Merge pull request #1 from Vishwa-docs/adi

Feat: React UI Implementation and Backend FastAPI Refactoring

.dockerignore

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+.git
+.gitignore
+**/__pycache__
+**/*.pyc
+**/.pytest_cache
+**/.mypy_cache
+**/.ruff_cache
+**/node_modules
+**/dist
+**/.env
+**/.env.*
+!openenv-polypharmacy/.env.example

.gitattributes

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+*.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

@@ -1,3 +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
-__pycache__/
+
+# --- 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}"]

README.MD

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+# PolypharmacyEnv
+
+Monorepo for an OpenEnv-compatible medication safety environment with:
+
+- a FastAPI backend (`backend/`)
+- a React frontend (`frontend/`)
+- data assets (`data/`)
+- utility scripts (`scripts/`)
+
+---
+
+## Repository Structure
+
+```text
+openenv-polypharmacy/
+  backend/
+    main.py                      # ASGI entrypoint (uvicorn target)
+    requirements.txt             # Backend dependencies
+    Dockerfile                   # Backend container
+    src/polypharmacy_env/        # Python package source
+      api/
+        app.py                   # FastAPI/OpenEnv app assembly
+        server.py                # Compatibility import wrapper
+        routes/agent.py          # /agent/suggest route
+      services/
+        groq_agent.py            # Groq-based action suggestion logic
+      env_core.py                # OpenEnv environment core
+      models.py                  # Action/observation/state models
+      data_loader.py             # CSV loading
+      ddi_simulator.py           # DDI and Beers lookups
+      rewards.py                 # Reward shaping
+      graders.py                 # Task graders
+      tasks.py                   # Task/episode selection
+      tests/                     # Backend tests
+  frontend/
+    src/                         # React UI code
+    package.json
+    Dockerfile                   # Frontend container
+  data/
+    lookups/                     # drug_metadata.csv, ddi_rules.csv, beers_criteria.csv
+    processed/                   # patients_polypharmacy.csv
+  scripts/
+    preprocess_data.py           # Synthetic data generation
+    dev_backend.sh               # Local backend run helper
+    dev_frontend.sh              # Local frontend run helper
+    run_validation.sh            # Tests + baseline validation
+  docker-compose.yml             # Full stack orchestration
+  openenv.yaml                   # OpenEnv manifest
+  inference.py                   # Optional CLI inference baseline
+  .env.example                   # Environment template
+```
+
+---
+
+## What It Does
+
+The environment simulates elderly polypharmacy review. Agent actions:
+
+- `query_ddi`
+- `propose_intervention`
+- `finish_review`
+
+Supported tasks:
+
+- `easy_screening`
+- `budgeted_screening`
+- `complex_tradeoff`
+
+---
+
+## Prerequisites
+
+- Python 3.10+
+- Node.js 18+ (or 20+ recommended)
+- npm
+- Docker + Docker Compose (optional, for containerized run)
+
+---
+
+## Environment Setup
+
+Create `.env`:
+
+```bash
+cp .env.example .env
+```
+
+Set values:
+
+- `GROQ_API_KEY=...` (required)
+- `GROQ_BASE_URL=https://api.groq.com/openai/v1` (recommended)
+- `GROQ_MODEL_NAME=llama-3.3-70b-versatile` (recommended)
+
+---
+
+## Local Run (Recommended During Development)
+
+### 1) Install dependencies
+
+Backend:
+
+```bash
+pip install -r backend/requirements.txt
+```
+
+Frontend:
+
+```bash
+cd frontend
+npm install
+cd ..
+```
+
+### 2) Generate/update synthetic data (if needed)
+
+```bash
+python scripts/preprocess_data.py
+```
+
+### 3) Start services in two terminals
+
+Terminal A:
+
+```bash
+./scripts/dev_backend.sh
+```
+
+Terminal B:
+
+```bash
+./scripts/dev_frontend.sh
+```
+
+### 4) Open app
+
+- Frontend: [http://localhost:5173](http://localhost:5173)
+- Backend health: [http://localhost:7860/health](http://localhost:7860/health)
+
+---
+
+## Docker Run
+
+Run both services:
+
+```bash
+docker compose up --build
+```
+
+Stop:
+
+```bash
+docker compose down
+```
+
+Ports:
+
+- backend: `7860`
+- frontend: `5173`
+
+---
+
+## Hugging Face Spaces Deployment (Docker)
+
+This repo now includes a **root `Dockerfile`** that builds frontend + backend into one container, so Spaces can host both API and UI together.
+
+### 1) Create a new Space
+
+- Go to [Hugging Face Spaces](https://huggingface.co/new-space)
+- Choose **Docker** SDK
+- Create the Space
+
+### 2) Add Space secrets/variables
+
+In Space Settings -> Variables and Secrets:
+
+- Secret: `GROQ_API_KEY`
+- Variable: `GROQ_BASE_URL=https://api.groq.com/openai/v1`
+- Variable: `GROQ_MODEL_NAME=llama-3.3-70b-versatile`
+
+### 3) Push this repository to the Space
+
+Commit and push all files, including root `Dockerfile`.
+
+### 4) Verify after build
+
+- Space root URL loads the React UI
+- `/health` returns healthy status
+- OpenEnv endpoints are available (`/reset`, `/step`, `/state`, `/schema`)
+
+Notes:
+
+- Container reads `PORT` (defaults to `7860`) which is Space-friendly.
+- Frontend static assets are served by FastAPI from `frontend/dist`.
+
+---
+
+## API Endpoints
+
+OpenEnv/health:
+
+- `POST /reset`
+- `POST /step`
+- `GET /state`
+- `GET /health`
+- `GET /schema`
+- `WS /ws` (stateful session)
+
+AI helper:
+
+- `POST /agent/suggest`
+
+---
+
+## Testing
+
+Run backend tests:
+
+```bash
+python -m pytest backend/src/polypharmacy_env/tests -v
+```
+
+Or run validation script:
+
+```bash
+./scripts/run_validation.sh
+```
+
+---
+
+## Notes
+
+- OpenEnv HTTP reset/step is stateless; multi-step episode continuity should use websocket (`/ws`).
+- The frontend uses websocket for episode continuity and HTTP for AI suggestion.
+- AI behavior includes rule-based guardrails to avoid repetitive low-value loops.
+
+---
+
+## Troubleshooting
+
+- `ModuleNotFoundError: polypharmacy_env`
+  - Start backend using `./scripts/dev_backend.sh` from repo root.
+- `/agent/suggest` fails
+  - Check `.env` keys and restart backend.
+- UI state looks stale
+  - Hard refresh browser and click `Reset Episode`.

openenv-polypharmacy/.dockerignore

@@ -0,0 +1,8 @@
+.git
+.gitignore
+**/__pycache__/
+**/.pytest_cache/
+**/.DS_Store
+.env
+frontend/node_modules
+frontend/dist

openenv-polypharmacy/.env.example

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+GROQ_API_KEY=your_groq_api_key_here
+GROQ_BASE_URL=https://api.groq.com/openai/v1
+GROQ_MODEL_NAME=llama-3.3-70b-versatile

openenv-polypharmacy/Dockerfile

@@ -1,30 +1,39 @@
+FROM node:20-alpine AS frontend-builder
+WORKDIR /app/frontend
+COPY frontend/package*.json ./
+RUN npm ci
+COPY frontend/ ./
+RUN npm run build
+
 FROM python:3.11-slim
 
-# System deps
 RUN apt-get update && \
     apt-get install -y --no-install-recommends build-essential curl && \
     rm -rf /var/lib/apt/lists/*
 
 WORKDIR /app
 
-# Install Python deps first (layer caching)
-COPY requirements.txt .
-RUN pip install --no-cache-dir -r requirements.txt
+COPY backend/requirements.txt /app/backend/requirements.txt
+RUN pip install --no-cache-dir -r /app/backend/requirements.txt
+
+COPY backend /app/backend
+COPY data /app/data
+COPY scripts /app/scripts
+COPY openenv.yaml /app/openenv.yaml
+COPY .env.example /app/.env.example
+COPY inference.py /app/inference.py
 
-# Copy project
-COPY . .
+COPY --from=frontend-builder /app/frontend/dist /app/frontend/dist
 
-# Generate data if not present
-RUN python3 scripts/preprocess_data.py
+RUN python3 /app/scripts/preprocess_data.py
 
-# Environment
 ENV PORT=7860
-ENV PYTHONPATH="/app/src:${PYTHONPATH}"
+ENV PYTHONPATH="/app/backend/src:${PYTHONPATH}"
 ENV PYTHONUNBUFFERED=1
 
 EXPOSE 7860
 
-HEALTHCHECK --interval=30s --timeout=3s --start-period=10s --retries=3 \
+HEALTHCHECK --interval=30s --timeout=3s --start-period=15s --retries=3 \
     CMD curl -f http://localhost:7860/health || exit 1
 
-CMD ["uvicorn", "polypharmacy_env.api.server:app", "--host", "0.0.0.0", "--port", "7860"]
+CMD ["sh", "-c", "uvicorn backend.main:app --host 0.0.0.0 --port ${PORT:-7860}"]

openenv-polypharmacy/PROMPT.md

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-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/README.md

