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})