Update README.md

README.md

@@ -1,92 +1,17 @@
-# Meta Hackathon OpenEnv - Cyber Security Log Analysis
-
-This project exposes a small cybersecurity log-analysis environment with a FastAPI
-server, a local environment class, and an agent evaluation script.
-
-## Output contract
-
-The agent must return only JSON with exactly these keys:
-
-```json
-{
-  "category": "brute_force",
-  "severity": "high",
-  "action": "block source IP and enable rate limiting"
-}
-```
-
-Allowed `category` values:
-
-- `brute_force`
-- `malware`
-- `phishing`
-- `dos_attack`
-- `normal`
-
-Allowed `severity` values:
-
-- `low`
-- `medium`
-- `high`
-
-`action` should be a short, concrete mitigation step.
-
-## API endpoints
-
-- `GET /reset` returns a random sample plus `instructions`, `allowed_categories`,
-  `allowed_severities`, `response_example`, and `agent_prompt`.
-- `POST /step` accepts the agent JSON payload and returns the normalized reward.
-- `GET /state` returns the current step count.
-- `GET /tasks` describes the task tiers and output contract.
-- `POST /grader` scores a `predicted` payload against an `expected` payload.
-- `GET /baseline` runs one simple baseline action against a fresh sample.
-
-## Local setup
-
-```bash
-python -m venv .venv
-. .venv/Scripts/activate
-pip install -r requirements.txt
-uvicorn app:app --host 0.0.0.0 --port 7860
-```
-
-For PowerShell activation, use:
-
-```powershell
-.venv\Scripts\Activate.ps1
-```
-
-## Agent evaluation runner
-
-`test_grader.py` is the local runner that calls a chat completions API, parses the
-model output, grades it, and appends a record to `agent_eval_log.jsonl`.
-
-Create a local `.env` file with:
-
-```env
-AGENT_API_KEY=your_api_key
-AGENT_API_URL=https://api.openai.com/v1/chat/completions
-```
-
-Then run:
-
-```bash
-python test_grader.py
-```
-
-## Docker
-
-Build and run the API container with:
-
-```bash
-docker build -t security-log-env .
-docker run --rm -p 7860:7860 security-log-env
-```
-
-## Scoring
-
-Scoring uses cosine similarity between vectorized predicted and expected responses.
-The raw cosine value is mapped from `[-1, 1]` into the reward range `[0, 1]`:
-
-- aligned vectors score `1`
-- opposite vectors score `0`
+---
+title: Security Log OpenEnv
+emoji: 🔐
+colorFrom: blue
+colorTo: indigo
+sdk: docker
+app_port: 7860
+---
+
+# 🔐 Security Log Analysis OpenEnv
+
+This project implements a **production-ready OpenEnv environment** for cybersecurity log analysis.
+
+An AI agent interacts with this environment using the standard:
+
+```python
+reset() → step(action) → state()