@@ -1,184 +0,0 @@
-# PolypharmacyEnv
-
-An [OpenEnv](https://github.com/meta-pytorch/OpenEnv)-compliant reinforcement-learning environment that simulates **elderly polypharmacy medication review**. An RL agent acts as a clinical pharmacist assistant, identifying dangerous drug-drug interactions (DDIs), Beers-criteria violations, and proposing safe interventions.
-
----
-
-## Motivation
-
-Polypharmacy (concurrent use of multiple medications) is extremely common in elderly patients (age >= 65) and carries significant risks:
-
-- **Drug-drug interactions** can cause adverse events, hospitalisation, and death.
-- **Beers-criteria violations** flag medications that are inappropriate or require dose adjustments in older adults.
-- Stopping critical medications (anticoagulants, insulin) without proper substitution can be equally dangerous.
-
-This environment lets RL and LLM-based agents learn to **balance risk reduction against regimen stability**.
-
----
-
-## Action Space
-
-Each step, the agent sends a `PolypharmacyAction` with one of three action types:
-
-| `action_type` | Required fields | Description |
-|---|---|---|
-| `query_ddi` | `drug_id_1`, `drug_id_2` | Query the DDI database for an interaction between two drugs |
-| `propose_intervention` | `target_drug_id`, `intervention_type` | Propose changing a medication (`stop`, `dose_reduce`, `substitute`, `add_monitoring`) |
-| `finish_review` | — | End the review and trigger final grading |
-
-Optional fields: `proposed_new_drug_id`, `rationale`.
-
-## Observation Space
-
-`PolypharmacyObservation` includes:
-
-- **Patient demographics**: `age`, `sex`, `conditions`, `eGFR_category`, `liver_function_category`
-- **Medications**: list of `MedicationEntry` (drug_id, name, class, dose, high-risk flags, Beers flags)
-- **History**: `interaction_queries` (past DDI query results), `interventions` (past actions)
-- **Budgets**: `remaining_query_budget`, `remaining_intervention_budget`
-- **Reward signals**: `shaped_reward`, `done`
-
-## State
-
-`PolypharmacyState`: `episode_id`, `task_id`, `step_count`, `max_steps`, `num_query_actions`, `num_interventions`.
-
----
-
-## Tasks
-
-| Task ID | Difficulty | Drugs | Query Budget | Intervention Budget | Max Steps | Description |
-|---|---|---|---|---|---|---|
-| `easy_screening` | Easy | 3-5 | 4 | 2 | 10 | One severe DDI, simple resolution |
-| `budgeted_screening` | Medium | 6-10 | 8 | 3 | 20 | Multiple DDIs + Beers issues, limited budgets |
-| `complex_tradeoff` | Hard | 10-15 | 12 | 5 | 30 | Critical drugs, trade-off between risk and regimen stability |
-
----
-
-## Reward Structure
-
-**Per-step shaped rewards:**
-
-| Event | Reward |
-|---|---|
-| DDI query | -0.01 (cost) + 0.03 bonus if severe DDI discovered |
-| Successful intervention | +(previous_risk - new_risk) - 0.02 cost |
-| Invalid action | -0.10 penalty |
-| Timeout (max steps exceeded) | -0.20 penalty |
-| `finish_review` | + grader score (0.0 to 1.0) |
-
-**Terminal grader scoring:**
-- **Easy**: 50% risk reduction + 50% targeted intervention flag
-- **Medium**: 50% risk reduction + 30% intervention precision + 20% query efficiency
-- **Hard**: risk reduction - regimen disruption penalty - critical drug penalty
-
----
-
-## Setup & Usage
-
-### Install dependencies
-
-```bash
-pip install -r requirements.txt
-```
-
-### Generate synthetic data
-
-```bash
-python3 scripts/preprocess_data.py
-```
-
-### Run the API server locally
-
-```bash
-PYTHONPATH=src uvicorn polypharmacy_env.api.server:app --host 0.0.0.0 --port 7860
-```
-
-### Run the heuristic baseline
-
-```bash
-PYTHONPATH=src python3 -m polypharmacy_env.baselines.heuristic_agent
-```
-
-### Run tests
-
-```bash
-PYTHONPATH=src python3 -m pytest src/polypharmacy_env/tests/ -v
-```
-
-### Run `inference.py` (LLM baseline)
-
-```bash
-# Start the server first, then in another terminal:
-export OPENAI_API_KEY="sk-..."
-export MODEL_NAME="gpt-4.1"
-export POLYPHARMACY_ENV_URL="http://localhost:7860"
-python3 inference.py
-```
-
-### Docker
-
-```bash
-docker build -t polypharmacy-env .
-docker run -p 7860:7860 polypharmacy-env
-```
-
----
-
-## Hugging Face Space
-
-This repo is ready for deployment as a HF Space:
-
-- **Space type**: `docker`
-- **Tag**: `openenv`
-- The container listens on port 7860 and exposes `/reset`, `/step`, `/state`, `/health`.
-
----
-
-## Baseline Scores
-
-### Heuristic Agent (deterministic, rule-based)
-
-| Task | Avg Score | Avg Reward |
-|---|---|---|
-| `easy_screening` | ~0.96 | ~1.30 |
-| `budgeted_screening` | ~0.48 | ~0.45 |
-| `complex_tradeoff` | ~0.24 | ~0.11 |
-
-*(Scores vary by seed; run `scripts/run_validation.sh` for exact numbers.)*
-
----
-
-## Project Structure
-
-```
-openenv-polypharmacy/
-  openenv.yaml              # OpenEnv manifest
-  Dockerfile                # Container image
-  inference.py              # LLM baseline script
-  requirements.txt
-  pyproject.toml
-  src/polypharmacy_env/
-    config.py               # Constants, task configs
-    models.py               # Pydantic action/observation/state models
-    env_core.py             # PolypharmacyEnv implementation
-    tasks.py                # Task selection utilities
-    graders.py              # Deterministic graders (3 difficulty levels)
-    rewards.py              # Reward shaping logic
-    data_loader.py          # CSV data loading
-    ddi_simulator.py        # Drug interaction lookup engine
-    api/
-      server.py             # FastAPI HTTP server
-      schemas.py            # Request/response schemas
-    baselines/
-      heuristic_agent.py    # Rule-based baseline
-      random_agent.py       # Random baseline
-    tests/
-      test_env_core.py
-      test_api.py
-  data/
-    lookups/                # Drug metadata, DDI rules, Beers criteria CSVs
-    processed/              # Synthetic patient episodes
-  scripts/
-    preprocess_data.py      # Synthetic data generator
-    run_validation.sh       # Run tests + baseline
-```

openenv-polypharmacy/backend/Dockerfile

@@ -0,0 +1,28 @@
+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 backend/requirements.txt /app/backend/requirements.txt
+RUN pip install --no-cache-dir -r /app/backend/requirements.txt
+
+COPY backend /app/backend
+COPY data /app/data
+COPY scripts /app/scripts
+COPY .env.example /app/.env.example
+
+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=10s --retries=3 \
+    CMD curl -f http://localhost:7860/health || exit 1
+
+CMD ["uvicorn", "backend.main:app", "--host", "0.0.0.0", "--port", "7860"]

openenv-polypharmacy/backend/__init__.py

@@ -0,0 +1 @@
+"""Backend entrypoint package for monorepo structure."""

openenv-polypharmacy/backend/main.py

@@ -0,0 +1,15 @@
+"""ASGI entrypoint for backend service in monorepo layout."""
+
+from __future__ import annotations
+
+import sys
+from pathlib import Path
+
+BACKEND_DIR = Path(__file__).resolve().parent
+SRC = BACKEND_DIR / "src"
+if str(SRC) not in sys.path:
+    sys.path.insert(0, str(SRC))
+
+from polypharmacy_env.api.app import app  # noqa: E402
+
+__all__ = ["app"]

openenv-polypharmacy/{src/polypharmacy_env.egg-info/requires.txt → backend/requirements.txt}

@@ -2,10 +2,8 @@ fastapi>=0.104.0
 uvicorn>=0.24.0
 pydantic>=2.0.0
 requests>=2.31.0
+httpx>=0.25.0
+openenv-core>=0.2.0
 openai>=1.0.0
-
-[dev]
+python-dotenv>=1.0.0
 pytest>=7.0.0
-httpx>=0.25.0
-black
-isort

openenv-polypharmacy/backend/src/polypharmacy_env/api/app.py

@@ -0,0 +1,63 @@
+"""FastAPI app factory for PolypharmacyEnv."""
+
+from __future__ import annotations
+
+from pathlib import Path
+
+from dotenv import load_dotenv
+from fastapi import HTTPException
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.staticfiles import StaticFiles
+from openenv.core.env_server.http_server import create_app
+from starlette.responses import FileResponse
+
+from ..env_core import PolypharmacyEnv
+from ..models import PolypharmacyAction, PolypharmacyObservation
+from .routes.agent import router as agent_router
+
+load_dotenv()
+
+
+class SPAStaticFiles(StaticFiles):
+    """Serve SPA index for unknown frontend routes."""
+
+    async def get_response(self, path: str, scope):
+        response = await super().get_response(path, scope)
+        if response.status_code != 404:
+            return response
+        index_path = Path(self.directory) / "index.html"
+        if index_path.exists():
+            return FileResponse(index_path)
+        raise HTTPException(status_code=404, detail="Not Found")
+
+
+def create_polypharmacy_app():
+    app = create_app(
+        PolypharmacyEnv,
+        PolypharmacyAction,
+        PolypharmacyObservation,
+        env_name="polypharmacy_env",
+    )
+
+    app.add_middleware(
+        CORSMiddleware,
+        allow_origins=[
+            "http://localhost:5173",
+            "http://127.0.0.1:5173",
+        ],
+        allow_credentials=True,
+        allow_methods=["*"],
+        allow_headers=["*"],
+    )
+    app.include_router(agent_router)
+
+    # In Docker Space deployment, serve built frontend from same container.
+    project_root = Path(__file__).resolve().parents[4]
+    frontend_dist = project_root / "frontend" / "dist"
+    if frontend_dist.exists():
+        app.mount("/", SPAStaticFiles(directory=frontend_dist, html=True), name="frontend")
+
+    return app
+
+
+app = create_polypharmacy_app()

openenv-polypharmacy/backend/src/polypharmacy_env/api/routes/__init__.py

@@ -0,0 +1 @@
+"""API route modules."""

