app.py

import streamlit as st
import pandas as pd
from modules.data_preparation import prepare_df, plot_3dgraph
import numpy as np
import datetime

st.title('Sentiment Analysis for Price Trend Prediction')

st.header(f'Data based on Platts News and Insights Data')
st.subheader(f'{datetime.datetime.now()}')

news_category = st.selectbox("Select Market Movers Category", ("Crude Oil", "Light Ends", "Middle Distillates", "Heavy Distillates"))

latest_news = prepare_df(pd.read_csv('data/results_platts_09082024_clean.csv'), news_category)
top_news = prepare_df(pd.read_csv('data/topresults_platts_09082024_clean.csv'), news_category)

df_news = pd.concat([latest_news, top_news], ignore_index=True).drop_duplicates(['headline'])

df_mean = pd.DataFrame({
    'headline' : ['MEAN OF ALL NEWS'],
    'negative_score' : [df_news['negative_score'].mean()],
    'neutral_score' : [df_news['neutral_score'].mean()],
    'positive_score' : [df_news['positive_score'].mean()],
    'topic_verification' : ['']
})

df_news_final = pd.concat([df_news, df_mean]).drop(columns=['body'])

df_news_final.index = np.arange(1, len(df_news_final) + 1)

df_news_final

st.plotly_chart(plot_3dgraph(df_news_final), use_container_width=True)