| """Run with 'streamlit run app.py'.""" |
| from __future__ import annotations |
| import base64 |
| import json |
| from pathlib import Path |
| import streamlit as st |
| from frontierlab.demo_data import QUESTION, QUERY |
| from frontierlab.quantitative import inverse_variance_heterogeneity |
| from frontierlab.openweight import OpenWeightClient |
| from frontierlab.pipeline import make_plan, run_research, continue_after_review |
| from frontierlab.planning_readiness import build_planning_readiness, readiness_summary |
| from frontierlab.review import review_packet, save_review |
| from frontierlab.reporting import export_json, summary |
| from frontierlab.process_metrics import process_metrics |
| from frontierlab.trajectory_graph import build_trajectory_graph, summarize as trajectory_summary, to_json as trajectory_json |
| from frontierlab.interventions import record_human_event |
| from frontierlab.claim_benchmark import benchmark |
| from frontierlab.claim_verification import audit_predictions, conflict_transparency |
| from frontierlab.integrity_trials import suite_report |
| from frontierlab.evaluation_ui import render_evaluation |
| from frontierlab.evaluation import require_evaluation |
| from frontierlab.evidence_graph import (approve_plan_step, refresh_evidence_graph, step_gate, |
| summarize as graph_summary, to_json as graph_json, to_svg as graph_svg) |
| from frontierlab.literature_pilot import load_dataset, reviewer_packet |
| from frontierlab.recorded_gate_ablation import run_recorded_pilot |
| from frontierlab.pilot_review import canonicalize_review_upload, labels_from_review_packet |
| from frontierlab.research_backend import NvidiaResearchClient, research_enabled, remaining_calls |
| from frontierlab.version import __version__ |
|
|
|
|
|
|
| ASSETS = Path(__file__).resolve().parent / "docs" / "assets" |
| _icon_path = ASSETS / "flatland-sphere-icon.png" |
| st.set_page_config(page_title="FrontierLab | Research Intelligence", |
| page_icon=str(_icon_path) if _icon_path.exists() else "◯", layout="wide") |
|
|
| |
| SERIF = "Georgia, 'Times New Roman', Times, serif" |
| st.markdown(f""" |
| <style> |
| :root {{ --fl-ink: #F2F2F2; --fl-soft: #CFCFCF; --fl-bg: #1E1E1E; --fl-bg2: #262626; --fl-line: rgba(242,242,242,0.55); }} |
| .stApp {{ background: var(--fl-bg); color: var(--fl-ink); }} |
| .stApp, .stApp p, .stApp li, .stApp label, .stApp input, .stApp textarea, .stApp button, |
| .stApp h1, .stApp h2, .stApp h3, .stApp h4, .stApp summary p, [data-testid="stMetricValue"], |
| [data-testid="stMetricLabel"], [data-testid="stCaptionContainer"] {{ font-family: {SERIF}; }} |
| [data-testid="stDecoration"] {{ display: none; }} |
| [data-testid="stHeader"] {{ background: transparent; }} |
| .block-container {{ padding-top: 2rem; max-width: 1320px; }} |
| h1 {{ font-weight: 400; font-style: italic; letter-spacing: 0.02em; }} |
| h2, h3 {{ font-weight: 400; letter-spacing: 0.04em; }} |
| [data-testid="stCaptionContainer"], .stCaption {{ color: var(--fl-soft); }} |
| hr {{ border-color: var(--fl-line) !important; }} |
| a {{ color: var(--fl-ink) !important; text-decoration: underline; text-underline-offset: 3px; }} |
| /* hero banner and section bands */ |
| .fl-hero {{ display: block; width: 100%; height: auto; border: 1px solid var(--fl-line); margin: 0 0 1.2rem 0; }} |
| .fl-band {{ display: flex; align-items: center; gap: 0.6rem; margin: 1.6rem 0 0.4rem 0; |
| padding-bottom: 0.35rem; border-bottom: 1px solid var(--fl-line); |
| font-variant: small-caps; letter-spacing: 0.22em; font-size: 0.85rem; color: var(--fl-ink); }} |
| .fl-band svg {{ flex: none; }} |
| /* outlined white boxes instead of filled cards */ |
| [data-testid="stMetric"] {{ background: transparent; padding: 14px 16px; border: 1px solid var(--fl-ink); border-radius: 0; }} |
| [data-testid="stSidebar"] {{ background: #1A1A1A; border-right: 1px solid var(--fl-line); }} |
| [data-testid="stExpander"] details {{ border: 1px solid var(--fl-line); border-radius: 0; background: transparent; }} |
| [data-testid="stForm"] {{ border: 1px solid var(--fl-line); border-radius: 0; }} |
| [data-testid="stAlert"] > div, [data-testid="stAlertContainer"] {{ background: transparent !important; |
| border: 1px solid var(--fl-line); border-radius: 0; color: var(--fl-ink); }} |
| [data-testid="stAlertContentWarning"], [data-testid="stAlertContentError"], |
| [data-testid="stAlertContentSuccess"], [data-testid="stAlertContentInfo"] {{ color: var(--fl-ink); }} |
