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import torch
import numpy as np
import streamlit as st
from torch.nn import Softmax
import plotly.graph_objects as go
from transformers import AutoConfig, AutoTokenizer
from transformers import AutoModelForSequenceClassification
st.set_page_config(
page_title="Sentiment Analysis",
page_icon="🧠")
st.write("# Sentiment Analysis")
MODEL = f"cardiffnlp/twitter-roberta-base-sentiment-latest"
tokenizer = AutoTokenizer.from_pretrained(MODEL)
config = AutoConfig.from_pretrained(MODEL)
model = AutoModelForSequenceClassification.from_pretrained(MODEL)
user_input = st.text_input('What\'s in your mind?')
if st.button("Perform Sentiment Analysis"):
if not user_input:
st.warning("Please enter some text!")
else:
try:
st.write("## Sentiment Plot")
encoded_input = tokenizer(user_input, return_tensors='pt')
output = model(**encoded_input)
scores = output[0][0].detach().numpy()
softmax = Softmax(dim=1)
scores = softmax(torch.tensor([scores]))
scores = scores.numpy()[0]
categories = []
probabilities = []
ranking = np.argsort(scores)
ranking = ranking[::-1]
for i in range(scores.shape[0]):
categories.append(config.id2label[ranking[i]])
probabilities.append(np.round(float(scores[ranking[i]]), 4).tolist())
res = [[cat, sco] for cat,sco in zip(categories, probabilities)]
res.sort(key=lambda x: x[0], reverse=True)
probabilities = [i[1] for i in res]
# Create the bar chart
fig = go.Figure(data=[
go.Bar(
x=['Positive', 'Neutral', 'Negative'],
y=probabilities,
marker_color=['green', 'blue', 'red'], # Colors for each category
text=probabilities, # Show values on the bars
textposition='auto'
)
])
# Customize layout
fig.update_layout(
# title="Sentiment Analysis Results",
xaxis_title="Sentiment Categories",
yaxis_title="Probability",
template="plotly_white"
)
# Show the figure
st.plotly_chart(fig, use_container_width=True)
except Exception as e:
st.error("An error occurred: " + str(e))
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