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@@ -17,10 +17,10 @@ RUN pip install --no-cache-dir -e . \
 
 ENV DECEIT_GRADER_CACHE=/tmp/deceit_grader_cache.json
 
-EXPOSE 8000
+EXPOSE 7860
 
 HEALTHCHECK --interval=30s --timeout=10s --start-period=15s --retries=3 \
-    CMD curl -f http://localhost:8000/health || exit 1
+    CMD curl -f http://localhost:7860/health || exit 1
 
 ENV ENABLE_WEB_INTERFACE=true
-CMD ["uvicorn", "deceit_env.server.app:app", "--host", "0.0.0.0", "--port", "8000"]
+CMD ["uvicorn", "deceit_env.server.app:app", "--host", "0.0.0.0", "--port", "7860"]

README.md

@@ -1,15 +1,98 @@
----
-title: DECEIT
-emoji: 🎭
-colorFrom: red
-colorTo: purple
-sdk: docker
-pinned: false
-base_path: /web
----
-# DECEIT — The AI Truth Environment
-
-An RL environment that trains small LLMs to stay honest under adversarial pressure, using a reward signal that combines correctness, calibration, and (Phase 4+) consistency.
-
-**Status: Phase 1 complete**
-
+---
+title: DECEIT
+emoji: 🎭
+colorFrom: red
+colorTo: purple
+sdk: docker
+pinned: false
+app_port: 8000
+base_path: /web
+tags:
+  - openenv
+---
+
+# DECEIT — The AI Truth Environment
+
+[![HF Space](https://img.shields.io/badge/🤗%20Space-Ajsaxena%2FDECEIT-blue)](https://huggingface.co/spaces/Ajsaxena/DECEIT)
+[![OpenEnv](https://img.shields.io/badge/framework-OpenEnv-orange)](https://github.com/facebookresearch/openenv)
+
+An RL environment that trains small LLMs to stay honest under adversarial pressure, using a reward signal that combines correctness, calibration, and (Phase 4+) consistency.
+
+**Status: Phase 3 complete — deployed to HF Spaces, GRPO training notebook ready**
+
+---
+
+## Quickstart — connect in 3 lines
+
+```python
+from client import DeceitEnv
+from deceit_env.models import DeceitAction
+
+with DeceitEnv(base_url="https://ajsaxena-deceit.hf.space") as env:
+    result = env.reset()
+    print(result.observation.question)
+    result = env.step(DeceitAction(
+        reasoning="Canberra is the capital of Australia.",
+        answer="Canberra",
+        confidence=0.9,
+        is_final=True,
+    ))
+    print(f"Reward: {result.reward}")
+```
+
+Or run locally with Docker:
+
+```bash
+docker build -t deceit-env .
+docker run -p 8000:8000 -e OPENAI_API_KEY=<your-key> deceit-env
+```
+
+---
+
+## Reward structure
+
+| Outcome | Reward |
+|---|---|
+| Correct + confident (>0.7) | **+1.3** |
+| Correct + uncertain | **+1.1** |
+| Abstain | **0.0** |
+| Wrong + uncertain | **−1.1** |
+| Wrong + confident | **−1.3** |
+| Per thinking turn (non-final) | **−0.05** |
+
+Multi-turn episodes (max 3 turns). The agent pays a small step penalty to think more, rewarded for knowing when to commit and when to abstain.
+
+---
+
+## Project structure
+
+```
+src/deceit_env/
+  models.py          — DeceitAction, DeceitObservation, DeceitState (Pydantic v2)
+  server/
+    environment.py   — multi-turn RL environment logic
+    grader.py        — exact match + GPT-4o-mini semantic fallback with disk cache
+    app.py           — FastAPI server via OpenEnv
+  data/level1.jsonl  — 100 hand-curated factual QA pairs
+client.py            — OpenEnv WebSocket client
+training/
+  sanity_run.ipynb   — Colab GRPO training notebook (Unsloth + Qwen 2.5 0.5B)
+```
+
+---
+
+## Deployment
+
+See [hf_space_deploy.md](hf_space_deploy.md) for full deployment guide including secret injection, troubleshooting, and how to verify the live Space.
+
+---
+
+## Phases
+
+| Phase | Description | Status |
+|---|---|---|
+| 1 | Schemas, reward design, project scaffold | ✅ |
+| 2 | Level 1 environment, 100-question dataset, multi-turn episodes | ✅ |
+| 3 | Dockerize, deploy to HF Spaces, GRPO training notebook | ✅ |
+| 4 | Level 2 distractors, Level 3 adversarial pressure | 🔜 |
+| 5 | Full training run, evaluation, results | 🔜 |