Create app.py

app.py

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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.")