| [data-testid="stAlert"]:has([data-testid="stAlertContentWarning"]) > div {{ border-style: dashed; border-color: var(--fl-ink); }} |
| [data-testid="stAlert"]:has([data-testid="stAlertContentError"]) > div {{ border: 2px solid var(--fl-ink); }} |
| [data-testid="stAlert"]:has([data-testid="stAlertContentSuccess"]) > div {{ border-left: 4px double var(--fl-ink); }} |
| [data-testid="stTextAreaRootElement"], [data-testid="stTextInputRootElement"], [data-testid="stNumberInputContainer"], |
| [data-testid="stSelectbox"] .react-aria-ComboBox > [role="group"], [data-testid="stMultiSelect"] .react-aria-ComboBox > [role="group"] {{ |
| background: transparent !important; border: 1px solid var(--fl-line) !important; border-radius: 0 !important; }} |
| [data-testid="stTextAreaRootElement"]:focus-within, [data-testid="stTextInputRootElement"]:focus-within, |
| [data-testid="stNumberInputContainer"]:focus-within {{ border-color: var(--fl-ink) !important; }} |
| [data-testid="stMultiSelectTagsContainer"] span[data-tag] {{ |
| background: transparent !important; color: var(--fl-ink) !important; border: 1px solid var(--fl-ink); border-radius: 0; }} |
| [data-testid="stMultiSelectTagsContainer"] span[data-tag] * {{ color: var(--fl-ink) !important; fill: var(--fl-ink); }} |
| /* checkboxes: outlined square, white tick on charcoal (never white-on-white) */ |
| [data-testid="stCheckbox"] label > div:first-of-type {{ background: transparent !important; border: 1px solid var(--fl-ink) !important; border-radius: 0 !important; }} |
| [data-testid="stCheckbox"] label > div:first-of-type svg polyline {{ stroke: var(--fl-ink) !important; }} |
| [data-testid="stCode"] pre, [data-testid="stJson"] {{ border: 1px solid var(--fl-line); border-radius: 0; }} |
| /* JSON viewer: monochrome (keys soft, values ink) */ |
| [data-testid="stJson"] span {{ color: var(--fl-ink) !important; }} |
| [data-testid="stJson"] .object-key, [data-testid="stJson"] .object-key span {{ color: var(--fl-soft) !important; }} |
| [data-testid="stJson"] .object-key-val {{ border-left-color: rgba(242,242,242,0.25) !important; }} |
| [data-testid="stJson"] svg {{ color: var(--fl-ink) !important; fill: var(--fl-ink) !important; }} |
| /* buttons: outlined white boxes that invert on hover */ |
| .stApp :is([data-testid="stBaseButton-primary"], [data-testid="stBaseButton-secondary"], [data-testid="stBaseButton-primaryFormSubmit"], [data-testid="stBaseButton-secondaryFormSubmit"]) {{ background: transparent; color: var(--fl-ink); border: 1px solid var(--fl-ink); |
| border-radius: 0; box-shadow: none; letter-spacing: 0.06em; }} |
| .stApp :is([data-testid="stBaseButton-primary"], [data-testid="stBaseButton-secondary"], [data-testid="stBaseButton-primaryFormSubmit"], [data-testid="stBaseButton-secondaryFormSubmit"]) p {{ color: inherit; }} |
| .stApp :is([data-testid="stBaseButton-primary"], [data-testid="stBaseButton-secondary"], [data-testid="stBaseButton-primaryFormSubmit"], [data-testid="stBaseButton-secondaryFormSubmit"]):hover:not(:disabled), .stApp :is([data-testid="stBaseButton-primary"], [data-testid="stBaseButton-secondary"], [data-testid="stBaseButton-primaryFormSubmit"], [data-testid="stBaseButton-secondaryFormSubmit"]):focus-visible {{ |
| background: var(--fl-ink); color: var(--fl-bg); border-color: var(--fl-ink); }} |
| .stApp :is([data-testid="stBaseButton-primary"], [data-testid="stBaseButton-secondary"], [data-testid="stBaseButton-primaryFormSubmit"], [data-testid="stBaseButton-secondaryFormSubmit"]):disabled {{ background: transparent; color: var(--fl-soft); border: 1px dashed var(--fl-line); opacity: 0.7; }} |
| </style> |
| """, unsafe_allow_html=True) |
|
|
| _ICON = 'width="22" height="22" viewBox="0 0 24 24" fill="none" stroke="#F2F2F2" stroke-linecap="round" stroke-linejoin="round" aria-hidden="true"' |
| BAND_ICONS = { |
| "point": f'<svg {_ICON}><line x1="2" y1="18" x2="22" y2="18" stroke-width="1"/><circle cx="12" cy="11" r="3" fill="#F2F2F2" stroke="none"/></svg>', |
| "sphere": f'<svg {_ICON}><circle cx="12" cy="12" r="10" stroke-width="1.5"/><ellipse cx="12" cy="12" rx="10" ry="3" stroke-width="0.8" stroke-dasharray="2 2"/><ellipse cx="12" cy="12" rx="3.6" ry="10" stroke-width="0.8"/></svg>', |
| "cube": f'<svg {_ICON}><rect x="3" y="8" width="13" height="13" stroke-width="1.4"/><polyline points="3,8 8,3 21,3 16,8" stroke-width="1.4"/><polyline points="21,3 21,16 16,21" stroke-width="1.4"/></svg>', |
