| """Streamlit method review form; does not run scientific tools or remote inference.""" |
| from __future__ import annotations |
| import json |
|
|
| from .evaluation import (CHECKS, CHECK_STATES, CRITERIA, allowed_sources, |
| blank_candidate, context_id, demo_candidates, |
| save_evaluation, suggest_methods, require_evaluation) |
|
|
| CHECK_LABELS = { |
| "data": "Data / inputs", "code": "Code / model version", |
| "environment": "Environment / dependencies", "parameters": "Parameters / seeds", |
| "protocol": "Protocol / controls", "metrics": "Evaluation metrics", |
| } |
| CRITERION_LABELS = { |
| "expected_value": "Expected research value and rationale", |
| "resources": "Required resources / feasibility", |
| "validation": "Validation needed before interpreting results", |
| "limitations": "Limitations and uncertainty", |
| "novelty": "Novelty / diversity evidence (UNKNOWN if unassessed)", |
| } |
|
|
|
|
| def render_evaluation(run, *, model_client=None): |
| import streamlit as st |
| st.subheader("4 · Method evaluation") |
| if not run.hypotheses: |
| st.info("Generate hypotheses after evidence review before comparing methods.") |
| return |
| try: |
| sources = allowed_sources(run) |
| ctx = context_id(run) |
| except (ValueError, PermissionError) as exc: |
| st.warning(str(exc)) |
| return |
| state_key = "method_drafts_" + ctx |
| if state_key not in st.session_state: |
| st.session_state[state_key] = ( |
| run.evaluation["candidates"] if run.evaluation and run.evaluation.get("context_id") == ctx |
| else demo_candidates() if run.mode == "demo" |
| else [blank_candidate("M1"), blank_candidate("M2")] |
| ) |
| revision_key = "method_revision_" + ctx |
| st.session_state.setdefault(revision_key, 0) |
| st.caption( |
| "Record where each method detail was found. Documented means researcher-reported " |
| "documentation, not a verified download, executed reproduction or proven scientific result. " |
| "Only the saved evaluation is used for planning; save any edits before continuing." |
| ) |
| if run.mode == "demo": |
| st.warning("FICTIONAL strategies and evidence. No model or laboratory experiment has run.") |
| elif st.button("Suggest methods with this run's Meta backend", key="suggest_" + ctx, |
| disabled=model_client is None): |
| try: |
| proposals = suggest_methods(run, model_client=model_client) |
| st.session_state[state_key] = proposals |
| st.session_state[revision_key] += 1 |
| st.success("Editable model proposals loaded. Inspect sources and review every field.") |
| except (ValueError, PermissionError, RuntimeError, OSError) as exc: |
| st.error("Method suggestions unavailable: " + str(exc)) |
| if any(term in run.question.lower() for term in ("protein", "binder", "egfr", "glp1r")): |
| with st.expander("Protein-design evaluation context"): |
| st.write( |
| "Separate structure confidence, functional evidence, novelty and design diversity. " |
| "For a conditional binder, record relevant pH conditions and species; for an agonist, " |
| "record activation evidence separately from binding. Mark unavailable results UNKNOWN. " |
| "Complementary tools such as backbone generation and sequence design are not automatically " |
| "alternative methods." |
| ) |
| st.markdown("[Proteinbase selection context](https://proteinbase.com/competitions/anthropic-adaptyv-2026/selection)") |
| drafts = st.session_state[state_key] |
| prefix = ctx + "_" + str(st.session_state[revision_key]) |
| count = st.number_input( |
| "Number of candidate methods (0–1 allows deferral only)", min_value=0, max_value=4, |
| value=len(drafts), step=1, key="method_count_" + prefix) |
| records = [] |
| with st.form("method_evaluation_form_" + prefix): |
| for i in range(int(count)): |
| base = drafts[i] if i < len(drafts) else blank_candidate(f"M{i+1}") |
| cid = f"M{i+1}" |
| with st.expander("Candidate " + cid, expanded=True): |
| name = st.text_input("Method name", value=base.get("name", ""), key=prefix + cid + "name") |
| evidence_ids = st.multiselect( |
| "Reviewed source IDs", sources, |
| default=[p for p in base.get("evidence_ids", []) if p in sources], |
| key=prefix + cid + "sources") |
| scope = st.text_area( |
| "Applicability: target, conditions and comparison scope", |
| value=base.get("scope", ""), key=prefix + cid + "scope") |
| checks = {} |
| for check in CHECKS: |
| prior = base.get("checks", {}).get(check, {}) |
| st.markdown("**" + CHECK_LABELS[check] + "**") |
| state = st.selectbox( |
| "Documentation status", CHECK_STATES, |
| index=CHECK_STATES.index(prior.get("status", "unknown")), |
| key=prefix + cid + check + "state") |
| detail = st.text_input( |
| "Finding or reason (required unless unknown)", |
| value=prior.get("detail", ""), key=prefix + cid + check + "detail") |
| reference = st.text_input( |
| "Location / reference (required if documented)", |
| value=prior.get("reference", ""), key=prefix + cid + check + "reference") |
| checks[check] = {"status": state, "detail": detail, "reference": reference} |
| comparison = {} |
| for criterion in CRITERIA: |
| comparison[criterion] = st.text_area( |
| CRITERION_LABELS[criterion], value=base.get("comparison", {}).get(criterion, ""), |
| key=prefix + cid + criterion) |
| records.append({"candidate_id": cid, "name": name, "evidence_ids": evidence_ids, |
| "scope": scope, "checks": checks, "comparison": comparison}) |
| options = ["DEFER"] + [c["candidate_id"] for c in records] |
| previous = (run.evaluation or {}).get("selected_candidate_id") or "DEFER" |
| selected = st.selectbox( |
| "Researcher decision", options, |
| index=options.index(previous) if previous in options else 0, |
| format_func=lambda x: "Defer: insufficient basis for comparison" if x == "DEFER" else x, |
| key="decision_" + prefix) |
| rationale = st.text_area( |
| "Why select this method or defer? (at least 8 characters)", |
| value=(run.evaluation or {}).get("rationale", ""), key="rationale_" + prefix) |
| submitted = st.form_submit_button("Save method evaluation", type="primary") |
| if submitted: |
| try: |
| saved = save_evaluation( |
| run, records, selected_id=None if selected == "DEFER" else selected, |
| rationale=rationale) |
| st.session_state[state_key] = records |
| st.success("Evaluation saved. Status: " + saved["status"]) |
| except (ValueError, PermissionError) as exc: |
| st.error("Evaluation not saved: " + str(exc)) |
| if run.evaluation: |
| try: |
| require_evaluation(run) |
| st.success("Saved comparison is current; a draft plan can be approved below.") |
| except (ValueError, PermissionError) as exc: |
| st.warning(str(exc)) |
| st.dataframe([ |
| {"Method": c["candidate_id"] + " · " + c["name"], |
| "Reproducibility": c["reproducibility_status"], |
| "Open checks": len(c["open_checks"]), |
| "Scope": c["scope"], |
| **{CRITERION_LABELS[k]: c["comparison"][k] for k in CRITERIA}} |
| for c in run.evaluation["candidates"] |
| ], hide_index=True, use_container_width=True) |
| st.caption("No calibrated confidence percentage or scientifically proven winner is computed.") |
| st.json(run.evaluation) |
| st.download_button( |
| "Download method evaluation JSON", |
| data=json.dumps(run.evaluation, ensure_ascii=False, indent=2), |
| file_name="frontierlab_method_evaluation.json", mime="application/json", |
| key="export_eval_" + ctx) |
|
|
|
|
