Update app.py

app.py

@@ -145,29 +145,42 @@ with gr.Blocks(title="Science Text Analyzer") as demo:
     gr.Markdown("# Science Text Analyzer")
     
     with gr.Tab("Classify Text"):
-        gr.Markdown("## Classify scientific text into Science, Maths, or History")
+        gr.Markdown("### Classify Academic Text")
+        gr.Markdown(
+            "This tool automatically classifies a given passage into one of the following academic categories: "
+            "**Science**, **Mathematics**, or **History**. Simply enter your text below to see the predicted subject."
+        )
         with gr.Row():
             with gr.Column():
-                text_input = gr.Textbox(label="Enter text", lines=5)
+                text_input = gr.Textbox(label="Enter Text", lines=5, placeholder="Paste a sentence or paragraph here...")
                 classify_button = gr.Button("Classify")
             with gr.Column():
-                output = gr.Textbox(label="Classification Result")
+                output = gr.Textbox(label="Classification Result", placeholder="The predicted subject and confidence will appear here.")
         classify_button.click(fn=classify_interface, inputs=text_input, outputs=output)
     
     with gr.Tab("Generate Text"):
-        gr.Markdown("## Generate scientific text based on a prompt")
+        gr.Markdown("### Generate Academic Text")
+        gr.Markdown(
+            "Use this tool to generate educational text based on a given prompt. "
+            "You can control the output length and creativity using the sliders below."
+        )
         with gr.Row():
             with gr.Column():
-                prompt_input = gr.Textbox(label="Enter a prompt", lines=3)
-                length_slider = gr.Slider(minimum=10, maximum=200, value=50, step=10, label="Maximum Length")
-                temp_slider = gr.Slider(minimum=0.1, maximum=1.5, value=0.7, step=0.1, label="Temperature (Creativity)")
+                prompt_input = gr.Textbox(label="Enter a Prompt", lines=3, placeholder="Type an introductory sentence or concept...")
+                length_slider = gr.Slider(minimum=10, maximum=200, value=50, step=10, label="Maximum Length (words)")
+                temp_slider = gr.Slider(minimum=0.1, maximum=1.5, value=0.7, step=0.1, label="Temperature (Creativity Level)")
                 generate_button = gr.Button("Generate")
             with gr.Column():
-                generated_output = gr.Textbox(label="Generated Text", lines=8)
+                generated_output = gr.Textbox(label="Generated Text", lines=8, placeholder="The generated text will appear here.")
         generate_button.click(fn=generate_interface, inputs=[prompt_input, length_slider, temp_slider], outputs=generated_output)
 
-    gr.Markdown("### About")
-    gr.Markdown("This app uses deep learning models trained on scientific texts to classify and generate content related to Physics, Chemistry, and Biology.")
+    gr.Markdown("### About This App")
+    gr.Markdown(
+        "The Science Text Analyzer uses deep learning models trained on academic corpora to classify and generate content "
+        "relevant to disciplines such as Science, Mathematics, and History. It combines a classifier with a sequence-based language model "
+        "to support educational research and content creation."
+    )
 
 # Launch the app
 demo.launch()
+