| } |
|
|
|
|
| def band(label: str, icon: str) -> None: |
| """Small-caps section band, as on the README architecture plate.""" |
| st.markdown(f'<div class="fl-band">{BAND_ICONS[icon]}<span>{label}</span></div>', unsafe_allow_html=True) |
|
|
|
|
| _hero_path = ASSETS / "flatland-hero.svg" |
| if _hero_path.exists(): |
| _hero = base64.b64encode(_hero_path.read_bytes()).decode("ascii") |
| st.markdown(f'<img class="fl-hero" src="data:image/svg+xml;base64,{_hero}" ' |
| 'alt="FrontierLab: From Flatland to Spaceland. Role shapes rise from a flat plane toward a sphere.">', |
| unsafe_allow_html=True) |
|
|
| st.caption("META GLOBAL AI DEVELOPER HACKATHON 2026 / INDIE DEVELOPER / PUSH THE FRONTIER") |
| st.title("FrontierLab / Research Intelligence") |
| st.caption(f"Research → Evidence → Hypothesis → Evaluation → Planning · v{__version__}") |
| deployment_path = Path(__file__).resolve().parent / "deployment.json" |
| if deployment_path.exists(): |
| deployment = json.loads(deployment_path.read_text(encoding="utf-8")) |
| st.caption(f"Deployed source: {deployment['source_sha'][:12]} · {deployment['version']} · {deployment['space_id']}") |
| st.write("An evidence-grounded scientific research workflow that **records disagreement instead of hiding it**.") |
|
|
| nim_authorized = False |
| with st.sidebar: |
| st.header("Engine") |
| mode_label = st.radio("Evidence mode", ["Synthetic audit demonstration", "Live Europe PMC metadata (no model calls)", "Live Europe PMC + local Meta model", "Live Europe PMC + Meta via NVIDIA"], index=0) |
| mode = "demo" if mode_label.startswith("Synthetic") else "live" |
| use_local_meta = mode_label == "Live Europe PMC + local Meta model" |
| use_nim = mode_label == "Live Europe PMC + Meta via NVIDIA" |
| st.info("Paid Meta Platforms API is disabled. Choose a local model or the optional NVIDIA-hosted Meta backend.") |
| if mode == "demo": |
| st.warning("SYNTHETIC DATA. Invented publications demonstrate auditing; none are real scientific evidence.") |
| elif use_nim: |
| if not research_enabled(): |
| st.warning("NVIDIA research inference has not been enabled by the app owner.") |
| nim_authorized = st.checkbox( |
| "Allow this research question and retrieved abstracts to be sent to NVIDIA for Meta inference", |
| value=False, disabled=not research_enabled(), key="nim_research_consent") |
| st.caption(f"Remaining shared model calls in this server process: {remaining_calls()} / 8. Failed requests consume the allowance; no automatic retries.") |
| elif use_local_meta: |
| st.warning("Experimental real open-weight model mode. Requires an actual Muse Glimmer checkpoint served on THIS machine; charges no Meta API credits. Never expose the local server publicly.") |
| else: |
| st.info("Europe PMC metadata only. No model inference, no paid API requests.") |
| st.caption("No autonomous wet-lab execution, clinical advice or unverified study claims.") |
|
|
| band("I · Flatland — question and evidence", "point") |
| st.subheader("1 · Research question") |
| with st.form("research_form"): |
| question = st.text_area("Scientific research question", value=QUESTION, height=85) |
| query = st.text_input("Europe PMC search query", value=QUERY, disabled=(mode == "demo")) |
| n = st.slider("Maximum live abstracts", 1, 2 if use_nim else 10, |
| 2 if use_nim else 4, disabled=(mode == "demo")) |
| run_clicked = st.form_submit_button("Run research audit", use_container_width=True, |
| type="primary", disabled=use_nim and not nim_authorized) |
|
|
| if run_clicked: |
| with st.spinner("Retrieving evidence and applying verification gates..."): |
| try: |
| st.session_state.pop("frontier_result", None) |
| st.session_state.pop("frontier_model_client", None) |
| client = (NvidiaResearchClient(authorized=nim_authorized) if use_nim else |
| OpenWeightClient() if use_local_meta else None) |
| st.session_state["frontier_model_client"] = client |
| result = run_research(question, query, mode=mode, model_client=client, max_papers=n) |
| st.session_state["frontier_result"] = result |
| except Exception as exc: |
| st.error(f"Research run failed: {type(exc).__name__}: {exc}") |
|
|
| with st.expander("Published-literature pilot · 12 cases awaiting researcher annotation"): |
| pilot = load_dataset() |
| st.caption("Four published antibody-engineering papers; short abstract excerpts only. Draft reference labels are excluded from the review download. Researcher annotations remain pending.") |
