---
title: PharmaAgent
emoji: 💊
colorFrom: blue
colorTo: green
sdk: docker
pinned: false
---

# 🏥 PharmaAgent — Clinical Decision RL Environment

An OpenMV-compatible reinforcement learning environment where an agent acts as an **AI clinical pharmacist**, navigating a multi-step patient treatment pipeline powered by real DrugBank data.

## What the Agent Does

Given a patient case (symptoms + existing medications), the agent must:

| Step | Action | Reward |
|------|--------|--------|
| 1 | **Diagnose** — identify the condition from symptoms | 0–0.30 |
| 2 | **Select drugs** — build a safe treatment regimen | 0.05–0.20 per drug |
| 3 | **Check DDI** — verify drug-drug interactions | 0.05–0.30 |
| 4 | **Finalize** — submit the complete regimen | 0–0.30 |

**Total possible reward per episode: 1.5 (normalized to 0–1)**

## Why This Is Different

- All scoring is grounded in **real DrugBank data** (19,842 drugs, 500k interaction pairs) — no hardcoded opinions
- Patient cases are **generated dynamically** from the database — the agent cannot memorize them
- The LLM is called **only once per episode**, at the end, purely to format a human-readable summary — it has zero influence on any reward
- Penalizes dangerous drug combinations using DrugBank's own severity classifications

## API Endpoints

| Method | Path | Description |
|--------|------|-------------|
| POST | `/reset` | Start new episode |
| POST | `/step?session_id=X` | Take an action |
| GET | `/state?session_id=X` | Get session state |
| GET | `/health` | Health check |
| GET | `/web` | Browser UI for manual testing |

## Stack

- FastAPI + SQLite (DrugBank)
- Qwen 2.5 72B via HuggingFace Inference Router (agent)
- Groq Llama 3.3 70B (summary formatter only)