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import streamlit as st
import torch as nn
import requests
from transformers import pipeline
############ 1. PAGE LAYOUT, TITLE
st.set_page_config(
layout="centered", page_title='Simple Sentiment Analysis App Using\n\
Hugging Face Model Library', page_icon="(y)"
)
c1,c2,c3 = st.columns([1,3,1])
with c1:
st.write("")
with c2:
st.image("images/emotions.png")
with c3:
st.write("")
# prepare a list of top sentiment analysis models including default
models = ["distilbert-base-uncased-finetuned-sst-2-english",#default
"bhadresh-savani/distilbert-base-uncased-emotion",#emotions
"ProsusAI/finbert",#finance
"finiteautomata/bertweet-base-sentiment-analysis",#tweets
"cardiffnlp/twitter-roberta-base-sentiment"#tweet2
]
model_pointers = ["default: distilbert-base-uncased-finetuned-sst-2-english",
"emotion: bhadresh-savani/distilbert-base-uncased-emotion",
"finance: ProsusAI/finbert",
"tweets: finiteautomata/bertweet-base-sentiment-analysis",
"tweets2: cardiffnlp/twitter-roberta-base-sentiment"
]
#Prompt User for input text for sentiment analysis, keep input and model selection in form to delay page refresh
with st.form(key="init_form"):
input_text = st.text_area("Input a sentence on which to perform sentiment\
analysis", value="I love Streamlit and I love Data Science!")
choice = st.selectbox("Choose Model", model_pointers)
# The index of choice in model_pointers will access the models list
# and select the Hugging Face model path at index.
user_picked_model = models[model_pointers.index(choice)]
with st.spinner("Downloading Model"):
sentiment_pipeline = pipeline(model=user_picked_model)
analyze = st.form_submit_button("Analyze")
if analyze:
with st.spinner("Analyzing..."):
sentiment_pipeline = pipeline(model=user_picked_model)
sentiment_results=sentiment_pipeline(input_text)
st.write(f"Sentiment: {sentiment_results[0]['label']}")
st.write(f"Score: {sentiment_results[0]['score']}")
else:
st.write("no input detected")
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