push

.dockerignore

@@ -0,0 +1,12 @@
+.git
+.gitignore
+**/__pycache__
+**/*.pyc
+**/.pytest_cache
+**/.mypy_cache
+**/.ruff_cache
+**/node_modules
+**/dist
+**/.env
+**/.env.*
+!openenv-polypharmacy/.env.example

.gitattributes

@@ -0,0 +1,35 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -1,6 +1,35 @@
+# --- Python ---
 venv/
-.DS_Store
+.venv/
+env/
+.env
+.env.*
+!openenv-polypharmacy/.env.example
+*.py[cod]
 __pycache__/
+.pytest_cache/
+.mypy_cache/
+.ruff_cache/
+.coverage
+coverage.xml
+
+# --- Node / frontend ---
 node_modules/
-dist/
-.env
+**/node_modules/
+frontend/dist/
+**/dist/
+npm-debug.log*
+yarn-debug.log*
+yarn-error.log*
+pnpm-debug.log*
+
+# --- Build / temp ---
+*.log
+*.tmp
+*.swp
+.DS_Store
+
+# --- Project-specific nested paths ---
+openenv-polypharmacy/frontend/node_modules/
+openenv-polypharmacy/frontend/dist/
+openenv-polypharmacy/.pytest_cache/

.gitignore copy

@@ -0,0 +1,35 @@
+# --- Python ---
+venv/
+.venv/
+env/
+.env
+.env.*
+!openenv-polypharmacy/.env.example
+*.py[cod]
+__pycache__/
+.pytest_cache/
+.mypy_cache/
+.ruff_cache/
+.coverage
+coverage.xml
+
+# --- Node / frontend ---
+node_modules/
+**/node_modules/
+frontend/dist/
+**/dist/
+npm-debug.log*
+yarn-debug.log*
+yarn-error.log*
+pnpm-debug.log*
+
+# --- Build / temp ---
+*.log
+*.tmp
+*.swp
+.DS_Store
+
+# --- Project-specific nested paths ---
+openenv-polypharmacy/frontend/node_modules/
+openenv-polypharmacy/frontend/dist/
+openenv-polypharmacy/.pytest_cache/

Dockerfile

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+FROM node:20-alpine AS frontend-builder
+WORKDIR /app/frontend
+COPY openenv-polypharmacy/frontend/package*.json ./
+RUN npm ci
+COPY openenv-polypharmacy/frontend/ ./
+RUN npm run build
+
+FROM python:3.11-slim
+
+RUN apt-get update && \
+    apt-get install -y --no-install-recommends build-essential curl && \
+    rm -rf /var/lib/apt/lists/*
+
+WORKDIR /app
+
+COPY openenv-polypharmacy/backend/requirements.txt /app/backend/requirements.txt
+RUN pip install --no-cache-dir -r /app/backend/requirements.txt
+
+COPY openenv-polypharmacy/backend /app/backend
+COPY openenv-polypharmacy/data /app/data
+COPY openenv-polypharmacy/scripts /app/scripts
+COPY openenv-polypharmacy/openenv.yaml /app/openenv.yaml
+COPY openenv-polypharmacy/.env.example /app/.env.example
+COPY openenv-polypharmacy/inference.py /app/inference.py
+
+COPY --from=frontend-builder /app/frontend/dist /app/frontend/dist
+
+RUN python3 /app/scripts/preprocess_data.py
+
+ENV PORT=7860
+ENV PYTHONPATH="/app/backend/src:${PYTHONPATH}"
+ENV PYTHONUNBUFFERED=1
+
+EXPOSE 7860
+
+HEALTHCHECK --interval=30s --timeout=3s --start-period=15s --retries=3 \
+    CMD curl -f http://localhost:7860/health || exit 1
+
+CMD ["sh", "-c", "uvicorn backend.main:app --host 0.0.0.0 --port ${PORT:-7860}"]

