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import streamlit as st
from aggregator import get_articles_sentiment
st.title("Real time financial news fast sentiment")
model_to_use = ""
model_selected = st.radio("Choose your model", ["fin_distilBert", "fin_tinyBert", "fin_miniLM"])
ticker = st.text_input("Insert the company ticker")
col1, col2 = st.columns(2)
if model_selected != None and ticker != None:
st.write("You chose", model_selected)
if model_selected == "fin_distilBert":
model_to_use = "Andreagus/fin_distilbert_16"
if model_selected == "fin_tinyBert":
model_to_use = "Andreagus/fin_tinyBert_32"
if model_selected == "fin_miniLM12":
model_to_use = "Andreagus/fin_miniLM_16"
results = get_articles_sentiment(ticker, model_to_use)
with col1:
st.text("Bezinga news provider")
if results['bezinga']['bezinga_articles'] == 0:
st.text('Bezinga returned 0 articles')
else:
st.json(results['bezinga'], expanded=False)
st.text("finhub news provider")
if results['finhub']['finhub_articles'] == 0:
st.text('finhub returned 0 articles')
else:
st.json(results['finhub'], expanded=False)
st.text("marketaux news provider")
if results['marketaux']['marketaux_articles'] == 0:
st.text('marketaux returned 0 articles')
else:
st.json(results['marketaux'], expanded=False)
st.text("Newsapi news provider")
if results['newsapi']['newsapi_articles'] == 0:
st.text('newsapi returned 0 articles')
else:
st.json(results['newsapi'], expanded=False)
st.text('Newsdata news provider')
if results['newsdata']['newsdata_articles'] == 0:
st.text('newsdata returned 0 articles')
else:
st.json(results['newsdata'], expanded=False)
st.text("Vantage news provider")
if results['vantage']['vantage_articles'] == 0:
st.text('vantage returned 0 articles')
else:
st.json(results['vantage'], expanded=False)
with col2:
st.text("Summary results")
st.metric("Total articles", results['total_articles'])
st.metric("Total positive articles", results['total_positives'])
st.metric("Total negative articles", results['total_negatives'])
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