| recorded_path = Path(__file__).resolve().parent / "docs/runs/meta_nim_pilot_20261028.json" |
| if recorded_path.exists(): |
| recorded = json.loads(recorded_path.read_text(encoding="utf-8"))["receipt"] |
| st.info("RECORDED REAL META RUN · 12 structured predictions, 8 matched quotations and 4 insufficient-evidence responses. This panel makes no live model call; accuracy awaits researcher review.") |
| st.download_button("Download recorded Meta predictions", data=json.dumps(recorded["predictions"], ensure_ascii=False, indent=2), file_name="meta_pilot_predictions_20261028.json", mime="application/json") |
| ablation = run_recorded_pilot() |
| with st.expander("Gate ablation · recorded 12-case Glimmer batch", expanded=False): |
| st.warning( |
| "POST-GENERATION REPLAY ONLY. The recorded model prompt already requested evidence quotes, " |
| "so this does not isolate the causal effect of quote-grounded prompting. " |
| "The 12-case human review is still pending; no accuracy is reported." |
| ) |
| g1, g2, g3, g4 = st.columns(4) |
| g1.metric("Raw decisive", ablation["arm_a_fixed_model_outputs"]["decisive"]) |
| g2.metric("Quote-gated decisive", ablation["arm_b_source_quote_post_gate"]["decisive"]) |
| g3.metric("New abstentions from gate", ablation["effects"]["quote_gate_new_abstentions"]) |
| g4.metric("Human reviews pending", ablation["arm_c_source_quote_plus_human_review"]["pending_review"]) |
| st.caption( |
| f"Recorded Meta call: {ablation['recorded_model_call']['usage']['total_tokens']} tokens · " |
| f"{ablation['recorded_model_call']['elapsed_seconds']:.3f}s · " |
| f"new model calls for this replay: {ablation['new_model_calls']}." |
| ) |
| st.caption( |
| "In this clean recorded batch, all eight decisive outputs already had valid anchored quotes, " |
| "so the post-processing source/quote gate introduced zero additional abstentions. " |
| "That is a result, not evidence that the gate is unnecessary." |
| ) |
| st.download_button( |
| "Download recorded gate-ablation JSON", |
| data=json.dumps(ablation, ensure_ascii=False, indent=2, sort_keys=True), |
| file_name="gate_ablation_recorded_20261028.json", |
| mime="application/json", |
| ) |
| st.download_button( |
| "Download literature reviewer packet", data=json.dumps(reviewer_packet(pilot), ensure_ascii=False, indent=2), |
| file_name="frontierlab_real_excerpt_review.json", mime="application/json") |
| reviewer_name = st.text_input( |
| "Reviewer name for an uploaded 12-case packet", |
| key="pilot_review_reviewer", |
| help="Used for provenance only; FrontierLab does not verify identity." |
| ) |
| uploaded_review = st.file_uploader( |
| "Upload edited 12-case reviewer packet (JSON)", |
| type=["json"], |
| key="pilot_review_upload", |
| help="Keep the source/case fields unchanged; edit only label and rationale fields." |
| ) |
| if uploaded_review is not None: |
| try: |
| review_upload = json.loads(uploaded_review.getvalue().decode("utf-8")) |
| canonical_review = canonicalize_review_upload( |
| pilot, review_upload, reviewer=reviewer_name |
| ) |
| review_stats = canonical_review["review_stats"] |
| review_labels = labels_from_review_packet(pilot, canonical_review) |
| reviewed_ablation = run_recorded_pilot(review_labels=review_labels) |
| arm_c = reviewed_ablation["arm_c_source_quote_plus_human_review"] |
| if canonical_review["status"] == "COMPLETE_RESEARCHER_REVIEW": |
| st.success( |
| "12-case researcher review packet is complete and source-bound. " |
| "It remains researcher-supplied review, not independent gold." |
| ) |
| else: |
| st.info( |
| f"Partial researcher review: {review_stats['reviewed']} / " |
| f"{review_stats['total']} cases completed." |
| ) |
| rv1, rv2, rv3 = st.columns(3) |
| rv1.metric("Cases reviewed", review_stats["reviewed"]) |
| rv2.metric("Arm C reviews completed", arm_c["reviews_completed"]) |
| rv3.metric( |
| "Reviewer changes to surviving decisions", |
| arm_c["review_corrections_to_surviving_decisions"], |
| ) |
| st.caption( |
| f"Arm C pending reviews: {arm_c['pending_review']}. " |
| "Accuracy remains unavailable because independence_verified=false." |
| ) |
| st.download_button( |
| "Download canonical source-bound review JSON", |
| data=json.dumps(canonical_review, ensure_ascii=False, indent=2, sort_keys=True), |
| file_name="frontierlab_real_excerpt_review_completed.json", |
| mime="application/json", |
| ) |
| except (ValueError, UnicodeDecodeError, json.JSONDecodeError) as exc: |