openenv-polypharmacy/PROMPT.md

@@ -1,571 +0,0 @@
-You are an expert Python backend, ML, and infrastructure engineer.
-Your task is to implement a complete, production-ready OpenEnv environment called **PolypharmacyEnv** for training and evaluating agentic RL policies that act as an "elderly polypharmacy safety agent" (clinical pharmacist assistant).
-
-The deliverable MUST satisfy all of the following:
-- Fully compliant with the OpenEnv spec (typed models, `step()` / `reset()` / `state()`, `openenv.yaml`, HTTP server, Dockerfile).
-- Simulates a realistic healthcare workflow around elderly polypharmacy and dangerous drug combinations.
-- Defines at least **3 tasks** (easy → medium → hard) with deterministic agent graders producing scores in (0.0, 1.0).
-- Provides shaped rewards over the trajectory (not just sparse terminal rewards).
-- Includes a baseline LLM-based inference script `inference.py` in the repo root, following the evaluation requirements:
-  - Uses the OpenAI Python client.
-  - Reads `OPENAI_API_KEY`, `API_BASE_URL`, `MODEL_NAME`, and `HF_TOKEN` from the environment.
-  - Emits structured stdout logs in the exact `[START]`, `[STEP]`, `[END]` format from the OpenEnv sample inference script.
-- Is containerized and deployable as a **Hugging Face Space** tagged with `openenv` that responds to OpenEnv-style `reset` / `step` / `state` HTTP calls.
-
-Implement everything described below.
-
-=================================================
-1. Repository and folder structure
-=================================================
-
-Create a Python package repository with this structure (names are important unless clearly labeled as examples):
-
-- `openenv-polypharmacy/`
-  - `openenv.yaml`
-  - `README.md`
-  - `requirements.txt`
-  - `Dockerfile`
-  - `inference.py`                 # baseline LLM agent per spec
-  - `pyproject.toml` or `setup.cfg` (optional but recommended)
-  - `src/`
-    - `polypharmacy_env/`
-      - `__init__.py`
-      - `config.py`
-      - `models.py`                # Action, Observation, State, helper models
-      - `env_core.py`              # PolypharmacyEnv implementation
-      - `tasks.py`                 # task setup utilities
-      - `graders.py`               # deterministic graders for each task
-      - `rewards.py`               # reward shaping logic
-      - `data_loader.py`           # load/preprocess patient and lookup data
-      - `ddi_simulator.py`         # local DDI / guideline simulator
-      - `api/`
-        - `__init__.py`
-        - `schemas.py`            # HTTP request/response schemas
-        - `server.py`             # FastAPI app exposing OpenEnv endpoints
-      - `baselines/`
-        - `__init__.py`
-        - `heuristic_agent.py`    # simple rule-based baseline agent
-        - `random_agent.py`       # trivial random baseline (optional)
-      - `tests/`
-        - `__init__.py`
-        - `test_env_core.py`
-        - `test_api.py`
-  - `data/`
-    - `raw/`                      # placeholder for real/synthetic source data
-    - `processed/`
-    - `lookups/`
-      - `ddi_rules.csv`
-      - `beers_criteria.csv`
-      - `drug_metadata.csv`
-  - `scripts/`
-    - `preprocess_data.py`
-    - `run_validation.sh`         # optional; runs OpenEnv validator, tests, etc.
-
-Use Python 3.10+ with full type hints, and keep the code black/isort-compatible.
-
-=================================================
-2. Domain, data, and clinical abstraction
-=================================================
-
-2.1. Core scenario
-
-Model an elderly patient (age ≥ 65) with:
-- Demographics: age, sex.
-- Comorbidities: e.g., hypertension, diabetes, heart failure, CKD, dementia.
-- Basic labs: kidney function (eGFR category), liver function category.
-- A current medication list (polypharmacy, e.g., 3–15 drugs depending on task).
-
-Each **episode** is one medication-review session where the agent:
-- Observes patient info and current meds.
-- Optionally **queries** a DDI/guideline tool for specific drug pairs.
-- Proposes **interventions**:
-  - `stop`: discontinue a drug.
-  - `dose_reduce`: lower dose of a drug.
-  - `substitute`: swap to a safer alternative.
-  - `add_monitoring`: keep the drug but flag extra monitoring.
-- Calls `finish_review` when it decides the regimen is acceptable or budgets are exhausted.
-
-No external PHI, EHRs, or online APIs: all data is **synthetic** or de-identified and local to the container (CSV files).
-
-2.2. Data files and CSV schemas
-
-Implement local CSVs under `data/lookups/`:
-
-**`drug_metadata.csv`**
-- `drug_id` (string; unique key)
-- `generic_name` (string)
-- `atc_class` (string)
-- `is_high_risk_elderly` (0/1)
-- `default_dose_mg` (float)
-- `min_dose_mg` (float)
-- `max_dose_mg` (float)
-
-**`beers_criteria.csv`**
-- `drug_id` (string)
-- `criterion_type` (enum string: `avoid`, `caution`, `dose_adjust`, `avoid_in_condition`)
-- `condition` (nullable string; e.g., `CKD`, `dementia`)
-- `rationale` (brief text)
-
-**`ddi_rules.csv`**
-- `drug_id_1` (string; normalized so `drug_id_1 < drug_id_2` lexicographically)
-- `drug_id_2` (string)
-- `severity` (enum string: `mild`, `moderate`, `severe`)
-- `mechanism` (short text)
-- `recommendation` (enum string: `avoid_combination`, `monitor_closely`, `dose_adjust`, `no_action`)
-- `base_risk_score` (float in [0.0, 1.0])
-
-Implement a synthetic patient-episode dataset under `data/processed/`:
-
-**`patients_polypharmacy.csv`**
-- `episode_id` (string)
-- `age` (int)
-- `sex` (enum: `M`, `F`, `O`)
-- `conditions` (semicolon-separated; e.g., `HTN;DM;CKD`)
-- `eGFR_category` (enum: `normal`, `mild`, `moderate`, `severe`)
-- `liver_function_category` (enum: `normal`, `impaired`)
-- `medication_ids` (semicolon-separated list of `drug_id`)
-- `baseline_risk_score` (float in [0.0, 1.0])
-
-2.3. Preprocessing script
-
-In `scripts/preprocess_data.py`:
-- If real data is not provided, procedurally generate synthetic but plausible data using:
-  - Random combinations of conditions and drugs constrained by simple rules (e.g., CKD + renally-cleared drugs).
-  - Controlled distribution of high-risk DDIs and Beers violations.
-- Explicitly tag episodes as easy/medium/hard (e.g., via number of drugs, number/severity of DDIs, and number of Beers issues).
-- Save `patients_polypharmacy.csv` ready for the environment to consume.
-
-=================================================
-3. OpenEnv models and environment implementation
-=================================================
-
-3.1. Models
-
-In `models.py`, define dataclasses or Pydantic models that extend the appropriate OpenEnv base types (`Action`, `Observation`, `State`) and are JSON-compatible.
-
-Auxiliary models:
-
-**`MedicationEntry`**
-- `drug_id: str`
-- `generic_name: str`
-- `atc_class: str`
-- `dose_mg: float`
-- `frequency: str`          # e.g., `qd`, `bid`
-- `route: str`              # e.g., `po`
-- `is_high_risk_elderly: bool`
-- `beers_flags: list[str]`  # e.g., `["avoid", "dose_adjust_CKD"]`
-
-**`InteractionQueryRecord`**
-- `drug_id_1: str`
-- `drug_id_2: str`
-- `severity: str | None`
-- `recommendation: str | None`
-- `risk_score: float | None`
-- `step_index: int`
-
-**`InterventionRecord`**
-- `target_drug_id: str`
-- `action_type: Literal["stop", "dose_reduce", "substitute", "add_monitoring"]`
-- `proposed_new_drug_id: str | None`
-- `rationale: str`
-- `step_index: int`
-
-Core wire models:
-
-**`PolypharmacyObservation`** (extends OpenEnv `Observation`)
-- `episode_id: str`
-- `task_id: Literal["easy_screening", "budgeted_screening", "complex_tradeoff"]`
-- `age: int`
-- `sex: str`
-- `conditions: list[str]`
-- `eGFR_category: str`
-- `liver_function_category: str`
-- `current_medications: list[MedicationEntry]`
-- `interaction_queries: list[InteractionQueryRecord]`
-- `interventions: list[InterventionRecord]`
-- `step_index: int`
-- `remaining_query_budget: int`
-- `remaining_intervention_budget: int`
-- `shaped_reward: float`  # reward from last step
-- `done: bool`
-
-**`PolypharmacyAction`** (extends OpenEnv `Action`)
-- `action_type: Literal["query_ddi", "propose_intervention", "finish_review"]`
-- `drug_id_1: str | None`        # for DDI queries or some interventions
-- `drug_id_2: str | None`        # for DDI queries
-- `target_drug_id: str | None`   # for interventions
-- `intervention_type: Literal["stop", "dose_reduce", "substitute", "add_monitoring", "none"] | None`
-- `proposed_new_drug_id: str | None`
-- `rationale: str | None`
-
-**`PolypharmacyState`** (extends OpenEnv `State`)
-- `episode_id: str`
-- `task_id: str`
-- `step_count: int`
-- `max_steps: int`
-- `num_query_actions: int`
-- `num_interventions: int`
-
-3.2. Environment core
-
-In `env_core.py`, implement `PolypharmacyEnv` extending the appropriate OpenEnv environment base class. It must implement:
-
-**`reset(task_id: str | None = None) -> PolypharmacyObservation`**
-- If `task_id` is `None`, default to medium (`budgeted_screening`).
-- Sample an episode from `patients_polypharmacy.csv` filtered by difficulty.
-- Initialize:
-  - `episode_id`
-  - `step_count = 0`
-  - task-specific budgets (query, interventions, max_steps)
-  - baseline regime and risk
-  - empty `interaction_queries` and `interventions`
-- Return the initial `PolypharmacyObservation` with:
-  - `step_index = 0`
-  - `shaped_reward = 0.0`
-  - `done = False`
-
-**`step(action: PolypharmacyAction) -> dict`**
-- Validate the action; if invalid:
-  - Apply a negative reward.
-  - Do not modify regimen, but log error in `info`.
-- If `action_type == "query_ddi"`:
-  - If query budget exhausted, apply penalty and do not query.
-  - Else:
-    - Use `ddi_simulator.lookup_ddi(drug_id_1, drug_id_2)` to get severity, recommendation, base_risk_score.
-    - Append an `InteractionQueryRecord`.
-    - Apply a small negative reward for query cost.
-- If `action_type == "propose_intervention"`:
-  - If intervention budget exhausted, apply penalty and ignore change.
-  - Else:
-    - Update `current_medications` according to `intervention_type`:
-      - `stop`: remove medication.
-      - `dose_reduce`: adjust dose downward within [min_dose_mg, default_dose_mg].
-      - `substitute`: replace with a safer alternative from same `atc_class`.
-      - `add_monitoring`: keep drug but tag in internal state.
-    - Append an `InterventionRecord`.
-    - Recompute current regimen risk using the risk model (see 3.3).
-    - Compute shaped reward = (previous_risk - new_risk) - small intervention cost.
-- If `action_type == "finish_review"`:
-  - Mark `done = True`.
-  - Call the task’s grader to get episode-level score in [0.0, 1.0].
-  - Add this as a terminal bonus to the current step reward.
-
-- In all cases:
-  - Increment `step_count`.
-  - Check `max_steps`; if exceeded, auto-terminate:
-    - `done = True`
-    - apply time-out penalty
-    - call grader with current trajectory for a final score if appropriate.
-  - Construct next `PolypharmacyObservation` with updated fields.
-  - Return a dict:
-    - `observation`: `PolypharmacyObservation`
-    - `reward`: float shaped reward for this step
-    - `done`: bool
-    - `info`: dict with fields like `current_risk`, `baseline_risk`, `grader_score_if_terminal`, and debug flags.
-
-**`state` property**
-- Returns `PolypharmacyState` reflecting the current internal state.
-
-3.3. DDI simulator and risk model
-
-In `ddi_simulator.py`:
-- Load `ddi_rules.csv` once via `data_loader`.
-- Implement `lookup_ddi(drug_id_1, drug_id_2) -> tuple[severity, recommendation, base_risk_score]`:
-  - Normalize the pair ordering.
-  - Look up row; if missing, return:
-    - severity = `"none"`
-    - recommendation = `"no_action"`
-    - base_risk_score = 0.0
-
-In `rewards.py` (or a dedicated module), implement:
-- `compute_regimen_risk(current_drug_ids, patient_context, ddi_rules, beers_rules, drug_metadata) -> float`
