Update app.py

app.py

@@ -76,11 +76,45 @@ def is_reduction(code_txt, label):
 # Define GUI
 
 with gr.Blocks() as pragformer_gui:
+
+    gr.Markdown(
+        """
+        # PragFormer Pragma Classifiction
+        
+        """)
+
+    #with gr.Row(equal_height=True):
+    with gr.Column():
+        gr.Markdown("## Input")
+        with gr.Row():
+            with gr.Column():
+                drop = gr.Dropdown(list(data.keys()), label="Random Code Snippet", value="LLNL/AutoParBench/benchmarks/Autopar/NPB3.0-omp-c/BT/bt/129")
+                sample_btn = gr.Button("Sample")
+            
+            pragma =  gr.Textbox(label="Pragma")
+
+        code_in = gr.Textbox(lines=5, label="Write some code and see if it should be parallelized with OpenMP")
+        submit_btn = gr.Button("Submit")
+    with gr.Column():
+        gr.Markdown("## Results")
+
+        with gr.Row():
+            label_out = gr.Textbox(label="Label")
+            confidence_out = gr.Textbox(label="Confidence")
+
+        with gr.Row():
+            private = gr.Textbox(label="Private", visible=False)
+            reduction = gr.Textbox(label="Reduction", visible=False)
+
+    submit_btn.click(fn=predict, inputs=code_in, outputs=[label_out, confidence_out])
+    submit_btn.click(fn=is_private, inputs=code_in, outputs=private)
+    submit_btn.click(fn=is_reduction, inputs=code_in, outputs=reduction)
+    sample_btn.click(fn=fill_code, inputs=drop, outputs=[pragma, code_in])
+
     gr.Markdown(
     """
-    # PragFormer Pragma Classifiction
+    ## Description
     
-
     In past years, the world has switched to many-core and multi-core shared memory architectures.
     As a result, there is a growing need to utilize these architectures by introducing shared memory parallelization schemes to software applications. 
     OpenMP is the most comprehensive API that implements such schemes, characterized by a readable interface. 
@@ -98,35 +132,15 @@ with gr.Blocks() as pragformer_gui:
 
     ![](https://user-images.githubusercontent.com/104314626/165228036-d7fadd8d-768a-4e94-bd57-0a77e1330082.png)
 
-    Link to [PragFormer](https://arxiv.org/abs/2204.12835) Paper
-    """)
 
-    with gr.Row():
-        
-        with gr.Column():
-            gr.Markdown("## Input")
-            with gr.Row():
-                with gr.Column():
-                    drop = gr.Dropdown(list(data.keys()), label="Random Code Snippet", value="LLNL/AutoParBench/benchmarks/Autopar/NPB3.0-omp-c/BT/bt/129")
-                    sample_btn = gr.Button("Sample")
-                
-                pragma =  gr.Textbox(label="Pragma")
-
-            code_in = gr.Textbox(lines=5, label="Write some code and see if it should be parallelized with OpenMP")
-            submit_btn = gr.Button("Submit")
-        with gr.Column():
-            gr.Markdown("## Results")
-            label_out = gr.Textbox(label="Label")
-            confidence_out = gr.Textbox(label="Confidence")
+    ## How it Works?
 
-            with gr.Row():
-                private = gr.Textbox(label="Private", visible=False)
-                reduction = gr.Textbox(label="Reduction", visible=False)
+    To use the PragFormer tool, you will need to input a C language for-loop. You can either write your own code or use the samples
+    provided in the dropdown menu, which have been gathered from GitHub. Once you submit the code, the PragFormer model will analyze
+    it and predict whether the for-loop should be parallelized using OpenMP. If the PragFormer model determines that parallelization
+    is necessary, two additional models will be used to determine if private or reduction clauses are needed.
+    """)
 
-    submit_btn.click(fn=predict, inputs=code_in, outputs=[label_out, confidence_out])
-    submit_btn.click(fn=is_private, inputs=code_in, outputs=private)
-    submit_btn.click(fn=is_reduction, inputs=code_in, outputs=reduction)
-    sample_btn.click(fn=fill_code, inputs=drop, outputs=[pragma, code_in])
 
 
 pragformer_gui.launch()