| st.error(f"Reviewer packet rejected: {exc}") |
| st.markdown("[Pilot protocol and source attribution](https://github.com/Jaycee871/FrontierLab-Meta-Hackathon-2026/blob/main/docs/REAL_LITERATURE_PILOT.md)") |
| review_path = Path(__file__).resolve().parent / "docs/runs/nim_claim_review_completed_20261028.json" |
| if review_path.exists(): |
| reviewed = json.loads(review_path.read_text(encoding="utf-8")) |
| st.success("RECORDED CORE RUN · Jaycee871 reviewed both claims as SUPPORTED on October 28, 2026. These judgments cover two claims, separate from the 12-case pilot.") |
| st.caption("The original extraction receipt is preserved. No hypothesis, method selection or plan was generated by recording this review; this panel makes no model call.") |
| st.dataframe([{"Case": c["case_id"], "Claim": c["claim"], "Human judgment": c["review_label"], "Reason": c["review_note"]} for c in reviewed["cases"]], hide_index=True, use_container_width=True) |
| st.download_button("Download completed core-workflow human review", data=review_path.read_text(encoding="utf-8"), file_name=review_path.name, mime="application/json") |
|
|
| result = st.session_state.get("frontier_result") |
| if result: |
| active_client = st.session_state.get("frontier_model_client") |
| if result.backend == "nvidia-nim" and (not use_nim or not nim_authorized): |
| active_client = None |
| stats = summary(result) |
| a, b, c, d = st.columns(4) |
| a.metric("Sources", stats["sources"]) |
| b.metric("Claims: passage checks / synthetic", stats["claims"]) |
| c.metric("Conflicts", stats["conflicts"]) |
| d.metric("Rejected claims", stats["rejected"]) |
| st.caption(f"Decision gate: **{stats['decision']}** · Backend: {result.backend} · Model: {result.model} · Data source: {result.mode}") |
| if result.inference: |
| with st.expander("Model request receipts"): |
| st.json(result.inference) |
| with st.expander("Process metrics · audit only", expanded=False): |
| pm = process_metrics(result) |
| m1, m2, m3, m4 = st.columns(4) |
| m1.metric("Completed stages", f"{pm['stage_completion']['completed']} / 5") |
| m2.metric("SCR", f"{pm['scr'] * 100:.0f}%") |
| m3.metric("Observed human actions", pm["human_interventions"]["observed_total"]) |
| aerr = pm["nim"]["aerr"] |
| m4.metric("AERR", "N/A" if aerr is None else f"{aerr * 100:.0f}%") |
| st.caption( |
| "Process metrics describe workflow activity, not model accuracy. " |
| "Observed human actions use recorded intervention events when available; legacy runs use a conservative fallback. Identity is not verified." |
| ) |
| taxonomy = pm["error_taxonomy"] |
| e1, e2, e3 = st.columns(3) |
| e1.metric("Governance / evidence blocks", taxonomy["governance_or_evidence_blocks"]) |
| e2.metric("Runtime failures", taxonomy["runtime_failures"]) |
| e3.metric("Scientific tensions", taxonomy["scientific_tensions"]) |
| if pm["nim"]["applicable"]: |
| st.caption( |
| f"NIM calls: {pm['nim']['attempted_calls']} · " |
| f"tokens: {pm['nim']['total_tokens']} · " |
| f"recorded model time: {pm['nim']['elapsed_seconds']:.3f}s" |
| ) |
| for warning in result.audit.warnings: |
| st.warning(warning) |
| if result.mode == "demo": |
| with st.expander("Wolfram-validated heterogeneity example (FICTIONAL numbers)", expanded=False): |
| stats_q = inverse_variance_heterogeneity([0.4, -0.3], [0.15, 0.1]) |
| st.error("SYNTHETIC: These invented effect sizes do NOT represent observed studies.") |
| x, y, z = st.columns(3) |
| x.metric("Pooled effect (not a conclusion)", f"{stats_q['pooled_effect']:.4f}") |
| y.metric("Cochran Q", f"{stats_q['Q']:.4f}") |
| z.metric("I²", f"{stats_q['I2'] * 100:.1f}%") |
| st.caption("High inconsistency: do NOT force consensus. Independently checked with Wolfram Language; no online Wolfram call is made by this app.") |
| if result.mode == "demo": |
| with st.expander("Claim-to-passage audit benchmark (FICTIONAL · hand-labeled)", expanded=False): |
| fixture_papers, fixture_cases = benchmark() |
| verified = audit_predictions(fixture_papers, fixture_cases) |
| st.warning("All ten examples and predictions are HAND-AUTHORED FICTION. Scores do not represent Meta/NVIDIA model accuracy, nor prove scientific validity.") |
| x, y, z = st.columns(3) |
| x.metric("Known source IDs", f"{verified['source_id_coverage']:.0%}") |
| y.metric("Verbatim excerpt matches", f"{verified['verbatim_passage_coverage']:.0%}") |
| z.metric("Synthetic gold-label agreement", f"{verified['label_accuracy_vs_independent_gold']:.0%}") |
