Lazy-load translation pipelines and add deps

app.py

@@ -1,71 +1,71 @@
 import gradio as gr
 from transformers import pipeline
 
-# Load models locally on the Hugging Face Space server
-# Note: In a real space, you can also use Inference Endpoints for even more speed
-models = {
-    "Spanish":   pipeline("translation_en_to_es", model="Helsinki-NLP/opus-mt-en-es"),
-    "German":    pipeline("translation_en_to_de", model="Helsinki-NLP/opus-mt-en-de"),
-    "Japanese":  pipeline("translation_en_to_ja", model="staka/fugumt-en-ja"),
-    "Ukrainian": pipeline("translation_en_to_uk",  model="Helsinki-NLP/opus-mt-en-uk"),
-    "Russian":   pipeline("translation_en_to_ru",  model="Helsinki-NLP/opus-mt-en-ru"),
-       
+MODEL_SPECS = {
+    "Spanish":   ("translation_en_to_es", "Helsinki-NLP/opus-mt-en-es"),
+    "German":    ("translation_en_to_de", "Helsinki-NLP/opus-mt-en-de"),
+    "Japanese":  ("translation_en_to_ja", "staka/fugumt-en-ja"),
+    "Ukrainian": ("translation_en_to_uk", "Helsinki-NLP/opus-mt-en-uk"),
+    "Russian":   ("translation_en_to_ru", "Helsinki-NLP/opus-mt-en-ru"),
 }
 
+CACHED_MODELS = {}
+
+def get_model(language: str):
+    if language not in CACHED_MODELS:
+        task, model_name = MODEL_SPECS[language]
+        CACHED_MODELS[language] = pipeline(task, model=model_name)
+    return CACHED_MODELS[language]
+
 def translate(text, language):
+    text = (text or "").strip()
     if not text:
         return "Please enter an English sentence."
-    
-    # Perform translation using the selected model
-    result = models[language](text)
-    return result[0]['translation_text']
 
-# Create the Gradio interface
-with gr.Blocks(title="Short Translation Web App") as demo:
+    model = get_model(language)
+    result = model(text)
+    return result[0]["translation_text"]
+
+CSS = """
+#output-box textarea {
+    background-color: #800000 !important;
+    color: white !important;
+    font-size: 1.2em !important;
+    font-weight: bold !important;
+    text-align: center !important;
+}
+"""
+
+with gr.Blocks(title="Short Translation Web App", css=CSS) as demo:
     gr.Markdown("# Short Translation")
-    
+
     with gr.Row():
         with gr.Column(scale=2):
             input_text = gr.Textbox(
-                label="English Sentence", 
+                label="English Sentence",
                 placeholder="Type your English sentence here...",
                 lines=2
             )
             with gr.Row():
                 translate_btn = gr.Button("Translate", variant="primary")
                 clear_btn = gr.Button("Clear")
-        
+
         with gr.Column(scale=1):
             lang_radio = gr.Radio(
-                choices=["Spanish", "German", "Japanese", "Ukrainian" , 
-                         "Russian"], 
+                choices=list(MODEL_SPECS.keys()),
                 label="Translation Language",
                 value="Spanish"
             )
-    
-    # Output area styled to resemble the maroon box (using Gradio's color system)
+
     output_text = gr.Textbox(
-        label="Translation", 
+        label="Translation",
         interactive=False,
         container=True,
         elem_id="output-box"
     )
 
-    # Custom CSS to mimic the maroon look from the Tkinter app
-    demo.css = """
-    #output-box textarea {
-        background-color: #800000 !important;
-        color: white !important;
-        font-size: 1.2em !important;
-        font-weight: bold !important;
-        text-align: center !important;
-    }
-    """
-
-    # Define button actions
     translate_btn.click(fn=translate, inputs=[input_text, lang_radio], outputs=output_text)
-    clear_btn.click(fn=lambda: ("", ""), inputs=None, outputs=[input_text, output_text])
+    clear_btn.click(fn=lambda: ("", "Spanish", ""), inputs=None, outputs=[input_text, lang_radio, output_text])
 
-# Launch the app
 if __name__ == "__main__":
     demo.launch()