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| from openai import OpenAI | |
| import streamlit as st | |
| from langchain_openai import ChatOpenAI | |
| from tools import sentiment_analysis_util | |
| import numpy as np | |
| import pandas as pd | |
| from dotenv import load_dotenv | |
| import os | |
| st.set_page_config(page_title="LangChain Agent", layout="wide") | |
| load_dotenv() | |
| OPENAI_API_KEY = os.environ["OPENAI_API_KEY"] | |
| from langchain_core.runnables import RunnableConfig | |
| st.title("💬 News Search") | |
| st.image('el_pic.png') | |
| #@st.cache_resource | |
| if "messages" not in st.session_state: | |
| st.session_state["messages"] = [{"role":"system", "content":"""💬 How can I help you?"""}] | |
| # Display all previous messages | |
| for msg in st.session_state.messages: | |
| st.chat_message(msg["role"]).write(msg["content"]) | |
| #initialize_session_state() | |
| sideb=st.sidebar | |
| with st.sidebar: | |
| prompt=st.text_input("Enter topic for sentiment analysis: ") | |
| check1=sideb.button(f"analyze {prompt}") | |
| if check1: | |
| # Add user message to chat history | |
| st.session_state.messages.append({"role": "user", "content": prompt}) | |
| # Display user message in chat message container | |
| with st.chat_message("user"): | |
| st.markdown(prompt) | |
| # ========================== Sentiment analysis | |
| #Perform sentiment analysis on the cryptocurrency news & predict dominant sentiment along with plotting the sentiment breakdown chart | |
| # Downloading from reddit | |
| # Downloading from alpaca | |
| if len(prompt.split(' '))<3: | |
| st.write('I am analyzing Google News ...') | |
| news_articles = sentiment_analysis_util.fetch_news(str(prompt)) | |
| st.write('Now, I am analyzing Reddit ...') | |
| reddit_news_articles=sentiment_analysis_util.fetch_reddit_news(prompt) | |
| # Fetch news articles | |
| tavily_news_articles = sentiment_analysis_util.fetch_tavily_news(prompt) | |
| # Handle empty results | |
| if not tavily_news_articles: | |
| print("No news articles found. Try adjusting your search terms.") | |
| else: | |
| # Process the articles | |
| for url in tavily_news_articles: | |
| try: | |
| # Your existing article processing code | |
| st.write(f"Article URL: {url}") | |
| # ... rest of your processing ... | |
| except Exception as e: | |
| st.error(f"Error processing article {url}: {e}") | |
| continue | |
| analysis_results = [] | |
| #Perform sentiment analysis for each product review | |
| if len(prompt.split(' '))<3: | |
| for article in news_articles: | |
| if prompt.lower()[0:6] in article['News_Article'].lower(): | |
| sentiment_analysis_result = sentiment_analysis_util.analyze_sentiment(article['News_Article']) | |
| # Display sentiment analysis results | |
| #print(f'News Article: {sentiment_analysis_result["News_Article"]} : Sentiment: {sentiment_analysis_result["Sentiment"]}', '\n') | |
| result = { | |
| 'News_Article': sentiment_analysis_result["News_Article"], | |
| 'Sentiment': sentiment_analysis_result["Sentiment"][0]['label'], | |
| 'Index': sentiment_analysis_result["Sentiment"][0]['score'], | |
| 'URL': article['URL'] | |
| } | |
| analysis_results.append(result) | |
| articles_url=[] | |
| for article in reddit_news_articles: | |
| if prompt.lower()[0:6] in article.lower(): | |
| sentiment_analysis_result_reddit = sentiment_analysis_util.analyze_sentiment(article) | |
| # Display sentiment analysis results | |
| #print(f'News Article: {sentiment_analysis_result_reddit["News_Article"]} : Sentiment: {sentiment_analysis_result_reddit["Sentiment"]}', '\n') | |
| result = { | |
| 'News_Article': sentiment_analysis_result_reddit["News_Article"], | |
| 'Index':np.round(sentiment_analysis_result_reddit["Sentiment"][0]['score'],2) | |
| } | |
| analysis_results.append(np.append(result,np.append(article.split('URL:')[-1:], ((article.split('Date: ')[-1:])[0][0:10])))) | |
