Update app.py

app.py

@@ -2,6 +2,8 @@
 DeepSuite: The Quantum Auditor
 A tool to determine if your data requires quantum computing or if classical methods suffice.
 Implements the "Tang Test" (Dequantization Audit) from the DeepSuite Research Proposal.
+
+Authors: Eric Raymond & Myalou
 """
 
 import gradio as gr
@@ -461,7 +463,7 @@ def rag_consultant(ansatz_type, problem_domain):
             "entanglement": "Unstructured (no geometric prior)",
             "best_for": ["Hardware benchmarking", "Small proof-of-concept demos"],
             "warnings": [
-                "🚨 **BARREN PLATEAU RISK**: Gradients vanish exponentially with depth",
+                "🚨 BARREN PLATEAU RISK: Gradients vanish exponentially with depth",
                 "No inductive bias → poor generalization",
                 "Expressibility ≠ Trainability"
             ],
@@ -492,6 +494,7 @@ def rag_consultant(ansatz_type, problem_domain):
     return f"""## {r['icon']} {ansatz_type}
 
 ### Architecture Overview
+
 | Property | Value |
 |----------|-------|
 | **Structure** | {r['structure']} |
@@ -520,7 +523,7 @@ theory_content = """
 
 ### Why This Matters
 
-In 2018, **Ewin Tang** (then an 18-year-old undergraduate) shocked the quantum computing community by showing that many celebrated "quantum speedups" could be matched by classical algorithms—*if the data has low rank*.
+In 2018, **Ewin Tang** (then an 18-year-old undergraduate) shocked the quantum computing community by showing that many celebrated "quantum speedups" could be matched by classical algorithms, if the data has low rank.
 
 This tool implements a practical version of Tang's theoretical framework to help you determine **before** you spend QPU hours whether your problem actually needs a quantum computer.
 
@@ -569,7 +572,7 @@ When you run MNIST through this tool, you'll see it's **dequantizable**. Despite
 2. PCA with 50 components captures >95% of variance
 3. Quantum kernel methods offer **no advantage** over classical kernels
 
-This doesn't mean quantum ML is useless—it means we need to find the *right* problems.
+This doesn't mean quantum ML is useless, it means we need to find the *right* problems.
 
 ---
 
@@ -626,7 +629,7 @@ with gr.Blocks(
     # ⚛️ DeepSuite: The Quantum Auditor
     ### Stop guessing. Start verifying.
     
-    Determine if your data requires quantum computing—or if classical methods suffice.
+    Determine if your data requires quantum computing, or if classical methods suffice.
     
     </div>
     """)
@@ -638,7 +641,7 @@ with gr.Blocks(
         with gr.Tab("🔬 Tang Test (Dequantization Audit)"):
             
             gr.Markdown("""
-            Upload your data or select a preset to run the **Tang Test**—a mathematical audit 
+            Upload your data or select a preset to run the **Tang Test**, a mathematical audit 
             that determines if quantum methods offer genuine advantage over classical approximations.
             """)
             
@@ -812,7 +815,7 @@ with gr.Blocks(
     ---
     <center>
     
-    **DeepSuite Quantum Auditor** | Built for the DeepSuite Research Proposal  
+    **DeepSuite Quantum Auditor** | Built by: Eric Raymond & Myalou | Purdue AI/Robotics Engineering  
     *"Don't guess. Verify."*
     
     </center>