| st.caption("A quotation can exist yet be used to support a FALSE statement; genuine semantic verification needs independently annotated evidence.") |
| st.json({"cases": verified["cases"], "missing_conflict_detection_example": |
| conflict_transparency([], [("SYNTHETIC:A","SYNTHETIC:B")])}) |
| if result.mode == "demo": |
| with st.expander("Research Failure Lab · 12 fictional failure injections", expanded=False): |
| lab = suite_report() |
| st.warning( |
| "OFFLINE HAND-AUTHORED POLICY TESTS ONLY: no actual Meta inference, " |
| "real biomedical model call, independent scientific assessment or " |
| "human learning experiment has occurred." |
| ) |
| u, v, w = st.columns(3) |
| u.metric("Policy test cases", lab["total"]) |
| v.metric("Expected guard decisions matched", lab["matched_expectations"]) |
| w.metric("Real model calls", lab["model_calls"]) |
| st.caption( |
| "Cross-stage scenarios: stale source snapshots, unsupported excerpts, " |
| "unverified BioNeMo origin, train/test peptide leakage, and human teachability evidence. " |
| "A quote that exists can still be semantically wrong." |
| ) |
| st.json(lab["cases"]) |
| st.download_button( |
| "Download fictional failure-injection results", |
| data=json.dumps(lab, indent=2, ensure_ascii=False), |
| file_name="frontierlab_v011_failure_lab_fictional.json", |
| mime="application/json", |
| ) |
| with st.expander("2 · Evidence catalogue", expanded=True): |
| for p in result.papers: |
| st.markdown(f"**{p.title}** · {p.paper_id} · {p.year}") |
| st.caption(f"{p.authors} · {p.source} · {'SYNTHETIC / NO LINK' if p.synthetic else p.url}") |
| st.write(p.abstract) |
| st.divider() |
| with st.expander("Evidence claims and citation checks", expanded=True): |
| if not result.audit.verified_claims: |
| st.info("No claims have passed the source-ID gate. This is not evidence that a scientific claim is false.") |
| for claim in result.audit.verified_claims: |
| st.markdown(f"**{claim.stance.upper()}** · {claim.paper_id} · {claim.claim_key}") |
| st.write(claim.statement) |
| if claim.passage_matched: |
| st.caption(f"Verbatim abstract excerpt (NOT semantic verification): {claim.evidence_quote}") |
| else: |
| st.caption("No passage match recorded (synthetic fixture only).") |
| st.caption(f"Limitations: {claim.limitation}") |
| st.caption("Valid source IDs and verbatim excerpts are NOT independent scientific fact-checking or semantic entailment. Compare claims against the original article and methods.") |
| if result.audit.rejected_claims: |
| st.json(result.audit.rejected_claims) |
| with st.expander("Evidence conflicts and skeptical review", expanded=True): |
| if result.audit.conflicts: |
| st.error("Incompatible directions detected. Do not report consensus.") |
| st.json(result.audit.conflicts) |
| else: |
| st.info("No machine-detected contradictions; this is not proof of agreement.") |
| st.json(result.skeptic) |
| if result.mode == "live" and result.audit.verified_claims: |
| band("II · The Sphere — human claim review", "sphere") |
| with st.expander("Researcher claim review · required before synthesis", expanded=True): |
| st.warning( |
| "Verbatim excerpt matching does NOT establish entailment. " |
| "Choose a human judgment for each claim. PENDING items and fewer than two " |
| "supported distinct sources block research hypothesis generation." |
| ) |
| packet=review_packet(result) |
| previous={c["case_id"]:c for c in (result.review or {}).get("cases", [])} |
| st.download_button( |
| "Download source-bound blank reviewer packet", |
| data=json.dumps(packet, ensure_ascii=False, indent=2), |
| file_name="frontierlab_human_review_blank.json", mime="application/json" |
| ) |
| decisions=[] |
| with st.form("claim_reviewer_form"): |
| for case in packet["cases"]: |
| cid=case["case_id"] |
| earlier=previous.get(cid, {}) |
| st.markdown(f"**{cid} · {case['paper_title']}**") |
| st.caption(f"Source ID: {case['paper_id']} · {case['source']}") |
| st.write("**Model claim:** " + case["claim"]) |
| st.code(case["evidence_quote"], language="text") |
| st.caption("The quotation exists in the source abstract. Its meaning has NOT been verified.") |
| opts=["PENDING","SUPPORTED","CONTRADICTED","INSUFFICIENT"] |
| default=earlier.get("review_label","PENDING") |
| verdict=st.selectbox( |
| "Reviewer judgment", opts, |
| index=opts.index(default) if default in opts else 0, |
| key=f"claim_review_{cid}" |
