Update app.py

app.py

@@ -1,27 +1,14 @@
 import gradio as gr
 from transformers import pipeline
-import requests
 
-# Load translation pipelines
-translator_en_fi = pipeline(
-    "translation_en_to_fi", 
-    model="Helsinki-NLP/opus-mt-en-fi", 
-    # Optional: cache_dir="./model_cache", 
-    # Optional parameters:
-    # max_length=512, num_beams=5
-)
-translator_fi_en = pipeline(
-    "translation_fi_to_en", 
-    model="Helsinki-NLP/opus-mt-fi-en",
-    # max_length=512, num_beams=5
-)
+# Initialize the translation pipelines
+translator_en_fi = pipeline("translation_en_to_fi", model="Helsinki-NLP/opus-mt-en-fi")
+translator_fi_en = pipeline("translation_fi_to_en", model="Helsinki-NLP/opus-mt-fi-en")
 
 def translate(text, direction):
     text = text.strip()
     if not text:
         return "Please enter some text for translation."
-    
-    # Simple input validation
     if len(text) > 2000:
         return "Input text too long. Please shorten it."
     
@@ -31,7 +18,6 @@ def translate(text, direction):
         result = translator_fi_en(text)[0]['translation_text']
     return result
 
-# Optional: Provide some example texts to guide users.
 examples = [
     ["Hello, how are you?", "en-fi"],
     ["Mitä kuuluu?", "fi-en"]
@@ -45,13 +31,9 @@ iface = gr.Interface(
     ],
     outputs=gr.Textbox(label="Translated Text"),
     title="English-Finnish Translation App",
-    description=(
-        "This application uses Helsinki-NLP translation models "
-        "to translate text between English and Finnish."
-    ),
-    examples=examples,
-    allow_flagging="never",  # Disables any flagging if you don't need it
-    enable_queue=True  # Allows request queuing if concurrency is needed
+    description="This application uses Helsinki-NLP translation models to translate text between English and Finnish.",
+    examples=examples
 )
 
-iface.launch()
+if __name__ == "__main__":
+    iface.launch()