Deploy MangoMAS Space via script

app.py

@@ -982,14 +982,14 @@ def plot_expert_weights(weights: dict[str, float]) -> go.Figure:
 # ═══════════════════════════════════════════════════════════════════════════
 
 AGENTS = [
-    {"name": "SWE Agent", "specialization": "Code scaffold generation", "icon": "💻"},
-    {"name": "Architect Agent", "specialization": "System design and patterns", "icon": "🏗️"},
-    {"name": "QA Agent", "specialization": "Test plan and case generation", "icon": "🧪"},
-    {"name": "Security Agent", "specialization": "Threat modeling (OWASP)", "icon": "🔒"},
-    {"name": "DevOps Agent", "specialization": "Infrastructure planning", "icon": "🚀"},
-    {"name": "Research Agent", "specialization": "Technical analysis", "icon": "📚"},
-    {"name": "Performance Agent", "specialization": "Optimization analysis", "icon": "⚡"},
-    {"name": "Documentation Agent", "specialization": "Technical writing", "icon": "📝"},
+    {"name": "SWE Agent", "specialization": "Code scaffold generation", "icon": "[SWE]"},
+    {"name": "Architect Agent", "specialization": "System design and patterns", "icon": "[ARCH]"},
+    {"name": "QA Agent", "specialization": "Test plan and case generation", "icon": "[QA]"},
+    {"name": "Security Agent", "specialization": "Threat modeling (OWASP)", "icon": "[SEC]"},
+    {"name": "DevOps Agent", "specialization": "Infrastructure planning", "icon": "[OPS]"},
+    {"name": "Research Agent", "specialization": "Technical analysis", "icon": "[RES]"},
+    {"name": "Performance Agent", "specialization": "Optimization analysis", "icon": "[PERF]"},
+    {"name": "Documentation Agent", "specialization": "Technical writing", "icon": "[DOC]"},
 ]
 
 
@@ -1109,7 +1109,7 @@ def build_app() -> gr.Blocks:
         # Header
         gr.HTML("""
         <div class="main-header">
-            <h1>🧠 MangoMAS</h1>
+            <h1>MangoMAS</h1>
             <p style="color: #a8a8a8; font-size: 1.1rem;">
                 Multi-Agent Cognitive Architecture — Interactive Demo
             </p>
@@ -1125,7 +1125,7 @@ def build_app() -> gr.Blocks:
                 gr.HTML(f'<div class="stat-box"><h3>{value}</h3><p>{label}</p></div>')
 
         # ── TAB 1: Cognitive Cells ─────────────────────────────────────────
-        with gr.Tab("🧠 Cognitive Cells", id="cells"):
+        with gr.Tab("Cognitive Cells", id="cells"):
             gr.Markdown("### Execute any of the 10 biologically-inspired cognitive cells")
 
             with gr.Row():
@@ -1153,10 +1153,10 @@ def build_app() -> gr.Blocks:
                 value="{}",
                 lines=1,
             )
-            cell_btn = gr.Button("⚡ Execute Cell", variant="primary")
+            cell_btn = gr.Button("Execute Cell", variant="primary")
             cell_output = gr.JSON(label="Cell Output")
 
-            gr.Markdown("---\n### 🔗 Cell Composition Pipeline")
+            gr.Markdown("---\n### Cell Composition Pipeline")
             pipeline_input = gr.Textbox(
                 label="Pipeline (comma-separated cell types)",
                 value="ethics, reasoning, aggregator",
@@ -1167,7 +1167,7 @@ def build_app() -> gr.Blocks:
                 value="Analyze the security implications of this API design: user@example.com",
                 lines=2,
             )
-            pipeline_btn = gr.Button("🔗 Run Pipeline", variant="secondary")
+            pipeline_btn = gr.Button("Run Pipeline", variant="secondary")
             pipeline_output = gr.JSON(label="Pipeline Result")
 
             # Wiring
@@ -1179,7 +1179,7 @@ def build_app() -> gr.Blocks:
             pipeline_btn.click(compose_cells, inputs=[pipeline_input, pipeline_text], outputs=pipeline_output)
 
         # ── TAB 2: MCTS Planning ──────────────────────────────────────────
-        with gr.Tab("🌲 MCTS Planning", id="mcts"):
+        with gr.Tab("MCTS Planning", id="mcts"):
             gr.Markdown("### Monte Carlo Tree Search with Policy/Value Neural Networks")
 
             with gr.Row():
@@ -1194,18 +1194,18 @@ def build_app() -> gr.Blocks:
                     mcts_c = gr.Slider(0.1, 3.0, value=1.414, step=0.1, label="Exploration Constant (C)")
                     mcts_strat = gr.Radio(["ucb1", "puct"], value="ucb1", label="Selection Strategy")
 