openenv-polypharmacy/backend/src/polypharmacy_env/api/routes/agent.py

@@ -0,0 +1,35 @@
+"""Agent suggestion API routes."""
+
+from __future__ import annotations
+
+from fastapi import APIRouter, HTTPException
+from pydantic import BaseModel, Field
+
+from ...models import PolypharmacyAction, PolypharmacyObservation
+from ...services.groq_agent import suggest_action_from_observation
+
+router = APIRouter(prefix="/agent", tags=["agent"])
+
+
+class AgentSuggestRequest(BaseModel):
+    observation: PolypharmacyObservation
+    model_name: str | None = None
+
+
+class AgentSuggestResponse(BaseModel):
+    action: PolypharmacyAction
+    source: str = Field(default="groq")
+
+
+@router.post("/suggest", response_model=AgentSuggestResponse)
+def suggest_agent_action(payload: AgentSuggestRequest) -> AgentSuggestResponse:
+    """Return a model-suggested action for the current observation."""
+    try:
+        action = suggest_action_from_observation(
+            payload.observation, model_name=payload.model_name
+        )
+    except ValueError as exc:
+        raise HTTPException(status_code=400, detail=str(exc)) from exc
+    except Exception as exc:
+        raise HTTPException(status_code=500, detail=f"Model call failed: {exc}") from exc
+    return AgentSuggestResponse(action=action)

openenv-polypharmacy/backend/src/polypharmacy_env/api/server.py

@@ -0,0 +1,6 @@
+"""Backward-compatible app import path.
+
+Use `polypharmacy_env.api.app:app` for the main app module.
+"""
+
+from .app import app

openenv-polypharmacy/{src → backend/src}/polypharmacy_env/config.py

@@ -7,7 +7,7 @@ from pathlib import Path
 from typing import Dict
 
 # ── Paths ────────────────────────────────────────────────────────────────────
-PROJECT_ROOT = Path(__file__).resolve().parents[2]  # openenv-polypharmacy/
+PROJECT_ROOT = Path(__file__).resolve().parents[3]  # openenv-polypharmacy/
 DATA_DIR = PROJECT_ROOT / "data"
 LOOKUPS_DIR = DATA_DIR / "lookups"
 PROCESSED_DIR = DATA_DIR / "processed"

openenv-polypharmacy/backend/src/polypharmacy_env/services/__init__.py

@@ -0,0 +1 @@
+"""Service layer for external integrations."""

openenv-polypharmacy/backend/src/polypharmacy_env/services/groq_agent.py

@@ -0,0 +1,246 @@
+"""Groq-powered action suggester for PolypharmacyEnv."""
+
+from __future__ import annotations
+
+import json
+import os
+from typing import Any
+
+from openai import OpenAI
+
+from ..models import PolypharmacyAction, PolypharmacyObservation
+
+DEFAULT_MODEL = "llama-3.1-8b-instant"
+FALLBACK_MODELS = [
+    "llama-3.1-8b-instant",
+    "llama-3.3-70b-versatile",
+    "gemma2-9b-it",
+]
+CRITICAL_DRUG_IDS = {"DRUG_WARFARIN", "DRUG_INSULIN_GLARGINE", "DRUG_DIGOXIN"}
+
+SYSTEM_PROMPT = """You are a clinical medication safety assistant.
+Return exactly one JSON object describing the next action.
+Allowed output schema:
+{
+  "action_type": "query_ddi" | "propose_intervention" | "finish_review",
+  "drug_id_1": "optional",
+  "drug_id_2": "optional",
+  "target_drug_id": "optional",
+  "intervention_type": "stop|dose_reduce|substitute|add_monitoring|none",
+  "proposed_new_drug_id": "optional",
+  "rationale": "optional"
+}
+No markdown fences. No extra text.
+Do NOT use finish_review early. First, gather evidence with query_ddi and/or
+perform at least one meaningful intervention when needed.
+"""
+
+
+def _obs_to_prompt(obs: PolypharmacyObservation) -> str:
+    meds = ", ".join(m.drug_id for m in obs.current_medications)
+    conds = ", ".join(obs.conditions)
+    return (
+        f"Task: {obs.task_id}\n"
+        f"Age: {obs.age}, sex: {obs.sex}\n"
+        f"Conditions: {conds}\n"
+        f"Medications: {meds}\n"
+        f"Query budget: {obs.remaining_query_budget}\n"
+        f"Intervention budget: {obs.remaining_intervention_budget}\n"
+        f"Step index: {obs.step_index}\n"
+        "Choose the single safest, most useful next action."
+    )
+
+
+def _parse_action(text: str) -> PolypharmacyAction:
+    raw = text.strip()
+    if raw.startswith("```"):
+        raw = raw.split("\n", 1)[-1]
+    if raw.endswith("```"):
+        raw = raw.rsplit("```", 1)[0]
+    raw = raw.strip()
+    payload: dict[str, Any] = json.loads(raw)
+    return PolypharmacyAction.model_validate(payload)
+
+
+def _fallback_query_action(obs: PolypharmacyObservation) -> PolypharmacyAction:
+    meds = [m.drug_id for m in obs.current_medications]
+    if len(meds) >= 2 and obs.remaining_query_budget > 0:
+        return PolypharmacyAction(
+            action_type="query_ddi",
+            drug_id_1=meds[0],
+            drug_id_2=meds[1],
+        )
+    return PolypharmacyAction(action_type="finish_review")
+
+
+def _norm_pair(a: str, b: str) -> tuple[str, str]:
+    return (a, b) if a < b else (b, a)
+
+
+def _pick_unseen_query_pair(obs: PolypharmacyObservation) -> tuple[str, str] | None:
+    meds = [m.drug_id for m in obs.current_medications]
+    if len(meds) < 2 or obs.remaining_query_budget <= 0:
+        return None
+
+    seen = {
+        _norm_pair(q.drug_id_1, q.drug_id_2)
+        for q in obs.interaction_queries
+    }
+    # Prioritize pairs containing high-risk drugs.
+    high_risk = [m.drug_id for m in obs.current_medications if m.is_high_risk_elderly]
+    ordered = high_risk + [m for m in meds if m not in set(high_risk)]
+
+    for i in range(len(ordered)):
+        for j in range(i + 1, len(ordered)):
+            p = _norm_pair(ordered[i], ordered[j])
+            if p not in seen:
+                return p
+    return None
+
+
+def _pick_intervention_target(obs: PolypharmacyObservation) -> str | None:
+    if obs.remaining_intervention_budget <= 0:
+        return None
+    med_set = {m.drug_id for m in obs.current_medications}
+
+    # Use latest discovered severe/moderate query as intervention target.
+    for q in reversed(obs.interaction_queries):
+        if q.severity in ("severe", "moderate"):
+            m1 = next((m for m in obs.current_medications if m.drug_id == q.drug_id_1), None)
+            m2 = next((m for m in obs.current_medications if m.drug_id == q.drug_id_2), None)
+            candidates = [m for m in (m1, m2) if m is not None]
+            if not candidates:
+                continue
+            # Prefer non-critical risky drugs first.
+            candidates.sort(
+                key=lambda m: (
+                    m.drug_id in CRITICAL_DRUG_IDS,
+                    0 if any("avoid" in f for f in m.beers_flags) else 1,
+                    0 if m.is_high_risk_elderly else 1,
+                )
+            )
+            return candidates[0].drug_id
+
+    # Fallback: if no severe/moderate discovered, still intervene on obviously
+    # risky medications (Beers/high-risk flags) when budgets permit.
+    risky = sorted(
+        obs.current_medications,
+        key=lambda m: (
+            0 if any("avoid" in f for f in m.beers_flags) else 1,
+            0 if m.is_high_risk_elderly else 1,
+            1 if m.drug_id in CRITICAL_DRUG_IDS else 0,
+        ),
+    )
+    for med in risky:
+        if any("avoid" in f for f in med.beers_flags) or med.is_high_risk_elderly:
+            return med.drug_id
+    return None
+
+
+def _rule_based_action(obs: PolypharmacyObservation) -> PolypharmacyAction | None:
+    # If we already discovered significant risk, intervene before more querying.
+    target = _pick_intervention_target(obs)
+    if target and (
+        obs.step_index >= 1
+        and (
+            obs.remaining_query_budget <= 2
+            or len(obs.interaction_queries) >= 4
+            or any(q.severity in ("severe", "moderate") for q in obs.interaction_queries)
+        )
+    ):
+        intervention = "stop"
+        rationale = "Remove likely contributor to discovered interaction risk"
+        if target in CRITICAL_DRUG_IDS:
+            # Avoid blunt stop for critical meds.
+            intervention = "dose_reduce"
+            rationale = "Critical medication: prefer dose reduction over abrupt stop"
+        return PolypharmacyAction(
+            action_type="propose_intervention",
+            target_drug_id=target,
+            intervention_type=intervention,
+            rationale=rationale,
+        )
+
+    pair = _pick_unseen_query_pair(obs)
+    if pair:
+        return PolypharmacyAction(
+            action_type="query_ddi",
+            drug_id_1=pair[0],
+            drug_id_2=pair[1],
+        )
+
+    if obs.remaining_intervention_budget > 0:
+        # Final fallback before finish: at least one safety action.
+        target = _pick_intervention_target(obs)
+        if target:
+            return PolypharmacyAction(
+                action_type="propose_intervention",
+                target_drug_id=target,
+                intervention_type="dose_reduce"
+                if target in CRITICAL_DRUG_IDS
+                else "stop",
+                rationale="Fallback intervention when query options are exhausted",
+            )
+
+    if obs.step_index >= 3:
+        return PolypharmacyAction(action_type="finish_review")
+    return None
+
+
+def _postprocess_action(
+    obs: PolypharmacyObservation, action: PolypharmacyAction
+) -> PolypharmacyAction:
+    # First apply deterministic guardrails to avoid repetitive loops.
+    ruled = _rule_based_action(obs)
+    if ruled is not None:
+        return ruled
+
+    # Guardrail: prevent useless immediate finish actions.
+    if action.action_type == "finish_review":
+        if obs.step_index < 2 and obs.remaining_query_budget > 0:
+            return _fallback_query_action(obs)
+        if len(obs.interaction_queries) == 0 and obs.remaining_query_budget > 0:
+            return _fallback_query_action(obs)
+    return action
+
+
+def suggest_action_from_observation(
+    observation: PolypharmacyObservation,
+    model_name: str | None = None,
+) -> PolypharmacyAction:
+    """Use Groq chat completions to suggest a valid action."""
+    api_key = os.getenv("GROQ_API_KEY", "").strip()
+    if not api_key:
+        raise ValueError("GROQ_API_KEY is missing. Add it to your .env file.")
+
+    base_url = os.getenv("GROQ_BASE_URL", "https://api.groq.com/openai/v1").strip()
+    model = (model_name or os.getenv("GROQ_MODEL_NAME", DEFAULT_MODEL)).strip()
+    client = OpenAI(api_key=api_key, base_url=base_url)
+
+    user_prompt = _obs_to_prompt(observation)
+    tried: list[tuple[str, str]] = []
+    candidates: list[str] = [model] + [m for m in FALLBACK_MODELS if m != model]
+
+    for candidate in candidates:
+        try:
+            resp = client.chat.completions.create(
+                model=candidate,
+                messages=[
+                    {"role": "system", "content": SYSTEM_PROMPT},
+                    {"role": "user", "content": user_prompt},
+                ],
+                temperature=0.2,
+                max_tokens=220,
+            )
+            generated = (resp.choices[0].message.content or "").strip()
+            parsed = _parse_action(generated)
+            return _postprocess_action(observation, parsed)
+        except Exception as exc:
+            tried.append((candidate, str(exc)))
+
+    tried_txt = " | ".join(f"{m}: {err}" for m, err in tried)
+    raise ValueError(
+        "No Groq model worked. Try one of: "
+        "llama-3.3-70b-versatile, llama-3.1-8b-instant, gemma2-9b-it. "
+        f"Errors: {tried_txt}"
+    )

