Bug fixes

pyproject.toml

@@ -27,25 +27,30 @@ classifiers = [
 
 dependencies = [
     "pydantic>=2.0.0",
-    "openenv>=0.1.0",
     "numpy>=1.24.0",
     "openai>=1.0.0",
-    "typer>=0.9.0",
     "pyyaml>=6.0",
-    "matplotlib>=3.7.0",
-    "pandas>=2.0.0",
-    "google-genai>=0.8.0",
     "uvicorn[standard]>=0.24.0",
     "fastapi>=0.104.0",
-    "seaborn>=0.12.0",
     "websockets>=12.0",
-    "stable-baselines3>=2.0.0",
     "requests>=2.31.0",
-    "pytest>=7.4.0",
-    "pytest-cov>=4.1.0",
 ]
 
 [project.optional-dependencies]
+train = [
+    "stable-baselines3>=2.0.0",
+]
+viz = [
+    "matplotlib>=3.7.0",
+    "pandas>=2.0.0",
+    "seaborn>=0.12.0",
+]
+gemini = [
+    "google-genai>=0.8.0",
+]
+cli = [
+    "typer>=0.9.0",
+]
 dev = [
     "pytest>=7.4.0",
     "pytest-cov>=4.1.0",
@@ -53,6 +58,9 @@ dev = [
     "ruff>=0.0.280",
     "mypy>=1.4.0",
 ]
+all = [
+    "adaptive-alert-triage[train,viz,gemini,cli,dev]",
+]
 
 [project.urls]
 Homepage = "https://github.com/scalar/adaptive-alert-triage"

requirements.txt

@@ -1,10 +1,9 @@
 # requirements.txt — Adaptive Alert Triage
-# Generated from pyproject.toml dependencies
+# Server-only dependencies (matches pyproject.toml core deps)
 # Designed for 2 vCPU / 8 GB RAM (HF Spaces free tier)
 # No PyTorch / TensorFlow — RL agent is pure numpy
 
 # ── Core environment ──────────────────────────────────────────────────────────
-openenv>=0.1.0
 numpy>=1.24.0
 pydantic>=2.0.0
 
@@ -16,26 +15,8 @@ websockets>=12.0
 # ── LLM inference (OpenAI client — required by checklist) ────────────────────
 openai>=1.0.0
 
-# ── Gemini SDK (legacy inference.py in agents/ still uses it) ────────────────
-google-genai>=0.8.0
-
 # ── HTTP client (train_external.py, real-alert listener) ─────────────────────
 requests>=2.31.0
 
-# ── Data / plotting ───────────────────────────────────────────────────────────
-matplotlib>=3.7.0
-pandas>=2.0.0
-seaborn>=0.12.0
-
 # ── YAML parsing ─────────────────────────────────────────────────────────────
-pyyaml>=6.0
-
-# ── CLI helpers ───────────────────────────────────────────────────────────────
-typer>=0.9.0
-
-# ── Testing ───────────────────────────────────────────────────────────────────
-pytest>=7.4.0
-pytest-cov>=4.1.0
-
-# ── External RL trainer (optional — not needed for server or inference) ────────
-# stable-baselines3>=2.0.0   # uncomment if using train_external.py
+pyyaml>=6.0

src/adaptive_alert_triage/__init__.py

@@ -8,8 +8,8 @@ for alert triage and incident response simulation.
 __version__ = "0.1.0"
 __author__ = "Scalar Hackathon Team"
 
-from adaptive_alert_triage.env import AdaptiveAlertTriageEnv
-from adaptive_alert_triage.models import Action, Observation, Reward, Alert
+from .env import AdaptiveAlertTriageEnv
+from .models import Action, Observation, Reward, Alert
 
 __all__ = [
     "AdaptiveAlertTriageEnv",

src/adaptive_alert_triage/env.py

@@ -46,26 +46,27 @@ import numpy as np
 import openenv as gym
 from openenv import spaces
 
-from adaptive_alert_triage.models import (
+from .models import (
     Action,
     Alert,
     EpisodeState,
     Observation,
     Reward,
 )
-from adaptive_alert_triage import utils
+from . import utils
 
 # Import reward calculation with graceful fallback for development mode
+import os as _os
+import sys as _sys
+
 try:
     from rewards.reward import calculate_reward
 except ImportError:
-    import os
-    import sys
-
-    _project_root = os.path.dirname(
-        os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+    _project_root = _os.path.dirname(
+        _os.path.dirname(_os.path.dirname(_os.path.abspath(__file__)))
     )
-    sys.path.insert(0, _project_root)
+    if _project_root not in _sys.path:
+        _sys.path.insert(0, _project_root)
     from rewards.reward import calculate_reward  # type: ignore[no-redef]
 
 

src/adaptive_alert_triage/models.py

@@ -289,7 +289,7 @@ class EpisodeState(BaseModel):
     failures_count: int = Field(
         default=0, ge=0, description="System failures so far"
     )
-    actions_taken: List[Action] = Field(
+    actions_taken: List[Dict[str, Any]] = Field(
         default_factory=list, description="Full action history for this episode"
     )
     seed: Optional[int] = Field(

src/adaptive_alert_triage/utils.py

@@ -17,7 +17,7 @@ from typing import List, Dict, Tuple, Optional
 
 import numpy as np
 
-from adaptive_alert_triage.models import Alert, AlertType
+from .models import Alert, AlertType
 
 
 # ---------------------------------------------------------------------------

test_endpoints.py

@@ -0,0 +1,41 @@
+#!/usr/bin/env python3
+"""
+Test script to verify endpoints are accessible.
+Usage: python test_endpoints.py https://tusharp2006-scaler_deployment.hf.space
+"""
+
+import sys
+import httpx
+
+async def test_endpoints(base_url: str):
+    """Test all key endpoints"""
+    endpoints = [
+        "/",
+        "/health",
+        "/metrics",
+        "/tasks",
+    ]
+    
+    base_url = base_url.rstrip("/")
+    
+    async with httpx.AsyncClient(timeout=10.0) as client:
+        for endpoint in endpoints:
+            url = f"{base_url}{endpoint}"
+            try:
+                resp = await client.get(url)
+                print(f"[{resp.status_code}] GET {endpoint}")
+                if resp.status_code < 300:
+                    print(f"  ✓ Response: {str(resp.json())[:100]}")
+                else:
+                    print(f"  ✗ Error: {resp.text[:100]}")
+            except Exception as e:
+                print(f"[ERROR] GET {endpoint} - {e}")
+
+if __name__ == "__main__":
+    import asyncio
+    if len(sys.argv) < 2:
+        print("Usage: python test_endpoints.py <base_url>")
+        print("Example: python test_endpoints.py https://tusharp2006-scaler_deployment.hf.space")
+        sys.exit(1)
+    
+    asyncio.run(test_endpoints(sys.argv[1]))