| ) |
| note=st.text_input( |
| "Reason for verdict (required if contradicted or insufficient)", |
| value=earlier.get("review_note",""), key=f"claim_reason_{cid}" |
| ) |
| decisions.append({"case_id":cid,"review_label":verdict,"review_note":note}) |
| st.divider() |
| submitted=st.form_submit_button("Save human review judgments", type="primary") |
| if submitted: |
| try: |
| save_review(result, decisions) |
| st.session_state["frontier_result"]=result |
| st.success("Human review saved in this session; download JSON to preserve it.") |
| except ValueError as exc: |
| st.error(f"Review validation failed: {exc}") |
| if result.review: |
| st.json({"review_status":result.review.get("status"), |
| "review_statistics":result.review.get("review_stats"), |
| "independent_expert_adjudication_verified":False}) |
| st.download_button( |
| "Download human-annotated review JSON", |
| data=json.dumps(result.review,ensure_ascii=False,indent=2), |
| file_name="frontierlab_human_review_annotated.json", |
| mime="application/json" |
| ) |
| if result.review.get("status")=="READY_FOR_HUMAN_GATED_SYNTHESIS": |
| st.info("Review complete. Hypothesis synthesis requires an actually connected, available model and further human scrutiny.") |
| if st.button("Resume model synthesis after human review", type="secondary", |
| disabled=active_client is None): |
| try: |
| continue_after_review(result, model_client=active_client) |
| st.session_state["frontier_result"]=result |
| st.success("Provisional human-gated draft created; scientific validation is still needed.") |
| except (ValueError, PermissionError, RuntimeError) as exc: |
| st.error(f"Synthesis remains blocked: {type(exc).__name__}: {exc}") |
| band("III · Spaceland — hypothesis, evaluation and plan", "cube") |
| with st.expander("3 · Falsifiable hypotheses", expanded=True): |
| st.json(result.hypotheses) |
| render_evaluation(result, model_client=active_client) |
| st.subheader("5 · Research plan approval gate") |
| try: |
| require_evaluation(result) |
| evaluation_ready = True |
| except (ValueError, PermissionError): |
| evaluation_ready = False |
|
|
| if evaluation_ready and result.plan is None: |
| try: |
| readiness = build_planning_readiness(result) |
| ready_stats = readiness_summary(readiness) |
| with st.expander("Planning readiness · pre-approval contract", expanded=False): |
| st.warning( |
| "READY ≠ APPROVED. This packet validates the current reviewed context and " |
| "selected method, but it does not authorize or call the Planning Agent." |
| ) |
| p1, p2, p3, p4 = st.columns(4) |
| p1.metric("Selected method", ready_stats["selected_candidate_id"]) |
| p2.metric("Open reproducibility checks", len(ready_stats["open_reproducibility_checks"])) |
| p3.metric("Required planning obligations", ready_stats["required_obligations"]) |
| p4.metric("Planning approved", "NO") |
| st.caption( |
| "Open checks: " + ", ".join(ready_stats["open_reproducibility_checks"]) + |
| ". The future plan must preserve evidence/citation gates and address all obligations." |
| ) |
| st.dataframe( |
| [{ |
| "Obligation": item["obligation_id"], |
| "Category": item["category"], |
| "Status": item["status"], |
| "Requirement": item["requirement"], |
| } for item in readiness["planning_obligations"]], |
| hide_index=True, |
| use_container_width=True, |
| ) |
| st.download_button( |
| "Download planning-readiness JSON", |
| data=json.dumps(readiness, ensure_ascii=False, indent=2, sort_keys=True), |
| file_name="planning_readiness.json", |
| mime="application/json", |
| ) |
| except (ValueError, PermissionError) as exc: |
| st.info(f"Planning readiness not available: {exc}") |
| approved = st.checkbox("I have reviewed the evidence, conflicts and limitations. Allow a DRAFT research plan.") |
| if st.button("Generate cited plan", disabled=not approved or not result.hypotheses or not evaluation_ready or (result.mode == "live" and active_client is None), type="primary"): |
| try: |
| planner = active_client if result.mode == "live" else None |
| make_plan(result, model_client=planner, approved=approved) |
| st.success("Draft created. Human scientific review is still required.") |
| except Exception as exc: |
| st.error(f"Planning blocked: {exc}") |
| if result.plan: |
| st.json(result.plan) |
| |
| graph = refresh_evidence_graph(result) |
| g_stats = graph_summary(graph) |
| with st.expander("6 · Evidence graph · provenance of every plan step", expanded=False): |