-  - Aggregate contributions from:
-    - Beers violations (weighted by `criterion_type` and relevant conditions).
-    - DDI base risk scores for all present drug pairs.
-    - High-risk elderly drugs.
-  - Normalize and clip to [0.0, 1.0].
-
-Use this function to compute:
-- `baseline_risk` at episode start.
-- Risk after each intervention step.
-
-Also implement:
-- `compute_shaped_reward(previous_risk, new_risk, action, context, partial_metrics) -> float`
-  - Positive component: `previous_risk - new_risk`.
-  - Negative components: per-query cost, per-intervention cost, invalid-action penalty, time-out penalty.
-
-=================================================
-4. Tasks and graders (3 difficulty levels)
-=================================================
-
-Define three task IDs and semantics in `tasks.py` and `graders.py`:
-
-Task IDs:
-- `easy_screening`
-- `budgeted_screening`
-- `complex_tradeoff`
-
-4.1. `easy_screening` (easy)
-
-- Small regimen: 3–5 drugs.
-- Exactly one **severe** DDI pair and possibly one simple Beers violation.
-- Budgets:
-  - query_budget ≈ 4
-  - intervention_budget ≈ 2
-  - max_steps ≈ 10
-
-Grader:
-- Input: full trajectory, baseline risk, final risk, list of interventions.
-- Compute:
-  - `risk_reduction = max(0.0, baseline_risk - final_risk) / max(baseline_risk, ε)` (normalized).
-  - `targeted_intervention_flag = 1.0` if at least one intervention affects one of the drugs in the known severe DDI pair, else 0.0.
-- Score:
-  - `score = 0.5 * risk_reduction + 0.5 * targeted_intervention_flag`
-  - Clip to [0.0, 1.0].
-
-4.2. `budgeted_screening` (medium)
-
-- Medium regimen: 6–10 drugs.
-- Multiple DDIs (mild/moderate/severe) and multiple Beers issues.
-- Budgets:
-  - query_budget ≈ 8
-  - intervention_budget ≈ 3
-  - max_steps ≈ 20
-
-Grader:
-- Compute:
-  - `risk_reduction_score` as normalized risk drop.
-  - `intervention_precision_score` = fraction of interventions that actually reduce risk or fix guideline violations.
-  - `query_efficiency_score` = (number of severe/moderate DDIs discovered) / (number of queries used), normalized.
-- Weighted score, for example:
-  - `score = 0.5 * risk_reduction_score + 0.3 * intervention_precision_score + 0.2 * query_efficiency_score`
-  - Clip to [0.0, 1.0].
-
-4.3. `complex_tradeoff` (hard)
-
-- Larger regimen: 10–15 drugs.
-- Some drugs are **clinically critical** (e.g., anticoagulants, insulin analogues) and encoded as such in `drug_metadata` or a small internal map.
-- Episodes contain:
-  - multiple DDIs and Beers issues, including ones involving critical drugs.
-  - safer substitutes for some risky drugs.
-
-Budgets:
-- query_budget ≈ 12
-- intervention_budget ≈ 5
-- max_steps ≈ 30
-
-Grader adds a **regimen disruption penalty** component:
-- Metrics:
-  - `risk_reduction_score` (as above).
-  - `critical_drug_penalty` = penalty if a critical drug is stopped without substitution to another suitable agent.
-  - `total_drug_changes` = number of drugs stopped or substituted.
-  - `regimen_disruption_penalty` derived from `total_drug_changes` and `critical_drug_penalty`.
-
-Example scoring:
-- `base = risk_reduction_score`
-- `penalty = α * regimen_disruption_penalty`
-- `score = clamp(base - penalty, 0.0, 1.0)`
-
-4.4. Reward shaping
-
-In `rewards.py`, define a consistent shaping scheme:
-- On each query:
-  - Small negative reward (e.g., −0.01) plus any small bonus if it discovers a severe DDI, if desired.
-- On each intervention:
-  - Reward ≈ (previous_risk - new_risk) − small intervention cost.
-- On invalid actions:
-  - Larger negative reward (e.g., −0.1) and no state change.
-- On `finish_review`:
-  - Add the task-level `score` ∈ [0.0, 1.0] from the corresponding grader to that step’s shaped reward.
-
-Ensure the sum of step rewards per episode remains in a reasonable numeric range (e.g., roughly -5 to +5) while still allowing meaningful differentiation by graders.
-
-=================================================
-5. HTTP API server and openenv.yaml
-=================================================
-
-5.1. HTTP server (FastAPI)
-
-In `api/server.py`:
-- Implement a FastAPI app that maintains a `PolypharmacyEnv` instance (or a multiplexing scheme if needed).
-- Endpoints:
-  - `POST /reset`:
-    - Request body: may include `task_id` (string).
-    - Response: serialized `PolypharmacyObservation`.
-  - `POST /step`:
-    - Request body: serialized `PolypharmacyAction`.
-    - Response: dict with:
-      - `observation`: `PolypharmacyObservation`
-      - `reward`: float
-      - `done`: bool
-      - `info`: dict
-  - `GET /state`:
-    - Response: `PolypharmacyState`.
-
-Provide a module-level `app = FastAPI(...)` object for use with uvicorn and Hugging Face Spaces. Ensure the JSON schema is consistent with OpenEnv clients (simple, flat JSON for observation/action/state).
-
-5.2. `openenv.yaml`
-
-At repo root, define `openenv.yaml` consistent with the latest OpenEnv spec. At minimum, include:
-- `name`: `polypharmacy_env`
-- `version`: e.g., `0.1.0`
-- `description`: human-readable description.
-- `author`: your details.
-- `tags`: e.g., `["healthcare", "polypharmacy", "openenv"]`
-- `tasks`:
-  - One entry per task:
-    - `id`: `"easy_screening"` / `"budgeted_screening"` / `"complex_tradeoff"`
-    - `description`: one-line description
-    - `difficulty`: `"easy"`, `"medium"`, `"hard"`
-
-Ensure `openenv validate` (or equivalent validator) passes once implemented.
-
-=================================================
-6. Baseline heuristic (non-LLM) agent
-=================================================
-
-In `baselines/heuristic_agent.py`, implement a simple, deterministic baseline agent that:
-
-For each episode:
-- Iterates through all unordered medication pairs within query budget:
-  - Calls `query_ddi` via the environment for each pair until the query budget is exhausted or all pairs are examined.
-  - Records severe and moderate interactions.
-- After querying:
-  - For each severe DDI pair:
-    - Try `substitute` one of the drugs using `drug_metadata`:
-      - Prefer substitute within same `atc_class` that:
-        - is not marked high-risk elderly.
-        - does not participate in known severe DDIs with the rest of the regimen.
-    - If no substitute exists, propose `stop` for the higher-risk drug.
-  - Respect intervention budget limits.
-- Finally, call `finish_review`.
-
-This baseline should be callable as a simple Python function that interacts with `PolypharmacyEnv` directly (without HTTP).
-
-=================================================
-7. Baseline LLM inference script (inference.py)
-=================================================
-
-At repo root, create `inference.py` that:
-
-7.1. Uses the OpenAI Python client
-
-- Import and configure the official OpenAI Python client.
-- Read environment variables:
-  - `OPENAI_API_KEY` (required).
-  - `API_BASE_URL` (base URL for LLM; default to OpenAI standard if not set).
-  - `MODEL_NAME` (e.g., `gpt-4.1` or similar).
-  - `HF_TOKEN` (if needed for HF auth; do not hardcode).
-- Read `POLYPHARMACY_ENV_URL` (or similar) for the environment’s HTTP base URL.
-
-7.2. Implements the required logging format
-
-- For each **run** across all tasks:
-  - Emit a `[START]` line with a JSON payload exactly matching the evaluation specification:
-    - Fields such as `run_id`, `task_id`, `model`, etc., in the same order and naming as the sample OpenEnv inference script.
-- For each **step** in an episode:
-  - Emit a `[STEP]` line with JSON fields including:
-    - `run_id`
-    - `task_id`
-    - `episode_id`
-    - `step_index`
-    - `observation_summary` (brief, machine-readable summary)
-    - `action_payload` (the action sent to the env)
-    - `reward`
-    - `done`
-- After finishing an episode for a task:
-  - Emit an `[END]` line summarizing:
-    - `run_id`
-    - `task_id`
-    - per-episode statistics (e.g., total reward, grader score from last step’s `info`).
-- The stdout format MUST follow the sample exactly:
-  - Same tags: `[START]`, `[STEP]`, `[END]`.
-  - Same JSON field names and ordering as the provided reference.
-  - No extra prints except these structured logs (and necessary error messages to stderr).
-
-7.3. LLM agent loop
-
-- For each task (`easy_screening`, `budgeted_screening`, `complex_tradeoff`):
-  - Run a fixed small number of episodes (e.g., 5–10 per task) for baseline scoring.
-  - For each episode:
-    - Call `/reset` with the task id.
-    - At each step:
-      - Summarize the observation into a concise prompt for the LLM:
-        - Include age, sex, conditions, high-risk flags, budgets, and a compressed view of meds and previous actions.
-      - Ask the model to output a **strict JSON** representing `PolypharmacyAction` fields.
-      - Parse and validate the JSON; if invalid, fall back to a safe default (e.g., `finish_review` or a no-op) and penalize in evaluation.
-      - Send this action to `/step` and log `[STEP]`.
-    - End when `done=True` or max_steps is reached.
-- At the end, print aggregate scores per task and overall.
-
-Make sure runtime < 20 minutes and that the script can run within 2 vCPUs and 8 GB RAM.
-
-=================================================
-8. Dockerfile and Hugging Face Space
-=================================================
-
-8.1. Dockerfile
-
-Create a `Dockerfile` that:
-- Starts from a slim Python image (e.g., `python:3.11-slim`).
-- Installs system dependencies as needed (e.g., `build-essential`, `curl`).
-- Copies the project into the container.
-- Installs Python dependencies from `requirements.txt`.
-- Sets appropriate environment variables for the app (e.g., `PORT=7860`).
-- Exposes port 7860.
-- Uses a `CMD` or `ENTRYPOINT` that runs the FastAPI server, for example:
-  - `uvicorn polypharmacy_env.api.server:app --host 0.0.0.0 --port 7860`
-
-8.2. Hugging Face Space
-
-Ensure the repository is ready to be used as a Hugging Face Space:
-- Space type: `docker`.
-- Tag: `openenv`.
-- On container start, the server must listen on the correct port and respond to:
-  - `POST /reset`
-  - `POST /step`
-  - `GET /state`
-- The environment must start cleanly with `docker build` + `docker run` locally.
-
-=================================================
-9. README and documentation
-=================================================
-
-In `README.md`, include:
-
-- **Environment description & motivation**:
-  - What PolypharmacyEnv simulates.
-  - Why elderly polypharmacy safety matters.
-- **Action and observation spaces**:
-  - Describe `PolypharmacyAction`, `PolypharmacyObservation`, and `PolypharmacyState` fields and semantics.
-- **Task descriptions**:
-  - `easy_screening`, `budgeted_screening`, `complex_tradeoff`, their difficulty and goals.
-- **Reward structure**:
-  - Summarize shaping and terminal rewards.
-- **Setup & usage**:
-  - How to install dependencies.
-  - How to run the API server locally (uvicorn command).
-  - How to run the heuristic baseline.
-  - How to run `inference.py` with environment variables.
-- **Baseline scores**:
-  - Document reproducible baseline scores for each task (heuristic agent, and LLM baseline if available).
-
-=================================================
-10. Validation and quality gates
-=================================================
-
-- Ensure:
-  - `openenv.yaml` and the HTTP server pass the OpenEnv validation script.
-  - `docker build` and `docker run` work without errors.
-  - `inference.py` completes under 20 minutes, within 2 vCPUs / 8 GB RAM.
-  - All graders:
-    - Are deterministic.
-    - Return scores strictly in [0.0, 1.0].
-  - No grader returns a constant score irrespective of behavior.
-
-Aim for clean, well-structured, well-documented code with clear separation of concerns between:
-- Data loading,
-- Environment state & dynamics,
-- Reward/grade logic,
-- HTTP serving,
-- Baseline agents and inference.