openenv-polypharmacy/data/processed/patients_polypharmacy.csv

@@ -1,44 +1,44 @@
 episode_id,age,sex,conditions,eGFR_category,liver_function_category,medication_ids,baseline_risk_score,difficulty
-EP_0001,72,F,HTN,moderate,normal,DRUG_WARFARIN;DRUG_FUROSEMIDE;DRUG_LISINOPRIL;DRUG_AMLODIPINE;DRUG_NAPROXEN,0.264,easy
+EP_0001,72,F,HTN,moderate,normal,DRUG_AMLODIPINE;DRUG_LISINOPRIL;DRUG_DIGOXIN;DRUG_FUROSEMIDE;DRUG_AMIODARONE,0.25,easy
 EP_0002,67,M,OA;COPD;neuropathy,normal,normal,DRUG_IBUPROFEN;DRUG_TRAMADOL;DRUG_AMITRIPTYLINE,0.2833,easy
-EP_0003,73,F,HTN;HF,normal,normal,DRUG_IBUPROFEN;DRUG_WARFARIN;DRUG_FUROSEMIDE,0.2933,easy
-EP_0004,74,M,CKD,mild,impaired,DRUG_TRAMADOL;DRUG_AMLODIPINE;DRUG_DIAZEPAM,0.3067,easy
+EP_0003,73,F,HTN;HF,normal,normal,DRUG_FUROSEMIDE;DRUG_FLUOXETINE;DRUG_TRAMADOL,0.2733,easy
+EP_0004,74,M,CKD,mild,impaired,DRUG_AMITRIPTYLINE;DRUG_AMLODIPINE;DRUG_TRAMADOL,0.2833,easy
 EP_0005,76,F,OA;neuropathy;CKD,mild,normal,DRUG_IBUPROFEN;DRUG_GABAPENTIN;DRUG_TRAMADOL;DRUG_NAPROXEN;DRUG_AMITRIPTYLINE,0.17,easy
-EP_0006,74,M,HTN;OA,normal,impaired,DRUG_IBUPROFEN;DRUG_WARFARIN;DRUG_LISINOPRIL;DRUG_FUROSEMIDE;DRUG_NAPROXEN,0.44,easy
-EP_0007,90,M,BPH;OA,moderate,normal,DRUG_DIGOXIN;DRUG_TAMSULOSIN;DRUG_GABAPENTIN;DRUG_NAPROXEN;DRUG_AMIODARONE,0.16,easy
+EP_0006,74,M,HTN;OA,normal,impaired,DRUG_IBUPROFEN;DRUG_LISINOPRIL;DRUG_DIGOXIN;DRUG_FUROSEMIDE;DRUG_AMIODARONE,0.25,easy
+EP_0007,90,M,BPH;OA,moderate,normal,DRUG_WARFARIN;DRUG_NAPROXEN;DRUG_TAMSULOSIN;DRUG_GABAPENTIN,0.225,easy
 EP_0008,77,F,CKD;OA;depression,mild,normal,DRUG_AMITRIPTYLINE;DRUG_IBUPROFEN;DRUG_SERTRALINE;DRUG_TRAMADOL;DRUG_FUROSEMIDE,0.17,easy
-EP_0009,67,M,COPD;GERD;BPH,mild,normal,DRUG_TRAMADOL;DRUG_FLUOXETINE;DRUG_OMEPRAZOLE;DRUG_TAMSULOSIN,0.205,easy
-EP_0010,75,M,dementia;HTN;depression,normal,impaired,DRUG_TRAMADOL;DRUG_DIAZEPAM;DRUG_SERTRALINE;DRUG_AMITRIPTYLINE,0.4425,easy
-EP_0011,83,F,AF,moderate,normal,DRUG_TRAMADOL;DRUG_WARFARIN;DRUG_DIGOXIN;DRUG_ALPRAZOLAM,0.2275,easy
-EP_0012,71,F,HTN;GERD;depression,normal,normal,DRUG_LISINOPRIL;DRUG_FLUOXETINE;DRUG_APIXABAN;DRUG_AMLODIPINE;DRUG_NAPROXEN,0.254,easy
-EP_0013,70,F,HF;HTN;AF,mild,normal,DRUG_TRAMADOL;DRUG_FUROSEMIDE;DRUG_ALPRAZOLAM,0.3033,easy
+EP_0009,67,M,COPD;GERD;BPH,mild,normal,DRUG_WARFARIN;DRUG_IBUPROFEN;DRUG_OMEPRAZOLE;DRUG_TAMSULOSIN,0.22,easy
+EP_0010,75,M,dementia;HTN;depression,normal,impaired,DRUG_TRAMADOL;DRUG_SERTRALINE;DRUG_AMITRIPTYLINE,0.2833,easy
+EP_0011,83,F,AF,moderate,normal,DRUG_ALPRAZOLAM;DRUG_WARFARIN;DRUG_DIGOXIN;DRUG_TRAMADOL,0.2275,easy
+EP_0012,71,F,HTN;GERD;depression,normal,normal,DRUG_DIAZEPAM;DRUG_AMLODIPINE;DRUG_FLUOXETINE;DRUG_LISINOPRIL;DRUG_TRAMADOL,0.348,easy
+EP_0013,70,F,HF;HTN;AF,mild,normal,DRUG_ALPRAZOLAM;DRUG_FUROSEMIDE;DRUG_TRAMADOL,0.3033,easy
 EP_0014,82,F,dementia,normal,normal,DRUG_DONEPEZIL;DRUG_NAPROXEN;DRUG_APIXABAN;DRUG_SPIRONOLACTONE;DRUG_FUROSEMIDE,0.17,easy
 EP_0015,84,F,dementia;neuropathy,normal,normal,DRUG_DONEPEZIL;DRUG_GABAPENTIN;DRUG_AMITRIPTYLINE;DRUG_CELECOXIB;DRUG_TRAMADOL,0.17,easy
-EP_0016,83,M,HTN,normal,normal,DRUG_TRAMADOL;DRUG_METOPROLOL;DRUG_ALPRAZOLAM,0.3033,easy
-EP_0017,83,F,CKD,severe,normal,DRUG_APIXABAN;DRUG_AMLODIPINE;DRUG_NAPROXEN,0.2833,easy
-EP_0018,70,F,CKD;HF;HTN,mild,normal,DRUG_SPIRONOLACTONE;DRUG_ALPRAZOLAM;DRUG_TRAMADOL;DRUG_AMLODIPINE;DRUG_METOPROLOL,0.182,easy
-EP_0019,84,M,DM;depression,normal,normal,DRUG_GLIPIZIDE;DRUG_FLUOXETINE;DRUG_TRAMADOL;DRUG_INSULIN_GLARGINE;DRUG_DIAZEPAM,0.448,easy
-EP_0020,90,F,neuropathy;BPH;AF,normal,normal,DRUG_WARFARIN;DRUG_NAPROXEN;DRUG_TAMSULOSIN,0.3,easy
-EP_0021,87,M,HTN;BPH;HF,normal,normal,DRUG_TRAMADOL;DRUG_AMITRIPTYLINE;DRUG_AMLODIPINE;DRUG_SPIRONOLACTONE,0.2125,easy
-EP_0022,90,M,AF;GERD;DM,normal,impaired,DRUG_APIXABAN;DRUG_NAPROXEN;DRUG_METOPROLOL;DRUG_OMEPRAZOLE,0.2125,easy
-EP_0023,90,F,HF,normal,normal,DRUG_APIXABAN;DRUG_NAPROXEN;DRUG_METOPROLOL,0.2833,easy
+EP_0016,83,M,HTN,normal,normal,DRUG_ALPRAZOLAM;DRUG_METOPROLOL;DRUG_TRAMADOL,0.3033,easy
+EP_0017,83,F,CKD,severe,normal,DRUG_DIAZEPAM;DRUG_AMLODIPINE;DRUG_TRAMADOL,0.3067,easy
+EP_0018,70,F,CKD;HF;HTN,mild,normal,DRUG_SPIRONOLACTONE;DRUG_AMLODIPINE;DRUG_ALPRAZOLAM;DRUG_METOPROLOL;DRUG_TRAMADOL,0.182,easy
+EP_0019,84,M,DM;depression,normal,normal,DRUG_INSULIN_GLARGINE;DRUG_FLUOXETINE;DRUG_AMITRIPTYLINE;DRUG_GLIPIZIDE;DRUG_TRAMADOL,0.434,easy