| st.caption("EXTRACTED: passage located in the cited abstract and source IDs checked (not semantic entailment). " |
| "INFERRED: model stance without an anchored quote. AMBIGUOUS: conflicting labels. " |
| "Only all-EXTRACTED paths to a paper count as evidence.") |
| e_count = g_stats["edges_by_provenance"] |
| x, y, z = st.columns(3) |
| x.metric("EXTRACTED edges", e_count["EXTRACTED"]) |
| y.metric("INFERRED edges (not evidence)", e_count["INFERRED"]) |
| z.metric("AMBIGUOUS edges (not evidence)", e_count["AMBIGUOUS"]) |
| drawing = graph_svg(graph) |
| if drawing: |
| st.markdown('<img class="fl-hero" alt="Evidence provenance graph: solid lines EXTRACTED, dashed INFERRED, dotted AMBIGUOUS" ' |
| f'src="data:image/svg+xml;base64,{base64.b64encode(drawing.encode("utf-8")).decode("ascii")}">', |
| unsafe_allow_html=True) |
| else: |
| st.caption("Graph too large to draw legibly; use the tables and graph.json below.") |
| if g_stats["surfaced_for_review"]: |
| st.warning("Surfaced for researcher review. These links are NOT counted as evidence.") |
| st.dataframe([{"Provenance": e["provenance"], "From": e["source"], "Relation": e["relation"], |
| "To": e["target"], "Why": e["reason"]} for e in g_stats["surfaced_for_review"]], |
| hide_index=True, use_container_width=True) |
| steps = [n for n in graph["nodes"] if n["type"] == "plan_step"] |
| if steps: |
| gates = {n["id"]: step_gate(graph, n["id"]) for n in steps} |
| st.dataframe([{"Step": n["index"], "Text": n["text"], |
| "Path gate": "ALL EXTRACTED" if gates[n["id"]]["approvable"] else "BLOCKED", |
| "Researcher approval": "RECORDED" if n["id"] in graph["approvals"] else "—", |
| "Reasons": "; ".join(gates[n["id"]]["reasons"])} for n in steps], |
| hide_index=True, use_container_width=True) |
| eligible = [n["id"] for n in steps if gates[n["id"]]["approvable"] and n["id"] not in graph["approvals"]] |
| if eligible: |
| with st.form("plan_step_approval_form"): |
| chosen = st.selectbox("Plan step with an all-EXTRACTED evidence path", eligible, |
| format_func=lambda sid: next(f"Step {n['index']}" for n in steps if n["id"] == sid)) |
| reviewer = st.text_input("Reviewer") |
| rationale = st.text_input("Approval rationale (8+ characters)") |
| if st.form_submit_button("Record researcher approval of this step", type="secondary"): |
| try: |
| approve_plan_step(graph, chosen, reviewer=reviewer, note=rationale) |
| record_human_event(result, "PLAN_STEP_APPROVED", chosen, actor_label=reviewer, note=rationale) |
| st.session_state["frontier_result"] = result |
| st.success("Step approval recorded in this session; it remains a draft, not a validated protocol.") |
| except (ValueError, PermissionError) as exc: |
| st.error(f"Step approval blocked: {exc}") |
| else: |
| st.info("No plan step is currently eligible for approval under the EXTRACTED-only path rule.") |
| else: |
| st.caption("No plan steps yet. Steps appear here once a draft plan exists.") |
| st.download_button("Download graph.json", data=graph_json(graph), |
| file_name="graph.json", mime="application/json") |
| trajectory = build_trajectory_graph(result) |
| t_stats = trajectory_summary(trajectory) |
| with st.expander("7 · Trajectory audit graph · process trace", expanded=False): |
| st.caption( |
| "Audit-only process graph. It records model calls, human interventions, " |
| "method evaluation, rejected claims and process signals. It never participates " |
| "in the scientific evidence gate." |
| ) |
| t1, t2, t3, t4 = st.columns(4) |
| t1.metric("Model calls", t_stats["model_calls"]) |
| t2.metric("Human events", t_stats["human_events"]) |
| t3.metric("Rejected claims", t_stats["rejected_claims"]) |
| t4.metric("Process signals", t_stats["process_signals"]) |
| st.dataframe( |
| [{"Node type": key, "Count": value} for key, value in t_stats["nodes_by_type"].items()], |
| hide_index=True, use_container_width=True |
| ) |
| st.download_button( |
| "Download trajectory.json", |
| data=trajectory_json(trajectory), |
| file_name="trajectory.json", |
| mime="application/json", |
| ) |
| with st.expander("Audit trail", expanded=False): |
| st.json(result.log) |
| st.download_button("Download complete provenance JSON", data=export_json(result), |
| file_name="frontierlab_audit.json", mime="application/json") |
| else: |
| st.info("Begin with the synthetic audit or Europe PMC. Model-backed research requires a configured local server or an explicitly enabled NVIDIA backend.") |
|
|
|
|
