feat: update Dockerfile and README for HuggingFace Spaces deployment

README.md

@@ -1,4 +1,14 @@
-# 🕵️‍♂️ AuditEnv
+---
+title: Corp AI
+emoji: 🏢
+colorFrom: blue
+colorTo: green
+sdk: docker
+pinned: false
+tags:
+  - openenv
+---
+# 📊 AuditEnv
 
 **AuditEnv is an OpenEnv-style reinforcement learning environment for autonomous compliance auditing.**
 
@@ -8,7 +18,7 @@ The environment simulates real audit workflows and exposes deterministic, graded
 
 ---
 
-## 📖 What is AuditEnv? (The Core Concept)
+## ?? What is AuditEnv? (The Core Concept)
 
 In Reinforcement Learning (RL), the loop works like this:
 1. **State/Observation:** The environment gives the AI some data (e.g., procurement invoices, employee access logs).
@@ -19,12 +29,13 @@ AuditEnv provides this exact standardized simulation framework utilizing the Ope
 
 ---
 
-## 🏗️ How the Codebase is Structured
+## ??? How the Codebase is Structured
 
 - **The API (src/auditenv/server.py):** A FastAPI web server that acts as the "game engine". It has three main endpoints:
   - POST /reset: Starts a new audit scenario.
   - GET /state: Lets the AI look at the current documents/data.
-  - POST /step: The AI submits its action here, and the server replies with the 
+  - POST /step: The AI submits its action here, and the server replies with the 
+eward and whether the audit is done.
 - **The Rules (src/auditenv/models.py):** Defines exactly what data looks like using Pydantic. It ensures the AI can only take specific actions: submit_finding, lag_human_review, or 
 oop.
 - **The Logic & Referee (src/auditenv/grader.py):** This is the core of the environment. When the AI takes an action, this calculate a deterministic score between 0.0 (total failure) and 1.0 (perfect audit).
@@ -32,16 +43,16 @@ oop.
 
 ### Structure Tree
 AI_AUDIT/
-├── configs/           # YAML configs for dataset mapping & reward logic
-├── data/              # Local dataset folder (ignored in git)
-├── docker/            # Dockerization for the environment
-├── scripts/           # Baselines and data checkers
-├── src/auditenv/      # Core environment server, State, Models, and Graders
-└── tests/             # Unit and smoke tests
++-- configs/           # YAML configs for dataset mapping & reward logic
++-- data/              # Local dataset folder (ignored in git)
++-- docker/            # Dockerization for the environment
++-- scripts/           # Baselines and data checkers
++-- src/auditenv/      # Core environment server, State, Models, and Graders
++-- tests/             # Unit and smoke tests
 
 ---
 
-## 🚀 Tasks & Difficulty Levels
+## ?? Tasks & Difficulty Levels
 
 OpenEnv metadata is defined in openenv.yaml. All tasks return deterministic, normalized rewards from [0.0, 1.0].
 
@@ -51,7 +62,7 @@ OpenEnv metadata is defined in openenv.yaml. All tasks return deterministic, nor
 
 ---
 
-## ✅ Current Status (As of April 2026)
+## ? Current Status (As of April 2026)
 
 We have successfully built the "game engine" and the levels!
 
@@ -63,13 +74,14 @@ We have successfully built the "game engine" and the levels!
 
 ---
 
-## 🗺️ What Needs to be Done Next (Roadmap)
+## ??? What Needs to be Done Next (Roadmap)
 
 Now that the simulator is built, the next steps are pure Machine Learning: hooking up LLMs to this environment and optimizing them.
 
 ### Phase A: The LLM Baseline
 Our OpenAI baseline is currently blocked by quota limits.
-- **Action:** Add billing credits to the OpenAI key, or swap out the OpenAI code in 
+- **Action:** Add billing credits to the OpenAI key, or swap out the OpenAI code in 
+un_baseline.py for a free local model (like Llama-3 or Mistral) using Ollama/vLLM. 
 - **Goal:** See what an unschooled, prompt-based LLM scores on Easy, Medium, and Hard.
 
 ### Phase B: The Reinforcement Learning (RL) Pipeline
@@ -84,7 +96,7 @@ Right now, the AI only parses text/tabular data.
 
 ---
 
-## 🛠️ Setup & Installation
+## ??? Setup & Installation
 
 ### 1) Create environment and install dependencies
 Using uv (recommended):
@@ -97,4 +109,5 @@ uv pip install -r requirements.txt
 
 uvicorn auditenv.server:app --reload --app-dir src
 
-*(Endpoints available: GET /health, POST /reset, POST /step, GET /state)*
+*(Endpoints available: GET /health, POST /reset, POST /step, GET /state)*
+

docker/Dockerfile

@@ -14,6 +14,6 @@ COPY scripts ./scripts
 # Create data directory (fallback synthetic data will be used if empty)
 RUN mkdir -p data
 
-EXPOSE 8000
+EXPOSE 7860
 
-CMD ["uvicorn", "auditenv.server:app", "--host", "0.0.0.0", "--port", "8000", "--app-dir", "src"]
+CMD ["uvicorn", "auditenv.server:app", "--host", "0.0.0.0", "--port", "7860", "--app-dir", "src"]