+EP_0020,90,F,neuropathy;BPH;AF,normal,normal,DRUG_WARFARIN;DRUG_DIGOXIN;DRUG_AMIODARONE;DRUG_TAMSULOSIN,0.2,easy
+EP_0021,87,M,HTN;BPH;HF,normal,normal,DRUG_SPIRONOLACTONE;DRUG_APIXABAN;DRUG_NAPROXEN;DRUG_AMLODIPINE,0.2125,easy
+EP_0022,90,M,AF;GERD;DM,normal,impaired,DRUG_OMEPRAZOLE;DRUG_DIAZEPAM;DRUG_METOPROLOL;DRUG_TRAMADOL,0.23,easy
+EP_0023,90,F,HF,normal,normal,DRUG_DIAZEPAM;DRUG_METOPROLOL;DRUG_TRAMADOL,0.3067,easy
 EP_0024,71,F,OA,mild,normal,DRUG_IBUPROFEN;DRUG_GABAPENTIN;DRUG_TRAMADOL;DRUG_NAPROXEN;DRUG_APIXABAN,0.17,easy
-EP_0025,71,M,COPD;AF;neuropathy,mild,normal,DRUG_GABAPENTIN;DRUG_WARFARIN;DRUG_NAPROXEN,0.3,easy
-EP_0026,88,M,GERD;dementia,severe,normal,DRUG_TRAMADOL;DRUG_AMITRIPTYLINE;DRUG_DONEPEZIL;DRUG_OMEPRAZOLE,0.2125,easy
+EP_0025,71,M,COPD;AF;neuropathy,mild,normal,DRUG_WARFARIN;DRUG_DIGOXIN;DRUG_AMIODARONE;DRUG_GABAPENTIN,0.2,easy
+EP_0026,88,M,GERD;dementia,severe,normal,DRUG_DONEPEZIL;DRUG_OMEPRAZOLE;DRUG_APIXABAN;DRUG_NAPROXEN,0.2125,easy
 EP_0027,76,M,AF,normal,normal,DRUG_DIGOXIN;DRUG_METOPROLOL;DRUG_WARFARIN;DRUG_APIXABAN;DRUG_NAPROXEN,0.43,easy
 EP_0028,73,F,CKD,moderate,normal,DRUG_AMLODIPINE;DRUG_FUROSEMIDE;DRUG_METFORMIN;DRUG_AMITRIPTYLINE;DRUG_TRAMADOL,0.17,easy
-EP_0029,70,F,CKD;OA,mild,normal,DRUG_IBUPROFEN;DRUG_TRAMADOL;DRUG_GABAPENTIN;DRUG_AMLODIPINE;DRUG_DIAZEPAM,0.184,easy
-EP_0030,87,F,dementia;HF;depression,normal,normal,DRUG_WARFARIN;DRUG_DONEPEZIL;DRUG_FLUOXETINE;DRUG_FUROSEMIDE;DRUG_NAPROXEN,0.27,easy
-EP_0031,69,M,HF,severe,normal,DRUG_WARFARIN;DRUG_SPIRONOLACTONE;DRUG_LISINOPRIL;DRUG_FUROSEMIDE;DRUG_NAPROXEN,0.36,easy
+EP_0029,70,F,CKD;OA,mild,normal,DRUG_IBUPROFEN;DRUG_AMLODIPINE;DRUG_AMITRIPTYLINE;DRUG_GABAPENTIN;DRUG_TRAMADOL,0.17,easy
+EP_0030,87,F,dementia;HF;depression,normal,normal,DRUG_DIGOXIN;DRUG_FLUOXETINE;DRUG_DONEPEZIL;DRUG_FUROSEMIDE;DRUG_AMIODARONE,0.25,easy
+EP_0031,69,M,HF,severe,normal,DRUG_SPIRONOLACTONE;DRUG_LISINOPRIL;DRUG_DIGOXIN;DRUG_FUROSEMIDE;DRUG_AMIODARONE,0.426,easy
 EP_0032,89,F,neuropathy,mild,normal,DRUG_AMITRIPTYLINE;DRUG_GABAPENTIN;DRUG_PREDNISONE;DRUG_TRAMADOL,0.2125,easy
 EP_0033,68,F,dementia,mild,impaired,DRUG_DONEPEZIL;DRUG_OMEPRAZOLE;DRUG_SPIRONOLACTONE;DRUG_TRAMADOL;DRUG_ALPRAZOLAM,0.182,easy
-EP_0034,84,F,CKD;HF;HTN,moderate,normal,DRUG_HYDROCHLOROTHIAZIDE;DRUG_DIGOXIN;DRUG_AMIODARONE,0.2667,easy
-EP_0035,74,M,HTN;DM,normal,impaired,DRUG_IBUPROFEN;DRUG_GLIPIZIDE;DRUG_WARFARIN;DRUG_HYDROCHLOROTHIAZIDE;DRUG_METOPROLOL,0.176,easy
-EP_0036,80,F,DM;neuropathy;HTN,severe,normal,DRUG_WARFARIN;DRUG_AMLODIPINE;DRUG_AMITRIPTYLINE;DRUG_NAPROXEN,0.225,easy
-EP_0037,78,M,HF,normal,normal,DRUG_TRAMADOL;DRUG_FUROSEMIDE;DRUG_DIAZEPAM;DRUG_LISINOPRIL,0.23,easy
-EP_0038,89,F,HTN;AF,moderate,normal,DRUG_TRAMADOL;DRUG_FUROSEMIDE;DRUG_DIAZEPAM,0.3067,easy
+EP_0034,84,F,CKD;HF;HTN,moderate,normal,DRUG_WARFARIN;DRUG_DIGOXIN;DRUG_NAPROXEN;DRUG_HYDROCHLOROTHIAZIDE,0.225,easy
+EP_0035,74,M,HTN;DM,normal,impaired,DRUG_FLUOXETINE;DRUG_HYDROCHLOROTHIAZIDE;DRUG_GLIPIZIDE;DRUG_METOPROLOL;DRUG_TRAMADOL,0.164,easy
+EP_0036,80,F,DM;neuropathy;HTN,severe,normal,DRUG_DIGOXIN;DRUG_AMLODIPINE;DRUG_AMIODARONE;DRUG_AMITRIPTYLINE,0.2,easy
+EP_0037,78,M,HF,normal,normal,DRUG_LISINOPRIL;DRUG_AMITRIPTYLINE;DRUG_FUROSEMIDE;DRUG_TRAMADOL,0.2125,easy
+EP_0038,89,F,HTN;AF,moderate,normal,DRUG_AMITRIPTYLINE;DRUG_FUROSEMIDE;DRUG_TRAMADOL,0.2833,easy
 EP_0039,78,F,OA;depression,moderate,normal,DRUG_GABAPENTIN;DRUG_FLUOXETINE;DRUG_TRAMADOL;DRUG_SERTRALINE,0.205,easy
-EP_0040,72,F,neuropathy;COPD;BPH,normal,normal,DRUG_TRAMADOL;DRUG_ALPRAZOLAM;DRUG_AMITRIPTYLINE;DRUG_TAMSULOSIN,0.44,easy
+EP_0040,72,F,neuropathy;COPD;BPH,normal,normal,DRUG_ALPRAZOLAM;DRUG_AMITRIPTYLINE;DRUG_TRAMADOL;DRUG_TAMSULOSIN,0.44,easy
 EP_0041,89,F,AF;BPH;DM;HF;HTN,mild,normal,DRUG_GLIPIZIDE;DRUG_DIGOXIN;DRUG_METOPROLOL;DRUG_WARFARIN;DRUG_METFORMIN;DRUG_AMLODIPINE;DRUG_INSULIN_GLARGINE;DRUG_HYDROCHLOROTHIAZIDE;DRUG_APIXABAN,0.1,medium
 EP_0042,66,F,HTN;AF;CKD,moderate,normal,DRUG_METOPROLOL;DRUG_AMLODIPINE;DRUG_LISINOPRIL;DRUG_DIGOXIN;DRUG_FUROSEMIDE;DRUG_WARFARIN;DRUG_APIXABAN;DRUG_HYDROCHLOROTHIAZIDE;DRUG_IBUPROFEN;DRUG_SERTRALINE,0.173,medium
 EP_0043,70,F,OA;HTN;dementia,moderate,normal,DRUG_TRAMADOL;DRUG_HYDROCHLOROTHIAZIDE;DRUG_GABAPENTIN;DRUG_IBUPROFEN;DRUG_DONEPEZIL;DRUG_FUROSEMIDE;DRUG_NAPROXEN;DRUG_LISINOPRIL;DRUG_METOPROLOL,0.0467,medium