-            mcts_btn = gr.Button("🌲 Run MCTS", variant="primary")
+            mcts_btn = gr.Button("Run MCTS", variant="primary")
 
             with gr.Row():
                 mcts_tree_plot = gr.Plot(label="Search Tree Visualization")
                 mcts_json = gr.JSON(label="MCTS Result")
 
-            gr.Markdown("---\n### 📊 Strategy Benchmark")
+            gr.Markdown("---\n### Strategy Benchmark")
             bench_task = gr.Textbox(
                 label="Benchmark Task",
                 value="Optimize database query performance for high-throughput system",
             )
-            bench_btn = gr.Button("📊 Run Benchmark", variant="secondary")
+            bench_btn = gr.Button("Run Benchmark", variant="secondary")
             bench_output = gr.JSON(label="Benchmark Results (MCTS vs Greedy vs Random)")
 
             def run_and_plot(task, sims, c, strat):
@@ -1217,7 +1217,7 @@ def build_app() -> gr.Blocks:
             bench_btn.click(benchmark_strategies, inputs=bench_task, outputs=bench_output)
 
         # ── TAB 3: MoE Router ─────────────────────────────────────────────
-        with gr.Tab("🔀 MoE Router", id="moe"):
+        with gr.Tab("MoE Router", id="moe"):
             gr.Markdown("### Neural Mixture-of-Experts Routing Gate")
             gr.Markdown(
                 "The RouterNet MLP extracts 64-dimensional features from text, "
@@ -1233,7 +1233,7 @@ def build_app() -> gr.Blocks:
                 )
                 moe_topk = gr.Slider(1, 8, value=3, step=1, label="Top-K Experts", scale=1)
 
-            moe_btn = gr.Button("🔀 Route Task", variant="primary")
+            moe_btn = gr.Button("Route Task", variant="primary")
 
             with gr.Row():
                 moe_features_plot = gr.Plot(label="64-D Feature Vector")
@@ -1252,7 +1252,7 @@ def build_app() -> gr.Blocks:
             moe_btn.click(route_and_plot, inputs=[moe_task, moe_topk], outputs=[moe_features_plot, moe_weights_plot, moe_json])
 
         # ── TAB 4: Agent Orchestration ─────────────────────────────────────
-        with gr.Tab("🤖 Agents", id="agents"):
+        with gr.Tab("Agents", id="agents"):
             gr.Markdown("### Multi-Agent Orchestration with MoE Routing")
 
             with gr.Row():
@@ -1270,10 +1270,10 @@ def build_app() -> gr.Blocks:
                         label="Routing Strategy",
                     )
 
-            orch_btn = gr.Button("🤖 Orchestrate", variant="primary")
+            orch_btn = gr.Button("Orchestrate", variant="primary")
             orch_output = gr.JSON(label="Orchestration Result")
 
-            gr.Markdown("---\n### 👥 Available Agents")
+            gr.Markdown("---\n### Available Agents")
             agent_table = gr.Dataframe(
                 value=[[a["icon"], a["name"], a["specialization"]] for a in AGENTS],
                 headers=["", "Agent", "Specialization"],
@@ -1283,7 +1283,7 @@ def build_app() -> gr.Blocks:
             orch_btn.click(orchestrate, inputs=[orch_task, orch_agents, orch_strat], outputs=orch_output)
 
         # ── TAB 5: Architecture ────────────────────────────────────────────
-        with gr.Tab("📐 Architecture", id="arch"):
+        with gr.Tab("Architecture", id="arch"):
             gr.Markdown("""
 ### MangoMAS System Architecture
 
@@ -1340,10 +1340,10 @@ preprocess() → infer() → postprocess() → publish()
             """)
 
         # ── TAB 6: Metrics ─────────────────────────────────────────────────
-        with gr.Tab("📈 Metrics", id="metrics"):
+        with gr.Tab("Metrics", id="metrics"):
             gr.Markdown("### Live Performance Benchmarks")
 
-            metrics_btn = gr.Button("🔄 Run All Benchmarks", variant="primary")
+            metrics_btn = gr.Button("Run All Benchmarks", variant="primary")
 
             with gr.Row():
                 metrics_routing = gr.Plot(label="Routing Latency by Expert Count")