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import streamlit as st
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import plotly.graph_objects as go
# Page config
st.set_page_config(
page_title="Emotion Detector",
page_icon="📊",
layout="wide"
)
@st.cache_resource
def load_model():
tokenizer = AutoTokenizer.from_pretrained("j-hartmann/emotion-english-distilroberta-base")
model = AutoModelForSequenceClassification.from_pretrained("j-hartmann/emotion-english-distilroberta-base")
return tokenizer, model
def analyze_text(text, tokenizer, model):
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
outputs = model(**inputs)
probs = torch.nn.functional.softmax(outputs.logits, dim=-1)
return probs[0].detach().numpy()
def create_emotion_plot(emotions_dict):
fig = go.Figure(data=[
go.Bar(
x=list(emotions_dict.keys()),
y=list(emotions_dict.values()),
marker_color=['#FF9999', '#99FF99', '#9999FF', '#FFFF99', '#FF99FF', '#99FFFF', '#FFB366']
)
])
fig.update_layout(
title="Emotion Analysis Results",
xaxis_title="Emotions",
yaxis_title="Confidence Score",
yaxis_range=[0, 1]
)
return fig
# App title and description
st.title("📊 Text Emotion Analysis")
st.markdown("""
This app analyzes the emotional content of your text using a pre-trained emotion detection model.
Try typing or pasting some text below!
""")
# Load model
with st.spinner("Loading model..."):
tokenizer, model = load_model()
# Define emotions
emotions = ['anger', 'disgust', 'fear', 'joy', 'neutral', 'sadness', 'surprise']
# Text input
text_input = st.text_area("Enter your text here:", height=150)
# Add example button
if st.button("Try an example"):
text_input = "I just got the best news ever! I'm so excited and happy I can hardly contain myself! 🎉"
st.text_area("Enter your text here:", value=text_input, height=150)
if st.button("Analyze Emotions"):
if text_input.strip() == "":
st.warning("Please enter some text to analyze.")
else:
with st.spinner("Analyzing emotions..."):
# Get predictions
probs = analyze_text(text_input, tokenizer, model)
emotions_dict = dict(zip(emotions, probs))
# Display results
st.subheader("Analysis Results")
# Create columns for layout
col1, col2 = st.columns([2, 1])
with col1:
# Display plot
fig = create_emotion_plot(emotions_dict)
st.plotly_chart(fig, use_container_width=True)
with col2:
# Display scores
st.subheader("Emotion Scores:")
for emotion, score in emotions_dict.items():
st.write(f"{emotion.capitalize()}: {score:.2%}")
# Add footer
st.markdown("---")
st.markdown("""
Created with ❤️ using Hugging Face Transformers and Streamlit.
Model: j-hartmann/emotion-english-distilroberta-base
""") |