openenv-polypharmacy/docker-compose.yml

@@ -0,0 +1,35 @@
+version: "3.9"
+
+services:
+  backend:
+    build:
+      context: .
+      dockerfile: backend/Dockerfile
+    container_name: polypharmacy-backend
+    env_file:
+      - .env
+    ports:
+      - "7860:7860"
+    volumes:
+      - ./backend/src:/app/backend/src
+      - ./data:/app/data
+      - ./scripts:/app/scripts
+      - ./backend:/app/backend
+    healthcheck:
+      test: ["CMD", "curl", "-f", "http://localhost:7860/health"]
+      interval: 20s
+      timeout: 5s
+      retries: 5
+
+  frontend:
+    build:
+      context: .
+      dockerfile: frontend/Dockerfile
+    container_name: polypharmacy-frontend
+    depends_on:
+      - backend
+    ports:
+      - "5173:5173"
+    volumes:
+      - ./frontend:/app
+      - /app/node_modules

openenv-polypharmacy/frontend/Dockerfile

@@ -0,0 +1,12 @@
+FROM node:20-alpine
+
+WORKDIR /app
+
+COPY frontend/package*.json ./
+RUN npm ci
+
+COPY frontend/ ./
+
+EXPOSE 5173
+
+CMD ["npm", "run", "dev", "--", "--host", "0.0.0.0", "--port", "5173"]

openenv-polypharmacy/frontend/index.html

@@ -0,0 +1,12 @@
+<!doctype html>
+<html lang="en">
+  <head>
+    <meta charset="UTF-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <title>Polypharmacy Control Center</title>
+  </head>
+  <body>
+    <div id="root"></div>
+    <script type="module" src="/src/main.jsx"></script>
+  </body>
+</html>

openenv-polypharmacy/frontend/package-lock.json

@@ -0,0 +1,1677 @@
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+  "name": "polypharmacy-frontend",
+  "version": "0.1.0",
+  "lockfileVersion": 3,
+  "requires": true,
+  "packages": {
+    "": {
+      "name": "polypharmacy-frontend",
+      "version": "0.1.0",
+      "dependencies": {
+        "react": "^18.3.1",
+        "react-dom": "^18.3.1"
+      },
+      "devDependencies": {
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+        "vite": "^5.4.2"
+      }
+    },
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+      "dev": true,
+      "license": "MIT",
+      "dependencies": {
+        "@babel/helper-validator-identifier": "^7.28.5",
+        "js-tokens": "^4.0.0",
+        "picocolors": "^1.1.1"
+      },
+      "engines": {
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+      }
+    },
+    "node_modules/@babel/compat-data": {
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+      "integrity": "sha512-T1NCJqT/j9+cn8fvkt7jtwbLBfLC/1y1c7NtCeXFRgzGTsafi68MRv8yzkYSapBnFA6L3U2VSc02ciDzoAJhJg==",
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+      "engines": {
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+}

openenv-polypharmacy/frontend/package.json

@@ -0,0 +1,19 @@
+{
+  "name": "polypharmacy-frontend",
+  "version": "0.1.0",
+  "private": true,
+  "type": "module",
+  "scripts": {
+    "dev": "vite",
+    "build": "vite build",
+    "preview": "vite preview --port 4173"
+  },
+  "dependencies": {
+    "react": "^18.3.1",
+    "react-dom": "^18.3.1"
+  },
+  "devDependencies": {
+    "@vitejs/plugin-react": "^4.3.1",
+    "vite": "^5.4.2"
+  }
+}