| for article in tavily_news_articles: | |
| if prompt.lower()[0:5] in article: | |
| sentiment_analysis_result_tavily = sentiment_analysis_util.analyze_sentiment(article) | |
| # Display sentiment analysis results | |
| #print(f'News Article: {sentiment_analysis_result_tavily["News_Article"]} : Sentiment: {sentiment_analysis_result_tavily["Sentiment"]}', '\n') | |
| result = { | |
| 'News_Article': sentiment_analysis_result_tavily["News_Article"], | |
| 'Index':np.round(sentiment_analysis_result_tavily["Sentiment"][0]['score'],2) | |
| } | |
| analysis_results.append(np.append(result,np.append(article.split('URL:')[-1:], ((article.split('Date: ')[-1:])[0][0:10])))) | |
| print('is_present tavily 2',analysis_results) | |
| # #LinkedIn and Twitter previously downloaded from phantombuster | |
| # st.write('Teď analyzuji data z LinkedInu a Twitteru ...') | |
| # df=pd.read_csv('./data/LinkedIn_transurban_phantombuster.csv',index_col='postTimestamp',parse_dates=True,infer_datetime_format=True) | |
| # df=df.sort_index(ascending=False) | |
| # df=df.dropna() | |
| # from tools import sentiment_analysis_util | |
| # for linkedin_news in df['postContent']: | |
| # print(linkedin_news) | |
| # news_article={ | |
| # 'News_Article': linkedin_news, | |
| # 'URL': df.loc[df['postContent']==linkedin_news]['postUrl'][0], | |
| # 'date': df.loc[df['postContent']==linkedin_news].index[0]} | |
| # if prompt.lower()[0:6] in linkedin_news.lower(): | |
| # sentiment_analysis_result = sentiment_analysis_util.analyze_sentiment(news_article) | |
| # news_article["Sentiment"]=sentiment_analysis_result["Sentiment"][0]['label'] | |
| # news_article["Index"]=sentiment_analysis_result["Sentiment"][0]['score'] | |
| # analysis_results.append(news_article) | |
| # count=0 | |
| # df=pd.read_csv('./data/Twitter_transurban_phantombuster.csv',index_col='tweetDate',parse_dates=True,infer_datetime_format=True) | |
| # df=df.sort_index(ascending=False) | |
| # df=df.dropna() | |
| # from tools import sentiment_analysis_util | |
| # for twitter_news in df['text']: | |
| # print(twitter_news) | |
| # news_article={ | |
| # 'News_Article': twitter_news, | |
| # 'URL': df['tweetLink'][count], | |
| # 'date': df.iloc[count:count+1,:].index[0]} | |
| # if prompt.lower()[0:6] in twitter_news.lower(): | |
| # sentiment_analysis_result = sentiment_analysis_util.analyze_sentiment(news_article) | |
| # news_article["Sentiment"]=sentiment_analysis_result["Sentiment"][0]['label'] | |
| # news_article["Index"]=sentiment_analysis_result["Sentiment"][0]['score'] | |
| # analysis_results.append(news_article) | |
| # count+=1 | |
| #Generate summarized message rationalize dominant sentiment | |
| #st.write(analysis_results) | |
| summary = sentiment_analysis_util.generate_summary_of_sentiment(analysis_results) #, dominant_sentiment) | |
| st.chat_message("assistant").write((summary)) | |
| st.session_state.messages.append({"role": "assistant", "content": summary}) | |
| #answers=np.append(res["messages"][-1].content,summary) | |
| client = ChatOpenAI(model="gpt-4o",api_key=OPENAI_API_KEY) | |
| if "openai_model" not in st.session_state: | |
| st.session_state["openai_model"] = "gpt-4o" | |
| if prompt := st.chat_input("Any other questions? "): | |
| # Add user message to chat history | |
| st.session_state.messages.append({"role": "user", "content": prompt}) | |
| # Display user message in chat message container | |
| with st.chat_message("user"): | |
| st.markdown(prompt) | |
| # Display assistant response in chat message container | |
| with st.chat_message("assistant"): | |
| stream = client.chat.completions.create( | |
| model=st.session_state["openai_model"], | |
| messages=[ | |
| {"role": m["role"], "content": m["content"]} | |
| for m in st.session_state.messages | |
| ], | |
| stream=True, | |
| ) | |
| response = st.write_stream(stream) | |
| st.session_state.messages.append({"role": "assistant", "content": response}) | |