Merge pull request #63 from summerview1997/codex/webui-env-backend-preflight

Add WebUI env loading and backend preflight

skillopt_webui/app.py

@@ -9,15 +9,19 @@ import glob
 import json
 import os
 import signal
+import socket
 import subprocess
 import sys
 import threading
-import time
 from pathlib import Path
+from urllib.parse import urlparse
 
 import gradio as gr
 import yaml
 
+from skillopt.config import flatten_config
+from skillopt.config import load_config as load_merged_config
+
 PROJECT_ROOT = Path(__file__).resolve().parent.parent
 
 
@@ -42,6 +46,131 @@ def config_to_display(cfg: dict) -> str:
     return yaml.dump(cfg, default_flow_style=False, sort_keys=False)
 
 
+def _can_connect_to_url(url: str, timeout: float = 0.5) -> bool:
+    parsed = urlparse(url)
+    host = parsed.hostname
+    if not host:
+        return False
+    port = parsed.port or (443 if parsed.scheme == "https" else 80)
+    try:
+        with socket.create_connection((host, port), timeout=timeout):
+            return True
+    except OSError:
+        return False
+
+
+def _load_env_file(path: Path, env: dict[str, str]) -> None:
+    for line in path.read_text().splitlines():
+        line = line.strip()
+        if line.startswith("export "):
+            line = line[len("export "):].strip()
+        if line and not line.startswith("#") and "=" in line:
+            key, value = line.split("=", 1)
+            env[key.strip()] = value.strip().strip("\"'")
+
+
+def build_training_env() -> dict[str, str]:
+    """Build the environment shared by preflight and the training subprocess."""
+    env = os.environ.copy()
+    env["PYTHONUNBUFFERED"] = "1"
+
+    dot_env = PROJECT_ROOT / ".env"
+    if dot_env.is_file():
+        _load_env_file(dot_env, env)
+
+    secrets_dir = PROJECT_ROOT / ".secrets"
+    if secrets_dir.is_dir():
+        for env_file in sorted(secrets_dir.glob("*.env")):
+            _load_env_file(env_file, env)
+
+    # Propagate OPTIMIZER_* to base AZURE_OPENAI_* when base is missing,
+    # so target/default endpoints inherit from optimizer config.
+    for suffix in (
+        "ENDPOINT", "API_VERSION", "AUTH_MODE", "MANAGED_IDENTITY_CLIENT_ID",
+        "AD_SCOPE", "API_KEY",
+    ):
+        base_key = f"AZURE_OPENAI_{suffix}"
+        optimizer_key = f"OPTIMIZER_AZURE_OPENAI_{suffix}"
+        if not env.get(base_key) and env.get(optimizer_key):
+            env[base_key] = env[optimizer_key]
+    return env
+
+
+def validate_training_config(
+    config_path: str,
+    overrides: dict,
+    env: dict[str, str] | None = None,
+) -> str | None:
+    """Return an actionable preflight error, or None when training can start."""
+    env = env or os.environ
+    cfg_options = [
+        f"{key}={value}" for key, value in overrides.items()
+        if value is not None and value != ""
+    ]
+    try:
+        cfg = flatten_config(load_merged_config(str(PROJECT_ROOT / config_path), cfg_options))
+    except Exception as exc:
+        return f"❌ Invalid config: {exc}"
+
+    shared_endpoint = (
+        cfg.get("azure_openai_endpoint")
+        or cfg.get("azure_endpoint")
+        or env.get("AZURE_OPENAI_ENDPOINT")
+    )
+    missing_openai_roles = []
+    for role in ("optimizer", "target"):
+        if cfg.get(f"{role}_backend") != "openai_chat":
+            continue
+        role_endpoint = (
+            cfg.get(f"{role}_azure_openai_endpoint")
+            or env.get(f"{role.upper()}_AZURE_OPENAI_ENDPOINT")
+            or shared_endpoint
+        )
+        if not role_endpoint:
+            missing_openai_roles.append(role)
+    if missing_openai_roles:
+        configured_backend = cfg.get("model_backend")
+        detail = ""
+        if configured_backend in {"qwen", "qwen_chat"}:
+            detail = (
+                "\nNote: model.backend is qwen, but explicit optimizer_backend/"
+                "target_backend values are still openai_chat."
+            )
+        return (
+            "❌ Model backend is not ready: missing Azure/OpenAI-compatible endpoint "
+            f"for {', '.join(missing_openai_roles)}.\n"
+            "Set model.azure_openai_endpoint (or AZURE_OPENAI_ENDPOINT), or change "
+            "the role backends to the backend you intend to use."
+            f"{detail}"
+        )
+
+    qwen_failures = []
+    qwen_shared = (
+        cfg.get("qwen_chat_base_url")
+        or env.get("QWEN_CHAT_BASE_URL")
+        or "http://localhost:8000/v1"
+    )
+    for role in ("optimizer", "target"):
+        if cfg.get(f"{role}_backend") != "qwen_chat":
+            continue
+        base_url = (
+            cfg.get(f"{role}_qwen_chat_base_url")
+            or env.get(f"{role.upper()}_QWEN_CHAT_BASE_URL")
+            or qwen_shared
+        )
+        if not _can_connect_to_url(str(base_url)):
+            qwen_failures.append(f"{role}={base_url}")
+    if qwen_failures:
+        return (
+            "❌ Model backend is not ready: cannot connect to qwen_chat endpoint "
+            f"for {', '.join(qwen_failures)}.\n"
+            "Start your OpenAI-compatible Qwen/vLLM server, or set "
+            "model.qwen_chat_base_url / OPTIMIZER_QWEN_CHAT_BASE_URL / "
+            "TARGET_QWEN_CHAT_BASE_URL to the correct URL."
+        )
+    return None
+
+
 # ─── Training process management ────────────────────────────────────────────
 
 class TrainingManager:
@@ -63,6 +192,11 @@ class TrainingManager:
             if self.running:
                 return "⚠️ Training already running. Stop it first."
 
