"feat: design interactive streamlit presentation layer for dashboard studio"

app.py

@@ -0,0 +1,106 @@
+import streamlit as st
+import os
+# Import your verified engine classes directly from your production code
+from twin import FakeLLMScaler
+
+# --- Page Custom Configuration ---
+st.set_page_config(
+    page_title="llm-twin | Infrastructure & FinOps Studio",
+    page_icon="🚦",
+    layout="wide"
+)
+
+st.title("🚦 llm-twin: Distributed Architecture & FinOps Studio")
+st.markdown("""
+This interactive simulator evaluates LLM distributed inference strategies, communications efficiency bottlenecks, 
+and multi-cloud compute unit economics across specialized and legacy hyperscaler networks.
+""")
+
+st.sidebar.header("🛠️ Model & Inference Configuration")
+
+# --- Interactive Sidebar Controls ---
+model_name = st.sidebar.text_input("Model Identity Name", "llama-3-70b")
+param_size = st.sidebar.slider("Model Parameter Scale (Billion)", 1, 405, 70, step=1)
+
+st.sidebar.markdown("---")
+st.sidebar.header("💻 Hardware Cluster Topology")
+
+gpu_type = st.sidebar.selectbox("Accelerator Variant", ["H100", "A100", "H200"])
+gpu_mem = st.sidebar.selectbox("VRAM Capacity Allocation", [40, 80, 141], index=1)
+
+# Match real market baseline hardware performance specifications
+gpu_tflops = 989 if gpu_type == "H100" else (156 if gpu_mem == 40 else 312)
+if gpu_type == "H200": gpu_tflops = 1979
+
+gpu_count = st.sidebar.slider("Total Cluster Accelerator Count", 1, 64, 8, step=1)
+
+st.sidebar.markdown("---")
+st.sidebar.header("📈 Inference Workload Profile")
+batch_size = st.sidebar.slider("Active Concurrent Batch Size", 1, 256, 32, step=1)
+seq_len = st.sidebar.slider("Sequence Tail Length Context", 512, 8192, 2048, step=512)
+
+st.sidebar.markdown("---")
+st.sidebar.header("💰 Cloud Broker Tiers")
+provider_type = st.sidebar.radio("Compute Network Provider", ["specialized", "hyperscaler"])
+billing_model = st.sidebar.radio("Contract Procurement Type", ["on-demand", "reserved"])
+
+# --- Interface Core State Mapping ---
+mock_config = {
+    "model": {"name": model_name, "parameters_billion": param_size},
+    "hardware": {
+        "gpu_type": gpu_type,
+        "gpu_count": gpu_count,
+        "gpu_memory_gb": gpu_mem,
+        "gpu_tflops": gpu_tflops
+    },
+    "inference": {"batch_size": batch_size, "sequence_length": seq_len},
+    "economics": {"provider_type": provider_type, "billing_model": billing_model}
+}
+
+# Run the backend analytical scaler live on every input mutation
+scaler = FakeLLMScaler(mock_config)
+metrics = scaler.simulate()
+
+# --- Main Dashboard Metrics Panels Layout ---
+col1, col2, col3, col4 = st.columns(4)
+
+with col1:
+    if metrics["fits"]:
+        st.success("🚨 Memory Status: STABLE")
+    else:
+        st.error("🚨 Memory Status: CRITICAL OOM")
+
+with col2:
+    st.metric("Total Throughput", f"{metrics['throughput_tok_sec']:,.2f} tok/s")
+
+with col3:
+    st.metric("Hourly Run Rate", f"${metrics['hourly_cost']:.2f} / hr")
+
+with col4:
+    st.metric("Cost / Million Tokens", f"${metrics['cost_per_m_tokens']:.4f}")
+
+st.markdown("---")
+
+# --- Deep Architectural Breakdown Sections ---
+left_panel, right_panel = st.columns(2)
+
+with left_panel:
+    st.subheader("📊 Distributed Cluster Strategies")
+
+    st.info(f"**Auto-Selected Parallel Split Strategy:** TP={metrics['tp']} | PP={metrics['pp']} | DP={metrics['dp']}")
+
+    st.write(f"**Interconnect Bus Scaling Efficiency:** {metrics['comm_efficiency_pct']:.2f}%")
+    st.progress(int(metrics['comm_efficiency_pct']))
+    st.caption("Lower scaling efficiency values capture cross-node network segmentation hops and pipeline bubble stalls.")
+
+with right_panel:
+    st.subheader("💾 VRAM Allocation Breakdown")
+
+    util_percentage = min(100.0, metrics['mem_util_pct'])
+    st.write(f"**Aggregate Memory Threshold:** {metrics['required_mem_gb']:.2f} GB / {metrics['total_mem_gb']} GB")
+    st.progress(int(util_percentage))
+    st.write(f"**Active Workspace Utilization Ratio:** {metrics['mem_util_pct']:.2f}%")
+
+st.markdown("---")
+st.subheader("📁 Generated Infrastructure Blueprint Configuration")
+st.json(mock_config)