openenv-polypharmacy/frontend/src/App.jsx

@@ -1,7 +1,22 @@
 import { useEffect, useMemo, useRef, useState } from "react";
 
-const API_BASE = "http://localhost:7860";
-const WS_URL = "ws://localhost:7860/ws";
+function resolveApiBase() {
+  const explicitBase = import.meta.env.VITE_API_BASE;
+  if (explicitBase) return explicitBase.replace(/\/$/, "");
+
+  const host = window.location.hostname;
+  const isLocal =
+    host === "localhost" || host === "127.0.0.1" || host === "0.0.0.0";
+
+  // In local Vite dev, backend runs on :7860. In Spaces/prod, serve same-origin.
+  if (isLocal && window.location.port === "5173") {
+    return "http://localhost:7860";
+  }
+  return window.location.origin.replace(/\/$/, "");
+}
+
+const API_BASE = resolveApiBase();
+const WS_URL = `${API_BASE.replace(/^http/, "ws")}/ws`;
 const TASKS = ["easy_screening", "budgeted_screening", "complex_tradeoff"];
 
 async function apiPost(path, body) {
@@ -225,6 +240,7 @@ export default function App() {
       <header className="topbar glass">
         <div className="title-wrap">
           <h1>Polypharmacy Control Center</h1>
+          <p>Metaverse Clinical Ops Console</p>
         </div>
         <div className={`status-chip ${hasValidEpisode ? "live" : "idle"}`}>
           {hasValidEpisode ? "Session Live" : "Waiting for reset"}

openenv-polypharmacy/frontend/src/styles.css

@@ -1,15 +1,18 @@
 :root {
-  --bg: #eef5ff;
-  --panel: rgba(255, 255, 255, 0.82);
-  --panel-solid: #ffffff;
-  --text: #0b2445;
-  --muted: #5b7596;
-  --primary: #1f8bff;
-  --primary-2: #69beff;
-  --accent: #0dd3ff;
-  --border: rgba(93, 156, 219, 0.22);
-  --shadow: 0 20px 50px rgba(25, 83, 143, 0.12);
-  --shadow-strong: 0 20px 42px rgba(31, 112, 182, 0.24);
+  --bg: #070814;
+  --bg-layer: #0a1026;
+  --panel: rgba(14, 22, 44, 0.72);
+  --panel-solid: rgba(20, 28, 52, 0.92);
+  --text: #e8f1ff;
+  --muted: #9ab2db;
+  --primary: #37d4ff;
+  --primary-2: #5a8dff;
+  --accent: #9d59ff;
+  --success: #6dfbcf;
+  --border: rgba(122, 162, 255, 0.28);
+  --line: rgba(109, 143, 225, 0.18);
+  --shadow: 0 16px 45px rgba(5, 8, 23, 0.6);
+  --shadow-strong: 0 14px 32px rgba(44, 105, 255, 0.4);
 }
 
 * {
@@ -18,156 +21,197 @@
 
 body {
   margin: 0;
-  font-family: "Inter", "SF Pro Text", "Segoe UI", sans-serif;
-  background:
-    radial-gradient(circle at 8% 0%, #cce7ff 0%, rgba(204, 231, 255, 0) 42%),
-    radial-gradient(circle at 92% 100%, #d5efff 0%, rgba(213, 239, 255, 0) 42%),
-    var(--bg);
   color: var(--text);
+  font-family: "Segoe UI", "SF Pro Text", "Helvetica Neue", sans-serif;
+  background:
+    radial-gradient(circle at 8% 12%, rgba(121, 87, 255, 0.22), transparent 38%),
+    radial-gradient(circle at 88% 20%, rgba(59, 204, 255, 0.26), transparent 34%),
+    radial-gradient(circle at 50% 100%, rgba(43, 128, 255, 0.26), transparent 40%),
+    linear-gradient(145deg, var(--bg) 0%, var(--bg-layer) 60%, #04060f 100%);
+  background-attachment: fixed;
 }
 
 .shell {
   min-height: 100vh;
   position: relative;
-  padding: 28px 22px;
   overflow: hidden;
+  padding: 24px 16px 34px;
 }
 
 .container {
-  width: min(1300px, 100%);
+  width: min(1320px, 100%);
   margin: 0 auto;
   position: relative;
-  z-index: 1;
+  z-index: 2;
 }
 
