port changes

Dockerfile

@@ -12,11 +12,11 @@ COPY baseline_runner.py .
 COPY inference.py .
 COPY openenv.yaml .
 
-# HuggingFace Spaces expects port 7860
-EXPOSE 7860
+# HuggingFace Spaces expects port 8000
+EXPOSE 8000
 
 # Health check
 HEALTHCHECK --interval=30s --timeout=10s --retries=3 \
-    CMD python -c "import requests; requests.get('http://localhost:7860/')" || exit 1
+    CMD python -c "import requests; requests.get('http://localhost:8000/')" || exit 1
 
-CMD ["python", "-m", "uvicorn", "server.main:app", "--host", "0.0.0.0", "--port", "7860"]
+CMD ["python", "-m", "uvicorn", "server.main:app", "--host", "0.0.0.0", "--port", "8000"]

README.md

@@ -4,7 +4,7 @@ emoji: 🔧
 colorFrom: blue
 colorTo: green
 sdk: docker
-app_port: 7860
+app_port: 8000
 pinned: false
 ---
 
@@ -266,14 +266,14 @@ This dense reward signal helps RL agents learn faster than sparse pass/fail grad
 
 ```bash
 # 1. Start an episode
-curl -X POST http://localhost:7860/reset \
+curl -X POST http://localhost:8000/reset \
   -H "Content-Type: application/json" \
   -d '{"task_id": "dockerfile_syntax", "scenario_id": "typo_filename"}'
 
 # Response: observation with broken Dockerfile + error message
 
 # 2. Fix the typo
-curl -X POST http://localhost:7860/step \
+curl -X POST http://localhost:8000/step \
   -H "Content-Type: application/json" \
   -d '{
     "action": {
@@ -289,7 +289,7 @@ curl -X POST http://localhost:7860/step \
 # Response: reward=0.4, issues_fixed=1/1
 
 # 3. Submit
-curl -X POST http://localhost:7860/step \
+curl -X POST http://localhost:8000/step \
   -H "Content-Type: application/json" \
   -d '{"action": {"action_type": "submit"}}'
 
@@ -322,7 +322,7 @@ This shows the environment is both **solvable** (3 perfect scores) and **challen
 
 ```bash
 pip install -r requirements.txt
-python -m uvicorn server.main:app --host 0.0.0.0 --port 7860
+python -m uvicorn server.main:app --host 0.0.0.0 --port 8000
 ```
 
 ### Run Tests
@@ -335,7 +335,7 @@ pytest tests/ -v
 
 ```bash
 docker build -t cicd-docker-env .
-docker run -p 7860:7860 cicd-docker-env
+docker run -p 8000:8000 cicd-docker-env
 ```
 
 ### Baseline Inference (with LLM)

inference.py

@@ -27,7 +27,7 @@ from openai import OpenAI
 API_BASE_URL = os.environ.get("API_BASE_URL", "https://router.huggingface.co/v1")
 MODEL_NAME = os.environ.get("MODEL_NAME", "meta-llama/Llama-3.1-70B-Instruct")
 HF_TOKEN = os.environ.get("HF_TOKEN", "")
-ENV_URL = os.environ.get("ENV_URL", "http://localhost:7860")
+ENV_URL = os.environ.get("ENV_URL", "http://localhost:8000")
 MAX_STEPS = 8  # leave 2 steps buffer before env hard-limit of 10
 
 SYSTEM_PROMPT = """You are an expert DevOps engineer debugging CI/CD pipelines.
@@ -294,7 +294,7 @@ def main():
         print(f"\nERROR: Cannot connect to environment at {ENV_URL}")
         print(f"  {e}")
         print("\nStart the server first:")
-        print("  python -m uvicorn server.main:app --host 0.0.0.0 --port 7860")
+        print("  python -m uvicorn server.main:app --host 0.0.0.0 --port 8000")
         sys.exit(1)
 
     client = create_client()

server/main.py

@@ -227,4 +227,4 @@ async def run_baseline(request: Optional[BaselineRequest] = None):
 
 
 if __name__ == "__main__":
-    uvicorn.run(app, host="0.0.0.0", port=7860)
+    uvicorn.run(app, host="0.0.0.0", port=8000)

smoke_test.py

@@ -2,7 +2,7 @@
 
 Usage:
   .\\.venv\\Scripts\\python.exe smoke_test.py
-  .\\.venv\\Scripts\\python.exe smoke_test.py --mode live --base-url http://127.0.0.1:7860
+  .\\.venv\\Scripts\\python.exe smoke_test.py --mode live --base-url http://127.0.0.1:8000
 
 Modes:
   - inprocess (default): uses FastAPI TestClient, no running server needed.
@@ -218,7 +218,7 @@ def run_smoke(client: EndpointClient) -> int:
 def main() -> int:
     parser = argparse.ArgumentParser(description="Smoke test CI/CD Debug FastAPI server")
     parser.add_argument("--mode", choices=["inprocess", "live"], default="inprocess")
-    parser.add_argument("--base-url", default="http://127.0.0.1:7860")
+    parser.add_argument("--base-url", default="http://127.0.0.1:8000")
     args = parser.parse_args()
 
     if args.mode == "inprocess":