"""FATHOM Streamlit Demo — DEM-03. 3-panel layout: Left: Recursion tree visualization (D3 or plotly sunburst) Middle: Pareto frontier (accuracy vs token-cost, α sweep) Right: W&B training curves embed (live iframe) Run: streamlit run viz/app.py """ from __future__ import annotations import json import os from pathlib import Path import streamlit as st st.set_page_config( page_title="FATHOM — RL-Trained Recursive Language Model", page_icon="🧠", layout="wide", ) # --------------------------------------------------------------------------- # Header # --------------------------------------------------------------------------- st.markdown("## 🧠 FATHOM Demo") st.markdown( "_First RL-Trained Recursive Language Model — Meta × PyTorch × HF Hackathon_" ) st.divider() # --------------------------------------------------------------------------- # Reward composition badge (DEM-03 C.4: visible-to-judges reward overview) # --------------------------------------------------------------------------- with st.expander("Reward composition (4 components, deterministic verifier)", expanded=True): cb1, cb2 = st.columns([1, 2], gap="medium") with cb1: st.markdown("**Format gate** (multiplier)") st.success("`...` required \u2014 if missing, reward = 0") st.caption("Source: `rewards/format_gate.py` \u2014 audited against attack #2 in REWARD_AUDIT.md") with cb2: try: import plotly.graph_objects as go # type: ignore labels = ["correctness", "token_budget", "recursion_efficiency"] weights = [0.75, 0.20, 0.05] colors = ["#2ca02c", "#1f77b4", "#ff7f0e"] fig0 = go.Figure(go.Pie( labels=labels, values=weights, marker=dict(colors=colors), hole=0.4, textinfo="label+percent", )) fig0.update_layout(margin=dict(l=10, r=10, t=10, b=10), height=200, showlegend=False) st.plotly_chart(fig0, use_container_width=True) except ImportError: st.metric("correctness", 0.75) st.metric("token_budget", 0.20) st.metric("recursion_efficiency", 0.05) st.divider() # --------------------------------------------------------------------------- # Sidebar: controls # --------------------------------------------------------------------------- with st.sidebar: st.header("Controls") alpha_val = st.slider("Token-budget α", 0.05, 1.0, 0.20, 0.05, key="alpha") max_depth = st.slider("Max recursion depth shown", 1, 3, 2, key="max_depth") env_url = st.text_input( "Env server URL", value=os.environ.get("FATHOM_SPACE_URL", "http://localhost:8001"), key="env_url", ) st.divider() st.caption("Source: [github.com/fathom](https://github.com)") # --------------------------------------------------------------------------- # 3 columns # --------------------------------------------------------------------------- col_tree, col_pareto, col_wb = st.columns([1.2, 1.2, 1], gap="medium") # ── Column 1: Recursion tree ──────────────────────────────────────────────── with col_tree: st.subheader("Recursion Tree") st.caption("One episode from the trained model") # Placeholder D3 tree — replaced with live env call post-training sample_tree = { "name": "root: 200K doc", "children": [ { "name": "llm(chunk_0-50K)", "children": [{"name": "REPL: grep → 'azure'"}], }, {"name": "REPL: count_tokens → 200K"}, {"name": "→ azure"}, ], } tree_json = json.dumps(sample_tree) d3_html = f"""

""" st.components.v1.html(d3_html, height=260) # ── Column 2: Pareto frontier ──────────────────────────────────────────────── with col_pareto: st.subheader("Pareto Frontier") st.caption(f"Accuracy vs token cost (α = {alpha_val:.2f})") try: import plotly.graph_objects as go # type: ignore # Placeholder data — replaced with logged eval results post-training alpha_values = [0.05, 0.10, 0.20, 0.50, 1.00] accuracy = [0.62, 0.61, 0.58, 0.52, 0.44] token_cost = [1.00, 0.95, 0.85, 0.65, 0.48] fig = go.Figure() fig.add_trace(go.Scatter( x=token_cost, y=accuracy, mode="lines+markers", marker=dict(size=10, color="#6366f1"), line=dict(color="#a5b4fc", width=2), text=[f"α={a}" for a in alpha_values], textposition="top center", )) # Highlight current alpha idx = min(range(len(alpha_values)), key=lambda i: abs(alpha_values[i] - alpha_val)) fig.add_trace(go.Scatter( x=[token_cost[idx]], y=[accuracy[idx]], mode="markers", marker=dict(size=16, color="#ef4444", symbol="star"), name=f"Current α={alpha_val:.2f}", )) fig.update_layout( xaxis_title="Normalized Token Cost", yaxis_title="Accuracy", margin=dict(l=20, r=10, t=20, b=40), height=240, showlegend=False, ) st.plotly_chart(fig, use_container_width=True) except ImportError: st.info("plotly not installed — run `pip install plotly`") # ── Column 3: W&B training curves ──────────────────────────────────────────── with col_wb: st.subheader("Training Curves") wb_url = os.environ.get("WANDB_RUN_URL", "") if wb_url: st.components.v1.iframe(wb_url, height=240, scrolling=True) else: st.info("Set `WANDB_RUN_URL` env var to embed live training curves.") st.caption("W&B logged metrics: composite, format_pass, correctness, token_budget, recursion_eff") # Placeholder sparkline try: import plotly.graph_objects as go # type: ignore steps = list(range(0, 401, 50)) fake_reward = [0.10, 0.18, 0.28, 0.38, 0.45, 0.52, 0.57, 0.60, 0.62] fig2 = go.Figure(go.Scatter(x=steps, y=fake_reward, mode="lines+markers", line=dict(color="#6366f1", width=2))) fig2.update_layout( xaxis_title="GRPO step", yaxis_title="Composite reward", margin=dict(l=20, r=10, t=10, b=40), height=240, ) st.plotly_chart(fig2, use_container_width=True) except ImportError: pass st.divider() st.caption("FATHOM Phase 1 skeleton — full curves appear after GRPO training completes.")