Update app.py

app.py

@@ -2,7 +2,6 @@ import gradio as gr
 from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, pipeline
 
 # Load the translation model and tokenizer from Hugging Face
-# Here, we use the "Helsinki-NLP" translation models
 model_name = "Helsinki-NLP/opus-mt-en-fr"  # Example model for English to French
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
@@ -15,34 +14,23 @@ def translate_text(text, target_language):
     translation = translator(text)[0]['translation_text']
     return translation
 
-# Set up the Gradio interface
+# Define available languages
 languages = {
-    "en": "English",
-    "es": "Spanish",
     "fr": "French",
+    "es": "Spanish",
     "de": "German",
-    "zh": "Chinese (Mandarin)",
-    "ja": "Japanese",
-    "ko": "Korean",
     "it": "Italian",
-    "pt": "Portuguese",
-    "ru": "Russian",
-    "hi": "Hindi",
-    "ar": "Arabic",
-    "nl": "Dutch",
-    "tr": "Turkish",
-    "el": "Greek",
-    "ur": "Urdu"
+    "ru": "Russian"
 }
 
-
+# Set up the Gradio interface
 iface = gr.Interface(
     fn=translate_text,
     inputs=[
-        gr.inputs.Textbox(label="Enter text to translate"),
-        gr.inputs.Dropdown(list(languages.keys()), label="Target Language", type="value")
+        gr.Textbox(label="Enter text to translate"),
+        gr.Dropdown(list(languages.keys()), label="Target Language", type="value")
     ],
-    outputs="text",
+    outputs=gr.Textbox(label="Translation"),
     live=True,
     title="Text Translator",
     description="Translate text into multiple languages using Hugging Face models."