Create app.py

app.py

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+import gradio as gr
+from transformers import pipeline
+
+prefix = "<2id> "
+madlad = pipeline("translation", model="google/madlad400-3b-mt")
+lulu = pipeline("translation", model="tirtohadi/lulu7")
+
+
+def translate(text):
+    # Split input text into paragraphs
+    paragraphs = text.split("\n\n")  # Assuming paragraphs are separated by two newline characters
+    
+    # Translate each paragraph
+    translated_paragraphs_lulu = []
+    translated_paragraphs_madlad = []
+    for paragraph in paragraphs:
+        # Call your custom model here to translate each paragraph
+        translated_paragraph_madlad = madlad(prefix + paragraph, max_length=400)[0]["translation_text"]   
+        translated_paragraphs_madlad.append(translated_paragraph_madlad)
+    
+        translated_paragraph_lulu = lulu(paragraph, max_length=400)[0]["translation_text"]   
+        translated_paragraphs_lulu.append(translated_paragraph_lulu)
+
+    
+    # Join translated paragraphs back into text
+    translated_text_lulu = "\n\n".join(translated_paragraphs_lulu)
+    translated_text_madlad = "\n\n".join(translated_paragraphs_madlad)
+    
+    return translated_text_lulu,translated_text_madlad
+    
+with gr.Blocks() as demo:
+    gr.HTML("<h2>Lulu - Google Comparison</h2>")
+    gr.Markdown("This app compares translations between Lulu (Christian domain specific trained) and Google (Madlad-400-3B-MT)")
+    with gr.Row():        
+        input_text1 = gr.Textbox(label="English Text",lines=5)
+        output_lulu = gr.Textbox(label="Indonesian - Lulu",lines=5)
+        output_madlad = gr.Textbox(label="Indonesian - Google",lines=5)        
+    with gr.Row():
+        with gr.Column(scale=2):        
+            btn = gr.Button("Translate")
+            btn.click(fn=translate, inputs=input_text1, outputs=[output_lulu,output_madlad], api_name="translate")
+
+demo.launch()