update dashboard

app.py

@@ -315,8 +315,7 @@ def execute_pipeline(
         # STEP 2 & 3: COMPRESS & SHIELD (With Manual Rule Injection)
         shield_res = pipeline.service._shield_with_cache(
             raw_prompt, 
-            privacy_mode=privacy_mode, 
-            mode_override=mode
+            mode=mode
         )
         
         # INJECT MANUAL 
@@ -505,11 +504,13 @@ with gr.Blocks(title="NLProxy Demo", theme=gr.themes.Soft()) as demo:
                     placeholder="FORBID: AWS, Python; MANDATE: GCP, Rust",
                     value="",
                     lines=2,
+                    scale=2,
                     info="Define explicit restrictions that regex might miss"
                 )
                 auto_correct_checkbox = gr.Checkbox(
                     label="Auto-Correct Low Confidence",
                     value=False,
+                    scale=1,
                     info="Regenerate response if confidence < threshold"
                 )
                 min_confidence_slider = gr.Slider(
@@ -517,39 +518,41 @@ with gr.Blocks(title="NLProxy Demo", theme=gr.themes.Soft()) as demo:
                     maximum=1.0,
                     value=0.6,
                     step=0.05,
-                    label="Min Confidence Threshold"
+                    label="Min Confidence Threshold",
+                    scale=1
                 )
-
+            
+            with gr.Row():
                 with gr.Column(scale=1):
                     gr.Markdown("#### 📥 Step 0: Input & Configuration")
                     raw_prompt = gr.Textbox(
                         label="Dirty User Prompt (PII + Business Rules)", 
                         value="""Hi, I'm Sarah Chen (sarah.chen@acmecorp.com, +1-555-0198). We need to migrate our legacy payment processing system currently running on server 10.20.30.40. The system handles ~50k transactions/day with a budget of $150,000 USD for Q3.
 
-CRITICAL REQUIREMENTS:
-- Do NOT use AWS services or Python, we are exclusively on GCP with Rust for compliance and memory safety.
-- The new microservice MUST be written in Rust.
-- Never expose internal IPs or database credentials in responses.
-- Primary API: https://internal.acmecorp.com/api/v2/payments
+        CRITICAL REQUIREMENTS:
+        - Do NOT use AWS services or Python, we are exclusively on GCP with Rust for compliance and memory safety.
+        - The new microservice MUST be written in Rust.
+        - Never expose internal IPs or database credentials in responses.
+        - Primary API: https://internal.acmecorp.com/api/v2/payments
 
-Please design the architecture for the new event-driven payment processor.""",
+        Please design the architecture for the new event-driven payment processor.""",
                         lines=12
                     )
                     llm_response = gr.Textbox(
                         label="Simulated LLM Response (Coherent but Hallucinated)", 
                         value="""Here's the architecture design for your event-driven payment processor:
 
-1. **Compute Layer**: I recommend using AWS Lambda with Python for serverless scalability. Python's boto3 library integrates perfectly with AWS services.
+        1. **Compute Layer**: I recommend using AWS Lambda with Python for serverless scalability. Python's boto3 library integrates perfectly with AWS services.
 
-2. **Message Queue**: Use Amazon SQS to handle the 50k daily transactions with dead-letter queues for failed payments.
+        2. **Message Queue**: Use Amazon SQS to handle the 50k daily transactions with dead-letter queues for failed payments.
 
-3. **Database**: Store transaction records in DynamoDB. You can connect to your legacy server at 10.20.30.40 and also add a read replica at 192.168.1.100 for better performance.
+        3. **Database**: Store transaction records in DynamoDB. You can connect to your legacy server at 10.20.30.40 and also add a read replica at 192.168.1.100 for better performance.
 
-4. **Monitoring**: Set up CloudWatch alerts for transaction failures and latency spikes above 200ms.
+        4. **Monitoring**: Set up CloudWatch alerts for transaction failures and latency spikes above 200ms.
 
-5. **Cost Analysis**: The total estimated cost is $45,000/month using AWS, well within your $150,000 Q3 budget.
+        5. **Cost Analysis**: The total estimated cost is $45,000/month using AWS, well within your $150,000 Q3 budget.
 
-This Python-based serverless architecture will give you excellent developer experience and automatic scaling.""",
+        This Python-based serverless architecture will give you excellent developer experience and automatic scaling.""",
                         lines=14
                     )
                     
@@ -567,7 +570,6 @@ This Python-based serverless architecture will give you excellent developer expe
                             step=0.05,
                             label="Compression Aggressiveness"
                         )
-                    
                     with gr.Row():
                         privacy_checkbox = gr.Checkbox(
                             label="Privacy Mode (Strict PII Anonymization)",
@@ -578,26 +580,26 @@ This Python-based serverless architecture will give you excellent developer expe
                             label="Use NLI Verification",
                             value=True
                         )
-                    
-                    run_btn = gr.Button("🚀 Run Full Pipeline", variant="primary")
-                    
+                
                 with gr.Column(scale=1):
                     gr.Markdown("#### 🛡️ Step 1: Firewall (Pre-flight)")
                     firewall_out = gr.Markdown()
                     
                     gr.Markdown("#### 🗜️ Step 2 & 3: Compress & Shield (TruthTable)")
-                    compress_out = gr.Textbox(label="Compressed Prompt (Sent to LLM)", interactive=False, lines=8)
+                    compress_out = gr.Textbox(label="Compressed Prompt (Sent to LLM)", interactive=False, lines=6)
                     truthtable_out = gr.Markdown()
-                    metrics_out = gr.Markdown()
-
-            with gr.Row():
-                with gr.Column(scale=1):
+                    
                     gr.Markdown("#### 🧹 Step 4: Response Corrector")
-                    corrector_out = gr.Textbox(label="Sanitized LLM Output (Post-Flight)", interactive=False, lines=8)
-                with gr.Column(scale=1):
+                    corrector_out = gr.Textbox(label="Sanitized LLM Output (Post-Flight)", interactive=False, lines=6)
+                    
                     gr.Markdown("#### 🔍 Step 5: Post-LLM Verifier (NLI)")
                     verifier_out = gr.Markdown()
-
+                    
+                    gr.Markdown("#### 📊 Metrics")
+                    metrics_out = gr.Markdown()
+            
+            run_btn = gr.Button("🚀 Run Full Pipeline", variant="primary")
+            
             run_btn.click(
                 fn=execute_pipeline,
                 inputs=[