 .bg-orb {
   position: absolute;
   border-radius: 50%;
-  filter: blur(18px);
+  pointer-events: none;
   opacity: 0.9;
+  filter: blur(18px);
 }
+
 .orb-a {
-  width: 420px;
-  height: 420px;
-  right: -120px;
-  top: -100px;
-  background: radial-gradient(circle, rgba(72, 168, 255, 0.5), rgba(72, 168, 255, 0.1));
+  width: min(46vw, 530px);
+  aspect-ratio: 1 / 1;
+  right: -9%;
+  top: -10%;
+  background: radial-gradient(circle, rgba(52, 203, 255, 0.35), rgba(52, 203, 255, 0.04) 70%);
 }
+
 .orb-b {
-  width: 360px;
-  height: 360px;
-  left: -100px;
-  bottom: -120px;
-  background: radial-gradient(circle, rgba(110, 200, 255, 0.4), rgba(141, 205, 255, 0.06));
+  width: min(40vw, 460px);
+  aspect-ratio: 1 / 1;
+  left: -9%;
+  bottom: -15%;
+  background: radial-gradient(circle, rgba(160, 102, 255, 0.3), rgba(160, 102, 255, 0.06) 72%);
 }
 
 .glass {
-  backdrop-filter: blur(12px);
+  background:
+    linear-gradient(180deg, rgba(255, 255, 255, 0.06), rgba(255, 255, 255, 0.01)),
+    var(--panel);
   border: 1px solid var(--border);
-  background: var(--panel);
   box-shadow: var(--shadow);
+  backdrop-filter: blur(12px);
 }
 
 .topbar {
-  border-radius: 20px;
-  padding: 18px;
+  border-radius: 24px;
+  padding: clamp(14px, 2vw, 20px);
   display: grid;
-  grid-template-columns: 1.2fr auto 1fr;
-  justify-content: space-between;
+  gap: 12px 16px;
+  grid-template-columns: minmax(220px, 1.2fr) auto minmax(280px, 1fr);
   align-items: center;
-  gap: 12px;
 }
 
 .title-wrap h1 {
   margin: 0;
-  font-size: clamp(1.1rem, 1.5vw, 1.45rem);
-  letter-spacing: 0.01em;
+  font-size: clamp(1.15rem, 2.2vw, 1.95rem);
+  letter-spacing: 0.02em;
+  text-transform: uppercase;
+  text-shadow: 0 0 16px rgba(106, 192, 255, 0.3);
 }
 
 .title-wrap p {
-  margin: 4px 0 0;
+  margin: 6px 0 0;
+  font-size: 0.84rem;
   color: var(--muted);
-  font-size: 0.92rem;
+  letter-spacing: 0.03em;
+  text-transform: uppercase;
 }
 
 .status-chip {
   justify-self: center;
+  padding: 7px 14px;
   border-radius: 999px;
-  padding: 7px 12px;
-  font-size: 0.76rem;
+  font-size: 0.72rem;
   font-weight: 700;
-  letter-spacing: 0.04em;
+  letter-spacing: 0.08em;
   text-transform: uppercase;
   border: 1px solid transparent;
 }
 
 .status-chip.live {
-  color: #0d6a3f;
-  background: rgba(130, 245, 195, 0.18);
-  border-color: rgba(70, 199, 142, 0.3);
+  color: #052c24;
+  background: linear-gradient(90deg, rgba(126, 255, 220, 0.9), rgba(84, 244, 196, 0.95));
+  box-shadow: 0 0 14px rgba(96, 244, 198, 0.36);
 }
 
 .status-chip.idle {
-  color: #24527f;
-  background: rgba(114, 194, 255, 0.18);
-  border-color: rgba(62, 152, 223, 0.28);
+  color: #d8e8ff;
+  border-color: rgba(117, 186, 255, 0.48);
+  background: rgba(60, 106, 198, 0.25);
 }
 
 .actions {
   display: flex;
   justify-content: flex-end;
-  gap: 10px;
   flex-wrap: wrap;
+  gap: 10px;
 }
 
 button,
 select,
 input {
-  border: 1px solid var(--border);
+  width: 100%;
+  min-height: 42px;
   border-radius: 12px;
-  padding: 10px 13px;
+  border: 1px solid var(--border);
   font-size: 0.92rem;
-  background: #fff;
+  padding: 10px 12px;
   color: var(--text);
-  min-height: 42px;
+  background: rgba(11, 19, 38, 0.84);
+}
+
+select,
+input {
+  transition: border-color 120ms ease, box-shadow 120ms ease;
+}
+
+select:focus,
+input:focus {
+  outline: none;
+  border-color: rgba(119, 200, 255, 0.88);
+  box-shadow: 0 0 0 2px rgba(95, 187, 255, 0.18);
 }
 
 button {
   cursor: pointer;
-  background: linear-gradient(135deg, var(--primary) 0%, var(--primary-2) 78%, var(--accent) 100%);
-  color: #fff;
-  border: none;
+  border: 0;
+  width: auto;
   font-weight: 700;
+  letter-spacing: 0.02em;
+  background: linear-gradient(135deg, var(--primary), var(--primary-2) 55%, var(--accent));
   box-shadow: var(--shadow-strong);
-  transition: transform 120ms ease, filter 120ms ease;
+  transition: transform 140ms ease, filter 140ms ease, box-shadow 140ms ease;
 }
 
 button:hover {
   transform: translateY(-1px);
-  filter: brightness(1.02);
+  filter: brightness(1.04);
+  box-shadow: 0 18px 32px rgba(50, 141, 255, 0.48);
+}
+
+button:active {
+  transform: translateY(0);
 }
 
 button.secondary {
-  background: linear-gradient(135deg, #68c2ff, #9dd9ff);
+  background: linear-gradient(135deg, rgba(95, 185, 255, 0.9), rgba(154, 102, 255, 0.86));
 }
 
 button:disabled {
-  opacity: 0.58;
+  opacity: 0.56;
   cursor: not-allowed;
+  filter: grayscale(0.2);
+  box-shadow: none;
   transform: none;
 }
 