+        env = build_training_env()
+        preflight_error = validate_training_config(config_path, overrides, env)
+        if preflight_error:
+            return preflight_error
+
         cmd = [
             sys.executable, "scripts/train.py",
             "--config", config_path,
@@ -75,30 +209,6 @@ class TrainingManager:
             cmd.append("--cfg-options")
             cmd.extend(cfg_options)
 
-        env = os.environ.copy()
-        env["PYTHONUNBUFFERED"] = "1"
-        # Auto-load API credentials from .secrets/*.env
-        secrets_dir = PROJECT_ROOT / ".secrets"
-        if secrets_dir.is_dir():
-            for env_file in sorted(secrets_dir.glob("*.env")):
-                for line in env_file.read_text().splitlines():
-                    line = line.strip()
-                    if line and not line.startswith("#") and "=" in line:
-                        k, v = line.split("=", 1)
-                        env[k] = v
-        # Propagate OPTIMIZER_* to base AZURE_OPENAI_* when base is missing,
-        # so target/default endpoints inherit from optimizer config.
-        _propagate = [
-            ("ENDPOINT", ""), ("API_VERSION", ""), ("AUTH_MODE", ""),
-            ("MANAGED_IDENTITY_CLIENT_ID", ""), ("AD_SCOPE", ""),
-            ("API_KEY", ""),
-        ]
-        for suffix, _ in _propagate:
-            base_key = f"AZURE_OPENAI_{suffix}"
-            optimizer_key = f"OPTIMIZER_AZURE_OPENAI_{suffix}"
-            if not env.get(base_key) and env.get(optimizer_key):
-                env[base_key] = env[optimizer_key]
-
         try:
             proc = subprocess.Popen(
                 cmd,

tests/test_webui_env_preflight.py

@@ -0,0 +1,89 @@
+import pytest
+import yaml
+
+pytest.importorskip("gradio")
+
+from skillopt_webui import app as webui_app
+
+
+def _write_config(tmp_path, model):
+    config_path = tmp_path / "config.yaml"
+    config_path.write_text(
+        yaml.safe_dump({
+            "model": model,
+            "env": {"name": "searchqa"},
+        }),
+        encoding="utf-8",
+    )
+    return str(config_path)
+
+
+def test_build_training_env_loads_project_dotenv(tmp_path, monkeypatch):
+    monkeypatch.setattr(webui_app, "PROJECT_ROOT", tmp_path)
+    (tmp_path / ".env").write_text(
+        "\n".join([
+            "export QWEN_CHAT_BASE_URL=http://qwen.example/v1",
+            "QWEN_CHAT_MODEL=test-model",
+            "QWEN_CHAT_API_KEY='secret-value'",
+        ]),
+        encoding="utf-8",
+    )
+
+    env = webui_app.build_training_env()
+
+    assert env["QWEN_CHAT_BASE_URL"] == "http://qwen.example/v1"
+    assert env["QWEN_CHAT_MODEL"] == "test-model"
+    assert env["QWEN_CHAT_API_KEY"] == "secret-value"
+
+
+def test_preflight_reports_missing_openai_chat_endpoint(tmp_path, monkeypatch):
+    monkeypatch.delenv("AZURE_OPENAI_ENDPOINT", raising=False)
+    monkeypatch.delenv("OPTIMIZER_AZURE_OPENAI_ENDPOINT", raising=False)
+    monkeypatch.delenv("TARGET_AZURE_OPENAI_ENDPOINT", raising=False)
+    config_path = _write_config(
+        tmp_path,
+        {
+            "backend": "qwen",
+            "optimizer_backend": "openai_chat",
+            "target_backend": "openai_chat",
+        },
+    )
+
+    error = webui_app.validate_training_config(config_path, {})
+
+    assert "missing Azure/OpenAI-compatible endpoint for optimizer, target" in error
+    assert "model.backend is qwen" in error
+
+
+def test_preflight_reports_unreachable_qwen_endpoint(tmp_path, monkeypatch):
+    monkeypatch.setattr(webui_app, "_can_connect_to_url", lambda _url: False)
+    config_path = _write_config(
+        tmp_path,
+        {
+            "backend": "qwen",
+            "optimizer_backend": "qwen_chat",
+            "target_backend": "qwen_chat",
+            "qwen_chat_base_url": "http://127.0.0.1:9/v1",
+        },
+    )
+
+    error = webui_app.validate_training_config(config_path, {})
+
+    assert "cannot connect to qwen_chat endpoint" in error
+    assert "127.0.0.1:9" in error
+
+
+def test_preflight_accepts_reachable_qwen_endpoint(tmp_path, monkeypatch):
+    seen_urls = []
+    monkeypatch.setattr(webui_app, "_can_connect_to_url", lambda url: seen_urls.append(url) or True)
+    config_path = _write_config(
+        tmp_path,
+        {
+            "optimizer_backend": "qwen_chat",
+            "target_backend": "qwen_chat",
+            "qwen_chat_base_url": "http://qwen.example/v1",
+        },
+    )
+
+    assert webui_app.validate_training_config(config_path, {}) is None
+    assert seen_urls == ["http://qwen.example/v1", "http://qwen.example/v1"]