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import streamlit as st
from transformers import pipeline
# Set up the page configuration for a wide layout and better experience
st.set_page_config(
page_title="Live Transformer Demo",
layout="wide", # This will allow a wider layout for your app
)
# Add custom CSS for the header background
st.markdown("""
<style>
.header {
background-image: url('https://images.unsplash.com/photo-1557682250-48bfe2db9041');
background-size: cover;
padding: 60px;
text-align: center;
border-radius: 15px;
color: white;
font-family: 'Arial', sans-serif;
}
.header h1 {
font-size: 50px;
font-weight: bold;
}
.header p {
font-size: 20px;
margin-top: 10px;
}
.header a {
color: #ffcc00;
font-weight: bold;
text-decoration: none;
}
.header a:hover {
text-decoration: underline;
}
</style>
<div class="header">
<h1>🤗 Live Transformer Demo</h1>
<p>Explore Sentiment Analysis and Translation using models from <a href="https://huggingface.co/" target="_blank">Hugging Face</a>.</p>
</div>
""", unsafe_allow_html=True)
# Add an explanation of the app with markdown
st.markdown("""
Welcome to the Transformer NLP Demo! This app showcases **Sentiment Analysis** and **Translation** tasks.
- 🔍 **Sentiment Analysis** for understanding opinions.
- 🌐 **Translation** across multiple languages, including Albanian, Dutch, French, German, Hindi, Indonesian, Italian, Mandarin (Chinese), Russian, and Spanish.
Simply choose a task below, enter your text, and click 'Run' to see the results!
""")
# Two-column layout
col1, col2 = st.columns([2, 1])
# Left column for input and task selection
with col1:
st.subheader("Start Exploring")
# Task selection
task = st.selectbox("Choose a task", ["Sentiment Analysis", "Translation"])
# Language selection for translation
target_language = None
if task == "Translation":
target_language = st.selectbox("Select language", [
"Albanian", "Dutch", "French", "German", "Hindi", "Indonesian",
"Italian", "Mandarin (Chinese)", "Russian", "Spanish"
])
# Text input from the user
user_input = st.text_area("Enter your text here:", height=150)
# Load the appropriate model with advanced caching
@st.cache_resource(ttl=24*3600, max_entries=10)
def load_model(task_name, target_language=None):
if task_name == "Sentiment Analysis":
return pipeline("sentiment-analysis")
elif task_name == "Translation":
if target_language == "Albanian":
return pipeline("translation_en_to_sq", model="Helsinki-NLP/opus-mt-en-sq")
elif target_language == "Dutch":
return pipeline("translation_en_to_nl", model="Helsinki-NLP/opus-mt-en-nl")
elif target_language == "French":
return pipeline("translation_en_to_fr", model="Helsinki-NLP/opus-mt-en-fr")
elif target_language == "German":
return pipeline("translation_en_to_de", model="Helsinki-NLP/opus-mt-en-de")
elif target_language == "Hindi":
return pipeline("translation_en_to_hi", model="Helsinki-NLP/opus-mt-en-hi")
elif target_language == "Indonesian":
return pipeline("translation_en_to_id", model="Helsinki-NLP/opus-mt-en-id")
elif target_language == "Italian":
return pipeline("translation_en_to_it", model="Helsinki-NLP/opus-mt-en-it")
elif target_language == "Mandarin (Chinese)":
return pipeline("translation_en_to_zh", model="Helsinki-NLP/opus-mt-en-zh")
elif target_language == "Russian":
return pipeline("translation_en_to_ru", model="Helsinki-NLP/opus-mt-en-ru")
elif target_language == "Spanish":
return pipeline("translation_en_to_es", model="Helsinki-NLP/opus-mt-en-es")
model = load_model(task, target_language)
# Cache the results of each task to avoid re-computation
@st.cache_data(ttl=24*3600, max_entries=50)
def analyze_sentiment(input_text):
return model(input_text)
@st.cache_data(ttl=24*3600, max_entries=50)
def translate_text(input_text):
return model(input_text)[0]["translation_text"]
# Perform the selected task
if st.button("Run"):
if user_input:
with st.spinner(f"Performing {task.lower()}..."):
if task == "Sentiment Analysis":
result = analyze_sentiment(user_input)[0]
label = result["label"]
score = result["score"]
st.success("Sentiment Analysis Result:")
st.write(f"**Label**: {label}, **Score**: {score:.4f}")
elif task == "Translation":
translation = translate_text(user_input)
st.success(f"Translation (English to {target_language}):")
st.write(translation)
else:
st.error("Please enter some text.") |