openenv-polypharmacy/frontend/src/App.jsx

@@ -0,0 +1,387 @@
+import { useEffect, useMemo, useRef, useState } from "react";
+
+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 = `${API_BASE.replace(/^http/, "ws")}/ws`;
+const TASKS = ["easy_screening", "budgeted_screening", "complex_tradeoff"];
+
+async function apiPost(path, body) {
+  const res = await fetch(`${API_BASE}${path}`, {
+    method: "POST",
+    headers: { "Content-Type": "application/json" },
+    body: JSON.stringify(body),
+  });
+  if (!res.ok) {
+    const msg = await res.text();
+    throw new Error(msg || `HTTP ${res.status}`);
+  }
+  return res.json();
+}
+
+export default function App() {
+  const [taskId, setTaskId] = useState("budgeted_screening");
+  const [obs, setObs] = useState(null);
+  const [log, setLog] = useState([]);
+  const [loading, setLoading] = useState(false);
+  const [action, setAction] = useState({
+    action_type: "query_ddi",
+    drug_id_1: "",
+    drug_id_2: "",
+    target_drug_id: "",
+    intervention_type: "stop",
+    proposed_new_drug_id: "",
+    rationale: "",
+  });
+
+  const medIds = useMemo(
+    () => (obs?.current_medications || []).map((m) => m.drug_id),
+    [obs]
+  );
+  const hasValidEpisode = Boolean(obs?.episode_id) && (obs?.current_medications?.length || 0) > 0;
+  const isDone = Boolean(obs?.done);
+  const finalScore =
+    typeof obs?.metadata?.grader_score === "number" ? obs.metadata.grader_score : null;
+  const noBudgetsLeft =
+    hasValidEpisode &&
+    (obs?.remaining_query_budget ?? 0) <= 0 &&
+    (obs?.remaining_intervention_budget ?? 0) <= 0;
+  const wsRef = useRef(null);
+  const pendingRef = useRef([]);
+
+  const wsEnsure = async () => {
+    if (wsRef.current && wsRef.current.readyState === WebSocket.OPEN) return wsRef.current;
+    if (wsRef.current && wsRef.current.readyState === WebSocket.CONNECTING) {
+      await new Promise((r) => setTimeout(r, 80));
+      return wsEnsure();
+    }
+
+    const ws = new WebSocket(WS_URL);
+    wsRef.current = ws;
+
+    ws.onmessage = (evt) => {
+      try {
+        const msg = JSON.parse(evt.data);
+        const pending = pendingRef.current.shift();
+        if (pending) pending.resolve(msg);
+      } catch (e) {
+        const pending = pendingRef.current.shift();
+        if (pending) pending.reject(e);
+      }
+    };
+    ws.onerror = (err) => {
+      const pending = pendingRef.current.shift();
+      if (pending) pending.reject(err);
+    };
+    ws.onclose = () => {
+      wsRef.current = null;
+    };
+
+    await new Promise((resolve, reject) => {
+      const t = setTimeout(() => reject(new Error("WebSocket connect timeout")), 2500);
+      ws.onopen = () => {
+        clearTimeout(t);
+        resolve();
+      };
+    });
+    return ws;
+  };
+
+  const wsSend = async (type, data) => {
+    const ws = await wsEnsure();
+    return await new Promise((resolve, reject) => {
+      pendingRef.current.push({ resolve, reject });
+      ws.send(JSON.stringify({ type, data }));
+    });
+  };
+
+  useEffect(() => {
+    return () => {
+      try {
+        wsRef.current?.close();
+      } catch {
+        // ignore
+      }
+    };
+  }, []);
+
+  const appendLog = (text) => {
+    setLog((prev) => [`${new Date().toLocaleTimeString()}  ${text}`, ...prev].slice(0, 20));
+  };
+
+  const normalizeObsFromWs = (packetData) => {
+    const observation = packetData?.observation || {};
+    const mergedMetadata = {
+      ...(observation?.metadata || {}),
+      ...(packetData?.info || {}),
+    };
+    return {
+      ...observation,
+      done: Boolean(packetData?.done ?? observation?.done ?? false),
+      reward: packetData?.reward ?? observation?.reward ?? null,
+      metadata: mergedMetadata,
+    };
+  };
+
+  const handleReset = async () => {
+    setLoading(true);
+    try {
+      const msg = await wsSend("reset", { task_id: taskId });
+      const data = msg?.data || {};
+      const normalized = normalizeObsFromWs(data);
+      setObs(normalized);
+      const ids = (normalized?.current_medications || []).map((m) => m.drug_id);
+      setAction((prev) => ({
+        ...prev,
+        drug_id_1: ids[0] || "",
+        drug_id_2: ids[1] || "",
+        target_drug_id: ids[0] || "",
+      }));
+      appendLog(`Reset task=${taskId}`);
+    } catch (err) {
+      appendLog(`Reset failed: ${err.message}`);
+    } finally {
+      setLoading(false);
+    }
+  };
+
+  const buildActionPayload = () => {
+    if (noBudgetsLeft) {
+      return { action_type: "finish_review" };
+    }
+    if (action.action_type === "query_ddi") {
+      return {
+        action_type: "query_ddi",
+        drug_id_1: action.drug_id_1,
+        drug_id_2: action.drug_id_2,
+      };
+    }
+    if (action.action_type === "propose_intervention") {
+      return {
+        action_type: "propose_intervention",
+        target_drug_id: action.target_drug_id,
+        intervention_type: action.intervention_type,
+        proposed_new_drug_id: action.proposed_new_drug_id || undefined,
+        rationale: action.rationale || undefined,
+      };
+    }
+    return { action_type: "finish_review" };
+  };
+
+  const isActionValid = () => {
+    if (!hasValidEpisode) return false;
+    if (isDone) return false;
+    if (noBudgetsLeft) return true;
+    if (action.action_type === "query_ddi") {
+      return Boolean(action.drug_id_1 && action.drug_id_2);
+    }
+    if (action.action_type === "propose_intervention") {
+      return Boolean(action.target_drug_id && action.intervention_type);
+    }
+    return true;
+  };
+
+  const handleStep = async (overrideAction = null) => {
+    if (!hasValidEpisode) {
+      appendLog("Run Reset Episode before stepping.");
+      return;
+    }
+    setLoading(true);
+    try {
+      const payload = overrideAction || buildActionPayload();
+      const msg = await wsSend("step", payload);
+      const data = msg?.data || {};
+      const normalized = normalizeObsFromWs(data);
+      setObs(normalized);
+      appendLog(`Step: ${payload.action_type} -> reward=${data.reward ?? 0}`);
+    } catch (err) {
+      appendLog(`Step failed: ${err.message}`);
+    } finally {
+      setLoading(false);
+    }
+  };
+
+  const askAi = async () => {
+    if (!hasValidEpisode) {
+      appendLog("Run Reset Episode before asking AI.");
+      return;
+    }
+    setLoading(true);
+    try {
+      const data = await apiPost("/agent/suggest", { observation: obs });
+      appendLog(`AI suggestion: ${data.action.action_type}`);
+      await handleStep(data.action);
+    } catch (err) {
+      appendLog(`AI suggestion failed: ${err.message}`);
+    } finally {
+      setLoading(false);
+    }
+  };
+
+  return (
+    <div className="shell">
+      <div className="bg-orb orb-a" />
+      <div className="bg-orb orb-b" />
+
+      <div className="container">
+      <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"}
+        </div>
+        <div className="actions">
+          <select value={taskId} onChange={(e) => setTaskId(e.target.value)}>
+            {TASKS.map((t) => (
+              <option key={t} value={t}>
+                {t}
+              </option>
+            ))}
+          </select>
+          <button onClick={handleReset} disabled={loading}>
+            Reset Episode
+          </button>
+          <button className="secondary" onClick={askAi} disabled={!hasValidEpisode || isDone || loading}>
+            Ask AI + Auto Step
+          </button>
+        </div>
+      </header>
+
+      <main className="layout">
+        <section className="panel glass panel-wide">
+          <h2>Episode</h2>
+          {hasValidEpisode ? (
+            <div className="kpi-grid">
+              <div><span>Episode</span><strong>{obs.episode_id}</strong></div>
+              <div><span>Task</span><strong>{obs.task_id}</strong></div>
+              <div><span>Age / Sex</span><strong>{obs.age} / {obs.sex}</strong></div>
+              <div><span>Step</span><strong>{obs.step_index}</strong></div>
+              <div><span>Query budget</span><strong>{obs.remaining_query_budget}</strong></div>
+              <div><span>Intervention budget</span><strong>{obs.remaining_intervention_budget}</strong></div>
+            </div>
+          ) : (
+            <p className="muted">Start with Reset Episode. Until then, step actions are blocked.</p>
+          )}
+          {noBudgetsLeft && (
+            <p className="muted budget-note">Query and intervention budgets are exhausted. Finish review to get final score.</p>
+          )}
+          {isDone && (
+            <p className="muted budget-note">
+              Episode complete
+              {finalScore !== null ? ` • final score: ${finalScore.toFixed(3)}` : ""}.
+              Click Reset Episode to start a new case.
+            </p>
+          )}
+        </section>
+
+        <section className="panel glass">
+          <h2>Action Console</h2>
+          <div className="action-row">
+            <label>Action type</label>
+            <select
+              value={action.action_type}
+              onChange={(e) => setAction((a) => ({ ...a, action_type: e.target.value }))}
+            >
+              <option value="query_ddi">query_ddi</option>
+              <option value="propose_intervention">propose_intervention</option>
+              <option value="finish_review">finish_review</option>
+            </select>
+          </div>
+
+          {action.action_type === "query_ddi" && (
+            <div className="stack stack-two">
+              <input
+                placeholder="drug_id_1"
+                value={action.drug_id_1}
+                onChange={(e) => setAction((a) => ({ ...a, drug_id_1: e.target.value }))}
+              />
+              <input
+                placeholder="drug_id_2"
+                value={action.drug_id_2}
+                onChange={(e) => setAction((a) => ({ ...a, drug_id_2: e.target.value }))}
+              />
+            </div>
+          )}
+
+          {action.action_type === "propose_intervention" && (
+            <div className="stack">
+              <select
+                value={action.target_drug_id}
+                onChange={(e) => setAction((a) => ({ ...a, target_drug_id: e.target.value }))}
+              >
+                <option value="">Select target drug</option>
+                {medIds.map((id) => (
+                  <option key={id} value={id}>
+                    {id}
+                  </option>
+                ))}
+              </select>
+              <select
+                value={action.intervention_type}
+                onChange={(e) => setAction((a) => ({ ...a, intervention_type: e.target.value }))}
+              >
+                <option value="stop">stop</option>
+                <option value="dose_reduce">dose_reduce</option>
+                <option value="substitute">substitute</option>
+                <option value="add_monitoring">add_monitoring</option>
+              </select>
+              <input
+                placeholder="proposed_new_drug_id (optional)"
+                value={action.proposed_new_drug_id}
+                onChange={(e) =>
+                  setAction((a) => ({ ...a, proposed_new_drug_id: e.target.value }))
+                }
+              />
+              <input
+                placeholder="rationale (optional)"
+                value={action.rationale}
+                onChange={(e) => setAction((a) => ({ ...a, rationale: e.target.value }))}
+              />
+            </div>
+          )}
+          <button onClick={() => handleStep()} disabled={!isActionValid() || loading}>
+            {noBudgetsLeft ? "Finish Review" : "Submit Step"}
+          </button>
+        </section>
+
+        <section className="panel glass">
+          <h2>Current Medications</h2>
+          <div className="med-grid">
+            {(obs?.current_medications || []).map((m) => (
+              <div key={m.drug_id} className="med-card">
+                <strong>{m.drug_id}</strong>
+                <p>{m.generic_name}</p>
+                <small>{m.dose_mg} mg • {m.atc_class}</small>
+              </div>
+            ))}
+          </div>
+        </section>
+
+        <section className="panel glass">
+          <h2>Event Log</h2>
+          <div className="logs">
+            {log.map((line, idx) => (
+              <div key={idx}>{line}</div>
+            ))}
+          </div>
+        </section>
+      </main>
+      </div>
+    </div>
+  );
+}

openenv-polypharmacy/frontend/src/main.jsx

@@ -0,0 +1,10 @@
+import React from "react";
+import ReactDOM from "react-dom/client";
+import App from "./App";
+import "./styles.css";
+
+ReactDOM.createRoot(document.getElementById("root")).render(
+  <React.StrictMode>
+    <App />
+  </React.StrictMode>
+);

