app.py

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.")