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import streamlit as st
from transformers import pipeline
import numpy as np
import torch
# Set page configuration
st.set_page_config(
page_title="Sentiment Analysis API",
page_icon="😀",
layout="centered",
initial_sidebar_state="collapsed",
)
# App title and description
st.title("Sentiment Analysis API")
st.write("This API uses a pre-trained BERT model to classify text sentiment as positive, negative, or neutral.")
# Load the sentiment analysis model
@st.cache_resource
def load_model():
return pipeline('sentiment-analysis', model="cardiffnlp/twitter-roberta-base-sentiment")
# Get the model
model = load_model()
# Sample text examples
examples = [
"I absolutely love this new feature! It's amazing.",
"This product is terrible and doesn't work as advertised.",
"The weather is just okay today, nothing special."
]
# Create the UI elements
text_input = st.text_area("Enter text to analyze:", height=150,
placeholder="Type or paste your text here...")
# Add example buttons
st.write("Or try one of these examples:")
col1, col2, col3 = st.columns(3)
with col1:
if st.button("Positive Example"):
text_input = examples[0]
with col2:
if st.button("Negative Example"):
text_input = examples[1]
with col3:
if st.button("Neutral Example"):
text_input = examples[2]
# Function to analyze and display sentiment
def analyze_sentiment(text):
try:
result = model(text)[0]
# Map labels to user-friendly sentiment names
sentiment_mapping = {
'LABEL_0': 'Negative',
'LABEL_1': 'Neutral',
'LABEL_2': 'Positive'
}
sentiment = sentiment_mapping[result['label']]
confidence = result['score']
# Display results with color-coded box
if sentiment == "Positive":
st.success(f"Sentiment: {sentiment} (Confidence: {confidence:.4f})")
elif sentiment == "Negative":
st.error(f"Sentiment: {sentiment} (Confidence: {confidence:.4f})")
else:
st.info(f"Sentiment: {sentiment} (Confidence: {confidence:.4f})")
# Display confidence as a progress bar
st.progress(confidence)
# Show detailed sentiment breakdown
st.subheader("Sentiment Breakdown")
sentiment_data = {
'Sentiment': ['Negative', 'Neutral', 'Positive'],
'Score': [0, 0, 0] # Default values
}
# Update the score for the detected sentiment
if sentiment == "Positive":
sentiment_data['Score'][2] = confidence
elif sentiment == "Negative":
sentiment_data['Score'][0] = confidence
else:
sentiment_data['Score'][1] = confidence
# Display as a horizontal bar chart
st.bar_chart(sentiment_data, x='Sentiment', y='Score')
except Exception as e:
st.error(f"An error occurred: {str(e)}")
# Process the text when the analyze button is clicked
if st.button("Analyze Sentiment") and text_input:
with st.spinner("Analyzing sentiment..."):
analyze_sentiment(text_input)
elif text_input:
st.info("Click 'Analyze Sentiment' to process the text.")
else:
st.info("Please enter some text to analyze.")
def analyze_sentiment(text):
try:
if len(text.split()) > 512:
st.error("Input too long (max 512 words). Please shorten the text.")
return
result = model(text)[0]
sentiment_mapping = {
'LABEL_0': 'Negative',
'LABEL_1': 'Neutral',
'LABEL_2': 'Positive'
}
sentiment = sentiment_mapping[result['label']]
confidence = result['score']
if sentiment == "Positive":
st.success(f"Sentiment: {sentiment} (Confidence: {confidence:.4f})")
elif sentiment == "Negative":
st.error(f"Sentiment: {sentiment} (Confidence: {confidence:.4f})")
else:
st.info(f"Sentiment: {sentiment} (Confidence: {confidence:.4f})")
st.progress(confidence)
st.subheader("Sentiment Breakdown")
sentiment_data = {
'Sentiment': ['Negative', 'Neutral', 'Positive'],
'Score': [0, 0, 0]
}
if sentiment == "Positive":
sentiment_data['Score'][2] = confidence
elif sentiment == "Negative":
sentiment_data['Score'][0] = confidence
else:
sentiment_data['Score'][1] = confidence
st.bar_chart(sentiment_data, x='Sentiment', y='Score')
except Exception as e:
st.error(f"Error analyzing sentiment: {str(e)}. Please try again or use shorter text.")
# Add information about the model
with st.expander("About the Model"):
st.write("""
This application uses the `cardiffnlp/twitter-roberta-base-sentiment` model from Hugging Face.
The model is a RoBERTa-base model trained on ~58M tweets and fine-tuned for sentiment analysis
with the TweetEval benchmark. It classifies text into three sentiment categories:
- Negative (LABEL_0)
- Neutral (LABEL_1)
- Positive (LABEL_2)
Source: [cardiffnlp/twitter-roberta-base-sentiment](https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment)
""")
with st.expander("Model Performance"):
st.write("Tested on 100 samples from `tweet_eval` dataset.")
if st.button("Show Test Accuracy"):
from datasets import load_dataset
dataset = load_dataset("tweet_eval", "sentiment", split="test[:100]")
correct = sum(1 for text, label in zip(dataset['text'], dataset['label']) if (2 if model(text)[0]['label'] == 'LABEL_2' else 0 if model(text)[0]['label'] == 'LABEL_0' else 1) == label)
st.write(f"Accuracy: {correct/100:.2f}")
# Footer
st.markdown("---")
st.markdown("Created as part of Mini Project 1: Sentiment Analysis API") |