Update app.py

app.py

@@ -94,8 +94,15 @@ def translate_text(input_text):
     translation_nmt_base = translate_text_base_nmt(input_text)
     translation_nmt_finetuned = translate_text_nmt(input_text)  
     return translation_lstm, translation_nmt_base, translation_nmt_finetuned
+examples = [
+    ["Microservices Architecture: Containers are ideal for deploying microservices, as each service can run in its own container and be independently managed."],
+    ["Kubernetes: An open-source container orchestration platform that automates the deployment, scaling, and management of containerized applications."],
+       ["Machine Learning: The practice of using algorithms to parse data, learn from it, and then make a determination or prediction about something in the world."],
+    ["Data Science: An interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from noisy, structured, and unstructured data."],
+    ["DevOps: A set of practices that combines software development and IT operations to shorten the development lifecycle and deliver high-quality software continuously."]
+
+]
 
-# Create the Gradio interface
 iface = gr.Interface(
     fn=translate_text,
     inputs=gr.components.Textbox(lines=2, placeholder="Enter text to translate from English to Hindi..."),
@@ -105,7 +112,8 @@ iface = gr.Interface(
         gr.components.Textbox(label="Translation (Fine-tuned Helsinki Model)")
     ],
     title="English to Hindi Translator",
-    description="Enter English text and get the Hindi translation from three different models: LSTM, Base Helsinki-NLP, and Fine-tuned Helsinki-NLP."
+    description="Enter English text and get the Hindi translation from three different models: LSTM, Base Helsinki-NLP, and Fine-tuned Helsinki-NLP. LSTM models both have a single hidden layer trained for 50 epochs, the GPT-2 has been trained for 5 epochs",
+    examples=examples
 )
 
 # Launch the Gradio app