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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})
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