 .layout {
-  margin-top: 18px;
+  margin-top: 16px;
   display: grid;
-  gap: 16px;
-  grid-template-columns: 1.15fr 0.85fr;
+  gap: 14px;
+  grid-template-columns: 1.12fr 0.88fr;
   align-items: start;
 }
 
 .panel {
-  border-radius: 18px;
-  padding: 18px;
+  border-radius: 20px;
+  padding: clamp(14px, 1.8vw, 20px);
+  position: relative;
+}
+
+.panel::after {
+  content: "";
+  position: absolute;
+  inset: 0;
+  border-radius: inherit;
+  border: 1px solid var(--line);
+  pointer-events: none;
 }
 
 .panel-wide {
@@ -177,31 +221,36 @@ button:disabled {
 .panel h2 {
   margin: 0 0 12px;
   font-size: 1rem;
-  letter-spacing: 0.01em;
+  font-weight: 700;
+  letter-spacing: 0.05em;
+  text-transform: uppercase;
 }
 
 .kpi-grid {
   display: grid;
+  gap: 10px;
   grid-template-columns: repeat(3, minmax(0, 1fr));
-  gap: 12px;
 }
 
 .kpi-grid div {
-  background: rgba(255, 255, 255, 0.9);
+  border-radius: 13px;
   border: 1px solid var(--border);
-  border-radius: 14px;
-  padding: 12px;
+  background: var(--panel-solid);
+  padding: 11px 12px;
 }
 
 .kpi-grid span {
   display: block;
-  font-size: 0.74rem;
-  color: var(--muted);
   margin-bottom: 4px;
+  font-size: 0.72rem;
+  color: var(--muted);
+  text-transform: uppercase;
+  letter-spacing: 0.05em;
 }
 
 .kpi-grid strong {
-  font-size: 1.05rem;
+  font-size: 1.06rem;
+  line-height: 1.2;
 }
 
 .action-row,
@@ -211,6 +260,13 @@ button:disabled {
   margin-bottom: 12px;
 }
 
+.action-row label {
+  color: var(--muted);
+  font-size: 0.78rem;
+  letter-spacing: 0.05em;
+  text-transform: uppercase;
+}
+
 .stack-two {
   grid-template-columns: repeat(2, minmax(0, 1fr));
 }
@@ -219,22 +275,22 @@ button:disabled {
   display: grid;
   grid-template-columns: repeat(3, minmax(0, 1fr));
   gap: 10px;
-  max-height: 420px;
+  max-height: 430px;
   overflow: auto;
-  padding-right: 2px;
+  padding-right: 4px;
 }
 
 .med-card {
-  border: 1px solid var(--border);
   border-radius: 14px;
-  padding: 12px;
+  border: 1px solid var(--border);
   background: var(--panel-solid);
-  transition: transform 120ms ease, box-shadow 120ms ease;
+  padding: 11px 12px;
+  transition: transform 130ms ease, border-color 130ms ease;
 }
 
 .med-card:hover {
   transform: translateY(-1px);
-  box-shadow: 0 10px 25px rgba(44, 105, 165, 0.12);
+  border-color: rgba(109, 224, 255, 0.72);
 }
 
 .med-card p {
@@ -243,37 +299,42 @@ button:disabled {
   text-transform: capitalize;
 }
 
+.med-card small {
+  color: #c7d9ff;
+}
+
 .logs {
-  font-family: ui-monospace, SFMono-Regular, Menlo, Monaco, monospace;
-  font-size: 0.85rem;
   max-height: 300px;
   overflow: auto;
+  padding-right: 4px;
   display: grid;
-  gap: 6px;
-  padding-right: 2px;
+  gap: 7px;
+  font-size: 0.84rem;
+  font-family: ui-monospace, SFMono-Regular, Menlo, Monaco, monospace;
 }
 
 .logs div {
-  background: rgba(255, 255, 255, 0.78);
-  border: 1px solid var(--border);
   border-radius: 10px;
+  border: 1px solid var(--border);
+  background: rgba(10, 16, 31, 0.84);
   padding: 8px 10px;
+  color: #dbebff;
 }
 
 .muted {
-  color: var(--muted);
   margin: 0;
+  color: var(--muted);
 }
 
 .budget-note {
   margin-top: 10px;
-  padding: 10px 12px;
   border: 1px solid var(--border);
   border-radius: 12px;
-  background: rgba(255, 255, 255, 0.78);
+  padding: 10px 12px;
+  background: rgba(13, 22, 42, 0.82);
 }
 
-@media (max-width: 1120px) {
+@media (max-width: 1180px) {
   .layout {
     grid-template-columns: 1fr;
   }
@@ -293,7 +354,21 @@ button:disabled {
 
 @media (max-width: 760px) {
   .shell {
-    padding: 18px 12px;
+    padding: 14px 10px 24px;
+  }
+
+  .topbar,
+  .panel {
+    border-radius: 16px;
+  }
+
+  .actions {
+    width: 100%;
+  }
+
+  .actions button,
+  .actions select {
+    width: 100%;
   }
 
   .kpi-grid,
@@ -301,4 +376,8 @@ button:disabled {
   .stack-two {
     grid-template-columns: 1fr;
   }
+
+  .logs {
+    max-height: 240px;
+  }
 }