openenv-polypharmacy/frontend/src/styles.css

@@ -0,0 +1,383 @@
+:root {
+  --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);
+}
+
+* {
+  box-sizing: border-box;
+}
+
+body {
+  margin: 0;
+  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;
+  overflow: hidden;
+  padding: 24px 16px 34px;
+}
+
+.container {
+  width: min(1320px, 100%);
+  margin: 0 auto;
+  position: relative;
+  z-index: 2;
+}
+
+.bg-orb {
+  position: absolute;
+  border-radius: 50%;
+  pointer-events: none;
+  opacity: 0.9;
+  filter: blur(18px);
+}
+
+.orb-a {
+  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: 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 {
+  background:
+    linear-gradient(180deg, rgba(255, 255, 255, 0.06), rgba(255, 255, 255, 0.01)),
+    var(--panel);
+  border: 1px solid var(--border);
+  box-shadow: var(--shadow);
+  backdrop-filter: blur(12px);
+}
+
+.topbar {
+  border-radius: 24px;
+  padding: clamp(14px, 2vw, 20px);
+  display: grid;
+  gap: 12px 16px;
+  grid-template-columns: minmax(220px, 1.2fr) auto minmax(280px, 1fr);
+  align-items: center;
+}
+
+.title-wrap h1 {
+  margin: 0;
+  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: 6px 0 0;
+  font-size: 0.84rem;
+  color: var(--muted);
+  letter-spacing: 0.03em;
+  text-transform: uppercase;
+}
+
+.status-chip {
+  justify-self: center;
+  padding: 7px 14px;
+  border-radius: 999px;
+  font-size: 0.72rem;
+  font-weight: 700;
+  letter-spacing: 0.08em;
+  text-transform: uppercase;
+  border: 1px solid transparent;
+}
+
+.status-chip.live {
+  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: #d8e8ff;
+  border-color: rgba(117, 186, 255, 0.48);
+  background: rgba(60, 106, 198, 0.25);
+}
+
+.actions {
+  display: flex;
+  justify-content: flex-end;
+  flex-wrap: wrap;
+  gap: 10px;
+}
+
+button,
+select,
+input {
+  width: 100%;
+  min-height: 42px;
+  border-radius: 12px;
+  border: 1px solid var(--border);
+  font-size: 0.92rem;
+  padding: 10px 12px;
+  color: var(--text);
+  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;
+  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 140ms ease, filter 140ms ease, box-shadow 140ms ease;
+}
+
+button:hover {
+  transform: translateY(-1px);
+  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, rgba(95, 185, 255, 0.9), rgba(154, 102, 255, 0.86));
+}
+
+button:disabled {
+  opacity: 0.56;
+  cursor: not-allowed;
+  filter: grayscale(0.2);
+  box-shadow: none;
+  transform: none;
+}
+
+.layout {
+  margin-top: 16px;
+  display: grid;
+  gap: 14px;
+  grid-template-columns: 1.12fr 0.88fr;
+  align-items: start;
+}
+
+.panel {
+  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 {
+  grid-column: 1 / -1;
+}
+
+.panel h2 {
+  margin: 0 0 12px;
+  font-size: 1rem;
+  font-weight: 700;
+  letter-spacing: 0.05em;
+  text-transform: uppercase;
+}
+
+.kpi-grid {
+  display: grid;
+  gap: 10px;
+  grid-template-columns: repeat(3, minmax(0, 1fr));
+}
+
+.kpi-grid div {
+  border-radius: 13px;
+  border: 1px solid var(--border);
+  background: var(--panel-solid);
+  padding: 11px 12px;
+}
+
+.kpi-grid span {
+  display: block;
+  margin-bottom: 4px;
+  font-size: 0.72rem;
+  color: var(--muted);
+  text-transform: uppercase;
+  letter-spacing: 0.05em;
+}
+
+.kpi-grid strong {
+  font-size: 1.06rem;
+  line-height: 1.2;
+}
+
+.action-row,
+.stack {
+  display: grid;
+  gap: 10px;
+  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));
+}
+
+.med-grid {
+  display: grid;
+  grid-template-columns: repeat(3, minmax(0, 1fr));
+  gap: 10px;
+  max-height: 430px;
+  overflow: auto;
+  padding-right: 4px;
+}
+
+.med-card {
+  border-radius: 14px;
+  border: 1px solid var(--border);
+  background: var(--panel-solid);
+  padding: 11px 12px;
+  transition: transform 130ms ease, border-color 130ms ease;
+}
+
+.med-card:hover {
+  transform: translateY(-1px);
+  border-color: rgba(109, 224, 255, 0.72);
+}
+
+.med-card p {
+  margin: 6px 0 4px;
+  color: var(--muted);
+  text-transform: capitalize;
+}
+
+.med-card small {
+  color: #c7d9ff;
+}
+
+.logs {
+  max-height: 300px;
+  overflow: auto;
+  padding-right: 4px;
+  display: grid;
+  gap: 7px;
+  font-size: 0.84rem;
+  font-family: ui-monospace, SFMono-Regular, Menlo, Monaco, monospace;
+}
+
+.logs div {
+  border-radius: 10px;
+  border: 1px solid var(--border);
+  background: rgba(10, 16, 31, 0.84);
+  padding: 8px 10px;
+  color: #dbebff;
+}
+
+.muted {
+  margin: 0;
+  color: var(--muted);
+}
+
+.budget-note {
+  margin-top: 10px;
+  border: 1px solid var(--border);
+  border-radius: 12px;
+  padding: 10px 12px;
+  background: rgba(13, 22, 42, 0.82);
+}
+
+@media (max-width: 1180px) {
+  .layout {
+    grid-template-columns: 1fr;
+  }
+
+  .topbar {
+    grid-template-columns: 1fr;
+  }
+
+  .status-chip {
+    justify-self: start;
+  }
+
+  .actions {
+    justify-content: flex-start;
+  }
+}
+
+@media (max-width: 760px) {
+  .shell {
+    padding: 14px 10px 24px;
+  }
+
+  .topbar,
+  .panel {
+    border-radius: 16px;
+  }
+
+  .actions {
+    width: 100%;
+  }
+
+  .actions button,
+  .actions select {
+    width: 100%;
+  }
+
+  .kpi-grid,
+  .med-grid,
+  .stack-two {
+    grid-template-columns: 1fr;
+  }
+
+  .logs {
+    max-height: 240px;
+  }
+}

openenv-polypharmacy/frontend/vite.config.js

@@ -0,0 +1,10 @@
+import { defineConfig } from "vite";
+import react from "@vitejs/plugin-react";
+
+export default defineConfig({
+  plugins: [react()],
+  server: {
+    port: 5173,
+    host: "0.0.0.0",
+  },
+});

openenv-polypharmacy/inference.py

@@ -1,15 +1,14 @@
 #!/usr/bin/env python3
 """Baseline LLM inference script for the PolypharmacyEnv.
 
-Uses the OpenAI Python client to drive an LLM agent through the
+Uses Groq's OpenAI-compatible Chat Completions API to drive an LLM agent through the
 PolypharmacyEnv HTTP API.  Emits structured stdout logs in the
 [START], [STEP], [END] format required by the OpenEnv evaluation spec.
 
 Environment variables:
-  OPENAI_API_KEY        – required
-  API_BASE_URL          – LLM endpoint (default: https://api.openai.com/v1)
-  MODEL_NAME            – model to use (default: gpt-4.1)
-  HF_TOKEN              – HuggingFace token (optional)
+  GROQ_API_KEY          – required
+  GROQ_BASE_URL         – optional (default: https://api.groq.com/openai/v1)
+  GROQ_MODEL_NAME       – model to use (default: llama-3.1-8b-instant)
   POLYPHARMACY_ENV_URL  – environment HTTP base URL (default: http://localhost:7860)
 """
 
@@ -18,7 +17,6 @@ from __future__ import annotations
 import json
 import os
 import sys
-import time
 import uuid
 from typing import Any, Dict, List
 
@@ -27,10 +25,9 @@ from openai import OpenAI
 
 # ── Configuration ────────────────────────────────────────────────────────────
 
-API_KEY = os.environ.get("OPENAI_API_KEY", "")
-API_BASE = os.environ.get("API_BASE_URL", "https://api.openai.com/v1")
-MODEL = os.environ.get("MODEL_NAME", "gpt-4.1")
-HF_TOKEN = os.environ.get("HF_TOKEN", "")
+MODEL = os.environ.get("GROQ_MODEL_NAME", "llama-3.1-8b-instant")
+API_KEY = os.environ.get("GROQ_API_KEY", "")
+API_BASE = os.environ.get("GROQ_BASE_URL", "https://api.groq.com/openai/v1")
 ENV_URL = os.environ.get("POLYPHARMACY_ENV_URL", "http://localhost:7860")
 
 TASKS = ["easy_screening", "budgeted_screening", "complex_tradeoff"]
@@ -119,16 +116,16 @@ def _summarise_obs(obs: Dict[str, Any]) -> str:
 def _ask_llm(obs_summary: str) -> Dict[str, Any]:
     """Call the LLM and parse a PolypharmacyAction JSON."""
     try:
-        resp = client.chat.completions.create(
+        chat_resp = client.chat.completions.create(
             model=MODEL,
             messages=[
                 {"role": "system", "content": SYSTEM_PROMPT},
                 {"role": "user", "content": obs_summary},
             ],
-            temperature=0.2,
             max_tokens=256,
+            temperature=0.2,
         )
-        text = resp.choices[0].message.content or ""
+        text = (chat_resp.choices[0].message.content or "").strip()
         # Strip markdown fences if present
         text = text.strip()
         if text.startswith("```"):
@@ -146,7 +143,7 @@ def _ask_llm(obs_summary: str) -> Dict[str, Any]:
 
 def main() -> None:
     if not API_KEY:
-        _err("OPENAI_API_KEY is required")
+        _err("GROQ_API_KEY is required")
         sys.exit(1)
 
     run_id = str(uuid.uuid4())[:8]
@@ -159,7 +156,6 @@ def main() -> None:
             "run_id": run_id,
             "task_id": task_id,
             "model": MODEL,
-            "api_base": API_BASE,
             "episodes": EPISODES_PER_TASK,
         })
 

openenv-polypharmacy/openenv.yaml

@@ -13,7 +13,7 @@ tags:
   - openenv
 type: space
 runtime: fastapi
-app: polypharmacy_env.api.server:app
+app: backend.main:app
 port: 7860
 
 tasks: