100: | |
| st.markdown(f'<a href="{google_news["url"][i]}" style="color: #2859C0; text-decoration: none; \ | |
| font-size: 20px;font-weight: bold;"> {google_news["title"][i]} </a>\ | |
| <span style="margin-left: 10px; background-color: white; padding: 0px 7px; border: 1px solid rgb(251, 88, 88); border-radius: 20px; font-size: 7px; color: rgb(251, 88, 88)">Google News</span>', unsafe_allow_html=True) | |
| st.markdown(google_news['summary'][i]) | |
| st.markdown(f'<a href="{data_mrf_link}" style="color: #2859C0; text-decoration: none; \ | |
| font-size: 20px;font-weight: bold;">{h_title}</a>\ | |
| <span style="margin-left: 10px; background-color: white; padding: 0px 7px; border: 1px solid rgb(251, 88, 88); border-radius: 20px; font-size: 7px; color: rgb(251, 88, 88)">Techcrunch</span>', unsafe_allow_html=True) | |
| st.markdown(h_content) | |
| st.subheader('Podcast and Speeches', divider='orange') | |
| st.markdown(f'<a href="{youtube_link}" style="color: #2859C0; text-decoration: none; \ | |
| font-size: 20px;font-weight: bold;">{L_title}</a>\ | |
| <span style="margin-left: 10px; background-color: white; padding: 0px 7px; border: 1px solid rgb(251, 88, 88); border-radius: 20px; font-size: 7px; color: rgb(251, 88, 88)">Lex Fridman</span>', unsafe_allow_html=True) | |
| st.markdown(lexi_boardcast) | |
| st.markdown(f'<a href="{next_link}" style="color: #2859C0; text-decoration: none; \ | |
| font-size: 20px;font-weight: bold;">{Nvidia_title}</a>\ | |
| <span style="margin-left: 10px; background-color: white; padding: 0px 7px; border: 1px solid rgb(251, 88, 88); border-radius: 20px; font-size: 7px; color: rgb(251, 88, 88)">Nvidia</span>', unsafe_allow_html=True) | |
| st.markdown(n_content) | |
| st.markdown(f'<a href="{machine_link}" style="color: #2859C0; text-decoration: none; \ | |
| font-size: 20px;font-weight: bold;">{machine_title}</a>\ | |
| <span style="margin-left: 10px; background-color: white; padding: 0px 7px; border: 1px solid rgb(251, 88, 88); border-radius: 20px; font-size: 7px; color: rgb(251, 88, 88)">Machine Learning Street Talk</span>', unsafe_allow_html=True) | |
| st.markdown(machine_learning_boardcast) | |
| st.subheader('Technology Blogs', divider='green') | |
| st.markdown(f'<a href= {openai_blog_url} style="color: #2859C0; text-decoration: none; \ | |
| font-size: 20px;font-weight: bold;"> {openai_title}</a>\ | |
| <span style="margin-left: 10px; background-color: white; padding: 0px 7px; border: 1px solid rgb(251, 88, 88); border-radius: 20px; font-size: 7px; color: rgb(251, 88, 88)">Openai</span>', unsafe_allow_html=True) | |
| st.markdown(openai_blog) | |
| st.markdown(f'<a href={Microsoft_link} style="color: #2859C0; text-decoration: none; \ | |
| font-size: 20px;font-weight: bold;"> {M_title}</a>\ | |
| <span style="margin-left: 10px; background-color: white; padding: 0px 7px; border: 1px solid rgb(251, 88, 88); border-radius: 20px; font-size: 7px; color: rgb(251, 88, 88)">Microsoft</span>', unsafe_allow_html=True) | |
| st.markdown(bair_blog) | |
| st.markdown(f'<a href="https://aws.amazon.com/blogs/machine-learning/" style="color: #2859C0; text-decoration: none; \ | |
| font-size: 20px;font-weight: bold;"> {A_title}</a>\ | |
| <span style="margin-left: 10px; background-color: white; padding: 0px 7px; border: 1px solid rgb(251, 88, 88); border-radius: 20px; font-size: 7px; color: rgb(251, 88, 88)">Amazon</span>', unsafe_allow_html=True) | |
| st.markdown(mit_blog) | |
| st.markdown( | |
| f'<a href={apple_link} style="color: #2859C0; text-decoration: none; font-size: 20px; font-weight: bold;">{Apple_blog_title}</a>\ | |
| <span style="margin-left: 10px; background-color: white; padding: 0px 7px; border: 1px solid rgb(251, 88, 88); border-radius: 20px; font-size: 7px; color: rgb(251, 88, 88)">Apple</span>', | |
| unsafe_allow_html=True | |
| ) | |
| st.markdown(Apple_blog) | |
| st.subheader('Cutting-edge Papers', divider='green') | |
| for result in search.results(): | |
| st.markdown(f'<a href="{result.entry_id}" style="color: #2859C0; text-decoration: none; \ | |
| font-size: 20px;font-weight: bold;"> {result.title} </a>\ | |
| <span style="margin-left: 10px; background-color: white; padding: 0px 7px; border: 1px solid rgb(251, 88, 88); border-radius: 20px; font-size: 7px; color: rgb(251, 88, 88)">{result.primary_category}</span>\ | |
| ', unsafe_allow_html=True) | |
| st.markdown(result.summary) | |
| elif st.session_state.language == '中文': | |
| st.subheader('摘要与评论', divider='rainbow') | |
| summary = after.replace('<|endoftext|>', '') | |
| st.markdown(summary) | |
| st.subheader('科技新闻', divider='rainbow') | |
| for i in range(len(google_news['title'])): | |
| title = google_news['title'][i] | |
| messages = [ | |
| {'role':'system', | |
| 'content': system_message_3}, | |
| {'role':'user', | |
| 'content': f"【{title}】"},] | |
| title = get_completion_from_messages(messages) | |
| news_summary = google_news['summary'][i] | |
| messages = [ | |
| {'role':'system', | |
| 'content': system_message_3}, | |
| {'role':'user', | |
| 'content': f"【{news_summary}】"},] | |
| news_summary = get_completion_from_messages(messages) | |
| st.markdown(f'<a href="{google_news["url"][i]}" style="color: #2859C0; text-decoration: none; \ | |
| font-size: 20px;font-weight: bold;"> {title} </a>\ | |
| <span style="margin-left: 10px; background-color: white; padding: 0px 7px; border: 1px solid rgb(251, 88, 88); border-radius: 20px; font-size: 7px; color: rgb(251, 88, 88)">Google News</span>', unsafe_allow_html=True) | |
| st.markdown(news_summary) | |
| news_summary = h_title | |
| messages = [ | |
| {'role':'system', | |
| 'content': system_message_3}, | |
| {'role':'user', | |
| 'content': f"【{news_summary}】"},] | |
| h_title = get_completion_from_messages(messages) | |
| st.markdown(f'<a href="{data_mrf_link}" style="color: #2859C0; text-decoration: none; \ | |
| font-size: 20px;font-weight: bold;">{h_title}</a>\ | |
| <span style="margin-left: 10px; background-color: white; padding: 0px 7px; border: 1px solid rgb(251, 88, 88); border-radius: 20px; font-size: 7px; color: rgb(251, 88, 88)">Techcrunch</span>', unsafe_allow_html=True) | |
| news_summary = h_content | |
| messages = [ | |
| {'role':'system', | |
| 'content': system_message_3}, | |
| {'role':'user', | |
| 'content': f"【{news_summary}】"},] | |
| h_content = get_completion_from_messages(messages) | |
| st.markdown(h_content) | |
| st.subheader('播客与博客', divider='orange') | |
| news_summary = L_title | |
| messages = [ | |
| {'role':'system', | |
| 'content': system_message_3}, | |
| {'role':'user', | |
| 'content': f"【{news_summary}】"},] | |
| L_title = get_completion_from_messages(messages) | |
| st.markdown(f'<a href="{youtube_link}" style="color: #2859C0; text-decoration: none; \ | |
| font-size: 20px;font-weight: bold;">{L_title}</a>\ | |
| <span style="margin-left: 10px; background-color: white; padding: 0px 7px; border: 1px solid rgb(251, 88, 88); border-radius: 20px; font-size: 7px; color: rgb(251, 88, 88)">Lex Fridman</span>', unsafe_allow_html=True) | |
| news_summary = lexi_boardcast | |
| messages = [ | |
| {'role':'system', | |
| 'content': system_message_3}, | |
| {'role':'user', | |
| 'content': f"【{news_summary}】"},] | |
| lexi_boardcast = get_completion_from_messages(messages) | |
| st.markdown(lexi_boardcast) | |
| news_summary = Nvidia_title | |
| messages = [ | |
| {'role':'system', | |
| 'content': system_message_3}, | |
| {'role':'user', | |
| 'content': f"【{news_summary}】"},] | |
| Nvidia_title = get_completion_from_messages(messages) | |
| st.markdown(f'<a href="{next_link}" style="color: #2859C0; text-decoration: none; \ | |
| font-size: 20px;font-weight: bold;">{Nvidia_title}</a>\ | |
| <span style="margin-left: 10px; background-color: white; padding: 0px 7px; border: 1px solid rgb(251, 88, 88); border-radius: 20px; font-size: 7px; color: rgb(251, 88, 88)">Nvidia</span>', unsafe_allow_html=True) | |
| news_summary = n_content | |
| messages = [ | |
| {'role':'system', | |
| 'content': system_message_3}, | |
| {'role':'user', | |
| 'content': f"【{news_summary}】"},] | |
| n_content = get_completion_from_messages(messages) | |
| st.markdown(n_content) | |
| news_summary = machine_title | |
| messages = [ | |
| {'role':'system', | |
| 'content': system_message_3}, | |
| {'role':'user', | |
| 'content': f"【{news_summary}】"},] | |
| machine_title = get_completion_from_messages(messages) | |
| st.markdown(f'<a href="{machine_link}" style="color: #2859C0; text-decoration: none; \ | |
| font-size: 20px;font-weight: bold;">{machine_title}</a>\ | |
| <span style="margin-left: 10px; background-color: white; padding: 0px 7px; border: 1px solid rgb(251, 88, 88); border-radius: 20px; font-size: 7px; color: rgb(251, 88, 88)">Machine Learning Street Talk</span>', unsafe_allow_html=True) | |
| news_summary = machine_learning_boardcast | |
| messages = [ | |
| {'role':'system', | |
| 'content': system_message_3}, | |
| {'role':'user', | |
| 'content': f"【{news_summary}】"},] | |
| machine_learning_boardcast = get_completion_from_messages(messages) | |
| st.markdown(machine_learning_boardcast) | |
| st.subheader('科技博客', divider='green') | |
| openai_blog = openai_blog.replace('<|endoftext|>', '') | |
| messages = [ | |
| {'role':'system', | |
| 'content': system_message_3}, | |
| {'role':'user', | |
| 'content': f"{openai_blog}"},] | |
| openai_blog = get_completion_from_messages(messages) | |
| messages = [ | |
| {'role':'system', | |
| 'content': system_message_3}, | |
| {'role':'user', | |
| 'content': f"【{openai_title}】"},] | |
| openai_title = get_completion_from_messages(messages) | |
| st.markdown(f'<a href= {openai_blog_url} style="color: #2859C0; text-decoration: none; \ | |
| font-size: 20px;font-weight: bold;"> {openai_title}</a>\ | |
| <span style="margin-left: 10px; background-color: white; padding: 0px 7px; border: 1px solid rgb(251, 88, 88); border-radius: 20px; font-size: 7px; color: rgb(251, 88, 88)">Openai</span>', unsafe_allow_html=True) | |
| st.markdown(openai_blog) | |
| bair_blog = bair_blog.replace('<|endoftext|>', '') | |
| messages = [ | |
| {'role':'system', | |
| 'content': system_message_3}, | |
| {'role':'user', | |
| 'content': f"【{bair_blog}】"},] | |
| bair_blog = get_completion_from_messages(messages) | |
| messages = [ | |
| {'role':'system', | |
| 'content': system_message_3}, | |
| {'role':'user', | |
| 'content': f"{M_title}"},] | |
| M_title = get_completion_from_messages(messages) | |
| st.markdown(f'<a href={link} style="color: #2859C0; text-decoration: none; \ | |
| font-size: 20px;font-weight: bold;"> {M_title}</a>\ | |
| <span style="margin-left: 10px; background-color: white; padding: 0px 7px; border: 1px solid rgb(251, 88, 88); border-radius: 20px; font-size: 7px; color: rgb(251, 88, 88)">Microsoft</span>', unsafe_allow_html=True) | |
| st.markdown(bair_blog) | |
| mit_blog = mit_blog.replace('<|endoftext|>', '') | |
| messages = [ | |
| {'role':'system', | |
| 'content': system_message_3}, | |
| {'role':'user', | |
| 'content': f"【{mit_blog}】"},] | |
| mit_blog = get_completion_from_messages(messages) | |
| messages = [ | |
| {'role':'system', | |
| 'content': system_message_3}, | |
| {'role':'user', | |
| 'content': f"{A_title}"},] | |
| A_title = get_completion_from_messages(messages) | |
| st.markdown(f'<a href="{A_link}" style="color: #2859C0; text-decoration: none; \ | |
| font-size: 20px;font-weight: bold;"> {A_title}</a>\ | |
| <span style="margin-left: 10px; background-color: white; padding: 0px 7px; border: 1px solid rgb(251, 88, 88); border-radius: 20px; font-size: 7px; color: rgb(251, 88, 88)">Amazon</span>', unsafe_allow_html=True) | |
| st.markdown(mit_blog) | |
| st.subheader('尖端论文', divider='green') | |
| for result in search.results(): | |
| title = result.title | |
| result_summary = result.summary | |
| messages = [ | |
| {'role':'system', | |
| 'content': system_message_3}, | |
| {'role':'user', | |
| 'content': f"{title}"},] | |
| result_title = get_completion_from_messages(messages) | |
| messages = [ | |
| {'role':'system', | |
| 'content': system_message_3}, | |
| {'role':'user', | |
| 'content': f"{result_summary}"},] | |
| result_summary = get_completion_from_messages(messages) | |
| st.markdown(f'<a href="{result.entry_id}" style="color: #2859C0; text-decoration: none; \ | |
| font-size: 20px;font-weight: bold;"> {result_title} </a>\ | |
| <span style="margin-left: 10px; background-color: white; padding: 0px 7px; border: 1px solid rgb(251, 88, 88); border-radius: 20px; font-size: 7px; color: rgb(251, 88, 88)">{result.primary_category}</span>\ | |
| ', unsafe_allow_html=True) | |
| st.markdown(result_summary) | |
| st.markdown(""" | |
| <style> | |
| .footer { | |
| position: fixed; | |
| bottom: 0; | |
| left: 10px; | |
| width: auto; | |
| background-color: transparent; | |
| text-align: left; | |
| padding-left: 10px; | |
| padding-top: 10px; | |
| } | |
| </style> | |
| <div class="footer">Made with ❤️ by Xuying Li</div> | |
| """, unsafe_allow_html=True) | |
| def page_one(): | |
| input_page(st) | |
| def page_two(): | |
| compute_page(st) | |
| def main(): | |
| # 初始化session状态 | |
| if "page" not in st.session_state: | |
| st.session_state.page = "one" | |
| if "choice" not in st.session_state: | |
| st.session_state.choice = "" | |
| if "language" not in st.session_state: | |
| st.session_state.language = "English" | |
| if "audio_length" not in st.session_state: | |
| st.session_state.audio_length = '5' | |
| if "day" not in st.session_state: | |
| st.session_state.day = 0 | |
| st.session_state.arxiv = 0 | |
| if "tone" not in st.session_state: | |
| st.session_state.tone = '' | |
| # 根据session状态来渲染页面 | |
| if st.session_state.page == "one": | |
| page_one() | |
| elif st.session_state.page == "two": | |
| page_two() | |
| if __name__ == "__main__": | |
| st.set_page_config(layout="wide", initial_sidebar_state="collapsed") | |
| main() |