Update app.py

app.py

@@ -140,7 +140,7 @@ sp_pseudo = spm.SentencePieceProcessor(model_file="pseudo.model")  # For decodin
 sp_code = spm.SentencePieceProcessor(model_file="code.model")      # For encoding C++ (source)
 
 # Load the full saved model (architecture + weights)
-model_path = "transformer_cpp_to_pseudo.pth"  # Adjust path to your C++ to pseudocode model
+model_path = "transformer_cpp_to_pseudo.pth"
 model = torch.load(model_path, map_location=device, weights_only=False)
 model.eval()
 model = model.to(device)
@@ -170,17 +170,26 @@ def respond(message, history, max_tokens):
     for response in generate_pseudocode(message, max_tokens):
         yield response
 
-# Gradio interface
-demo = gr.ChatInterface(
-    respond,
-    chatbot=gr.Chatbot(label="C++ to Pseudocode Generator"),
-    textbox=gr.Textbox(placeholder="Enter C++ code (e.g., 'int x = 5; for(int i=0; i<x; i++) cout << i;')", label="C++ Code"),
-    additional_inputs=[
-        gr.Slider(minimum=10, maximum=1000, value=50, step=1, label="Max tokens"),
-    ],
-    title="C++ to Pseudocode Transformer",
-    description="Convert C++ code to pseudocode using a custom transformer trained on the SPoC dataset.",
-)
+# Gradio UI setup with Blocks
+with gr.Blocks(title="C++ to Pseudocode Transformer") as demo:
+    gr.Markdown("## C++ to Pseudocode Converter")
+    gr.Markdown("Enter C++ code below and press Submit to generate pseudocode.")
+    cpp_input = gr.Textbox(
+        label="C++ Code",
+        placeholder="e.g., 'int x = 5; for(int i=0; i<x; i++) cout << i;'",
+        lines=5
+    )
+    submit_btn = gr.Button("Submit", variant="primary")
+    pseudocode_output = gr.Textbox(
+        label="Generated Pseudocode",
+        lines=5
+    )
+    
+    submit_btn.click(
+        fn=respond,
+        inputs=[cpp_input, gr.State(value=[]), gr.Slider(minimum=10, maximum=1000, value=50, step=1, visible=False)],
+        outputs=pseudocode_output
+    )
 
 if __name__ == "__main__":
     demo.launch()