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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 | |
| 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"] | |
| llm = ChatOpenAI(model="gpt-3.5-turbo") | |
| from langchain_core.runnables import RunnableConfig | |
| st.title("💬 ExpressMood") | |
| 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(' '))<2: | |
| print('here') | |
| 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) | |
| analysis_results = [] | |
| #Perform sentiment analysis for each product review | |
| if len(prompt.split(' '))<2: | |
| print('here') | |
| 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])))) | |
| # print(analysis_results) | |
| # import pandas as pd | |
| # print('STOP') | |
| # df_analysis_results=pd.DataFrame(analysis_results['News_Article']) | |
| # print(df_analysis_results) | |
| # df_analysis_results.sort_values(by='Date') | |
| # df_analysis_results.to_csv('analysis_results.csv') | |
| #Generate summarized message rationalize dominant sentiment | |
| 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 = OpenAI(api_key=OPENAI_API_KEY) | |
| if "openai_model" not in st.session_state: | |
| st.session_state["openai_model"] = "gpt-3.5-turbo" | |
| 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}) | |