Update demo.py

demo.py

@@ -43,6 +43,15 @@ css = """
     margin: 0 5px;
     color: black;
 }
+.row {
+    display: flex;
+    justify-content: center;
+    width: 100%;
+}
+.gradio-row input, .gradio-row select, .gradio-row button {
+    width: 250px;  /* Set all elements to the same width */
+    margin: 5px;
+}
 """
 
 # Gradio App
@@ -50,17 +59,15 @@ with gr.Blocks(css=css) as app:
     with gr.Row():
         gr.HTML('<div id="header">R-detect On HuggingFace</div>')
     with gr.Row():
-        gr.HTML(
-            """
-            <div class="links">
-                <a href="https://openreview.net/forum?id=z9j7wctoGV" target="_blank">Paper</a>
-                <span class="separator">|</span>
-                <a href="https://github.com/xLearn-AU/R-Detect" target="_blank">Code</a>
-                <span class="separator">|</span>
-                <a href="mailto:[email protected]" target="_blank">Contact</a>
-            </div>
-            """
-        )
+        gr.HTML("""
+        <div class="links">
+            <a href="https://openreview.net/forum?id=z9j7wctoGV" target="_blank">Paper</a>
+            <span class="separator">|</span>
+            <a href="https://github.com/xLearn-AU/R-Detect" target="_blank">Code</a>
+            <span class="separator">|</span>
+            <a href="mailto:[email protected]" target="_blank">Contact</a>
+        </div>
+        """)
     with gr.Row():
         input_text = gr.Textbox(
             label="Input Text",
@@ -69,7 +76,7 @@ with gr.Blocks(css=css) as app:
         )
         output = gr.Textbox(
             label="Inference Result",
-            placeholder="Maked by Human or AI",
+            placeholder="Made by Human or AI",
             elem_id="output-text",
         )
     with gr.Row():
@@ -84,31 +91,27 @@ with gr.Blocks(css=css) as app:
         )
         submit_button = gr.Button("Run Detection", variant="primary")
         clear_button = gr.Button("Clear", variant="secondary")
-
     
+    # Hooking up the change_mode function to select different models
     submit_button.click(run_test_power, inputs=[model_name, input_text, input_text], outputs=output)
     clear_button.click(lambda: ("", ""), inputs=[], outputs=[input_text, output])
-#    model_name.change(change_mode, inputs=[model_name], outputs=[model_name])
 
     with gr.Accordion("Disclaimer", open=False):
-        gr.Markdown(
-            """
-            - **Disclaimer**: This tool is for demonstration purposes only. It is not a foolproof AI detector.
-            - **Accuracy**: Results may vary based on input length and quality.
-            """
-        )
+        gr.Markdown("""
+        - **Disclaimer**: This tool is for demonstration purposes only. It is not a foolproof AI detector.
+        - **Accuracy**: Results may vary based on input length and quality.
+        """)
+
     with gr.Accordion("Citations", open=False):
-        gr.Markdown(
-            """
-            ```
-            @inproceedings{zhangs2024MMDMP,
-                title={Detecting Machine-Generated Texts by Multi-Population Aware Optimization for Maximum Mean Discrepancy},
-                author={Zhang, Shuhai and Song, Yiliao and Yang, Jiahao and Li, Yuanqing and Han, Bo and Tan, Mingkui},
-                booktitle = {International Conference on Learning Representations (ICLR)},
-                year={2024}
-            }
-            ```
-            """
-        )
+        gr.Markdown("""
+        ```
+        @inproceedings{zhangs2024MMDMP,
+            title={Detecting Machine-Generated Texts by Multi-Population Aware Optimization for Maximum Mean Discrepancy},
+            author={Zhang, Shuhai and Song, Yiliao and Yang, Jiahao and Li, Yuanqing and Han, Bo and Tan, Mingkui},
+            booktitle = {International Conference on Learning Representations (ICLR)},
+            year={2024}
+        }
+        ```
+        """)
 
 app.launch()