Create app.py

app.py

@@ -0,0 +1,130 @@
+import streamlit as st
+from transformers import MarianMTModel, MarianTokenizer
+import random
+
+# Map of language codes to full language names and flag images
+language_dict = {
+    "en": "English",
+    "de": "German",
+    "fr": "French",
+    "es": "Spanish",
+    "it": "Italian",
+    "nl": "Dutch",
+    "pt": "Portuguese",
+    "ro": "Romanian",
+    "ru": "Russian",
+    "zh": "Chinese"
+}
+
+# Reverse map for language selection
+language_codes = {v: k for k, v in language_dict.items()}
+
+# Add custom CSS for styling
+st.markdown("""
+    <style>
+        .stTextInput, .stTextArea {
+            background-color: #f4f6f8;
+            padding: 10px;
+            border-radius: 8px;
+        }
+        .stButton {
+            background-color: #4CAF50;
+            color: black;
+            padding: 10px 20px;
+            border-radius: 5px;
+        }
+        .stButton:hover {
+            background-color: #45a049;
+        }
+        .stText {
+            font-size: 1.2em;
+            font-family: 'Helvetica', sans-serif;
+        }
+        .footer {
+            padding: 10px;
+            background-color: #f1f1f1;
+            text-align: center;
+        }
+        h1 {
+            color: #4CAF50;
+        }
+    </style>
+""", unsafe_allow_html=True)
+
+# Function to load model and tokenizer
+def load_translation_model(src_lang, tgt_lang):
+    model_name = f'Helsinki-NLP/opus-mt-{src_lang}-{tgt_lang}'
+    model = MarianMTModel.from_pretrained(model_name)
+    tokenizer = MarianTokenizer.from_pretrained(model_name)
+    return model, tokenizer
+
+# Function to translate text
+def translate_text(text, src_lang, tgt_lang):
+    model, tokenizer = load_translation_model(src_lang, tgt_lang)
+
+    # Tokenize the input text
+    inputs = tokenizer(text, return_tensors="pt", padding=True)
+
+    # Translate the text
+    translated = model.generate(**inputs)
+
+    # Decode the translated text
+    translated_text = tokenizer.decode(translated[0], skip_special_tokens=True)
+
+    return translated_text
+
+# Language selector with flags and names
+def language_selector():
+    # Use two columns to display the language selectors
+    col1, col2 = st.columns(2)
+
+    with col1:
+        source_language = st.selectbox(
+            "Select source language",
+            list(language_dict.values()),
+            key="source_language"
+        )
+
+    with col2:
+        target_language = st.selectbox(
+            "Select target language",
+            list(language_dict.values()),
+            key="target_language"
+        )
+
+    return language_codes[source_language], language_codes[target_language]
+
+# Main application function
+def run_app():
+    st.title("🌍 Interactive Language Translation App")
+
+    st.subheader("Translate your text in real-time with ease! 🌐")
+
+    # Select source and target languages
+    src_lang_code, tgt_lang_code = language_selector()
+
+    # Text input field with placeholder
+    text_input = st.text_area("Enter text to translate", placeholder="Type here...", height=150)
+
+    # Option to upload a text file for input
+    uploaded_file = st.file_uploader("Or upload a text file", type=["txt"])
+
+    if uploaded_file is not None:
+        text_input = uploaded_file.read().decode("utf-8")
+        st.text_area("File content", value=text_input, height=150)
+
+    # Button for translation
+    if st.button("🔄 Translate", key="translate"):
+        if text_input:
+            with st.spinner("🔄 Translating... Please wait..."):
+                # Translate the text
+                translated_text = translate_text(text_input, src_lang_code, tgt_lang_code)
+                # Display translated text directly in the success container
+                st.success(f"✅ Translation: {translated_text}")
+            st.balloons()  # Show animation when translation is complete
+        else:
+            st.warning("⚠️ Please enter or upload some text to translate.")
+
+# Run the Streamlit app
+if __name__ == "__main__":
+    run_app()