"""Plan an application from a prompt using a real Hugging Face LLM.

Returns a structured plan including the actual source files for a deployable
Hugging Face Space. Falls back to a deterministic heuristic plan if the LLM is
unavailable (so the builder never hard-fails).
"""
from __future__ import annotations

import json
import re

from app.inference import InferenceError, chat

SYSTEM_PROMPT = (
    "You are a senior engineer that scaffolds complete, deployable Hugging Face "
    "Spaces from a product description. You output STRICT JSON only — no prose, "
    "no markdown fences."
)

USER_TEMPLATE = """Design a deployable Hugging Face Space for this idea:

\"\"\"{prompt}\"\"\"

Preferred SDK: {preferred_sdk}  (if "auto", pick the best of: gradio, docker, static)

Return STRICT JSON with EXACTLY this shape:
{{
  "app_name": "kebab-case-name",
  "selected_sdk": "gradio|docker|static",
  "summary": "2-3 sentence description of what the app does",
  "features": ["feature 1", "feature 2", "feature 3"],
  "files": [
    {{"path": "README.md", "content": "...full file content..."}},
    {{"path": "app.py", "content": "..."}},
    {{"path": "requirements.txt", "content": "..."}}
  ]
}}

Requirements for the generated files:
- Include a README.md whose top is a YAML metadata block (---) with title, emoji,
  colorFrom, colorTo, sdk, and (app_file for gradio/static, or app_port: 7860 for docker).
- For gradio: provide app.py (a runnable gradio app) and requirements.txt.
- For docker: provide Dockerfile exposing 7860, app.py, requirements.txt.
- For static: provide index.html.
- Code must be COMPLETE and runnable, not pseudo-code. Keep it focused and self-contained.
- Prefer free Hugging Face inference (huggingface_hub InferenceClient) when the app needs AI.
- Output JSON only.
"""


def _slugify(text: str) -> str:
    text = re.sub(r"[^a-zA-Z0-9]+", "-", text.strip().lower()).strip("-")
    return text[:50] or "generated-space"


def _extract_json(text: str) -> dict:
    text = text.strip()
    text = re.sub(r"^```(?:json)?", "", text).strip()
    text = re.sub(r"```$", "", text).strip()
    start = text.find("{")
    end = text.rfind("}")
    if start == -1 or end == -1:
        raise ValueError("No JSON object in model output.")
    return json.loads(text[start : end + 1])


def _heuristic_plan(prompt: str, preferred_sdk: str) -> dict:
    prompt_lower = prompt.lower()
    selected_sdk = preferred_sdk
    if preferred_sdk == "auto":
        if any(k in prompt_lower for k in ["stream", "agent", "api", "sandbox", "workflow"]):
            selected_sdk = "docker"
        elif any(k in prompt_lower for k in ["landing page", "portfolio", "static"]):
            selected_sdk = "static"
        else:
            selected_sdk = "gradio"
    return {
        "prompt": prompt,
        "app_name": _slugify(prompt.split(".")[0][:60]),
        "selected_sdk": selected_sdk,
        "summary": f"Starter scaffold for: {prompt}",
        "features": [
            "configurable UI scaffold",
            "placeholder business logic",
            "Hugging Face Space metadata",
            "local development instructions",
        ],
        "files": [],  # signals repo_generator to use built-in templates
        "source": "fallback",
    }


_VALID_SDKS = {"gradio", "docker", "static"}


def plan_application(prompt: str, preferred_sdk: str = "auto") -> dict:
    if not prompt or not prompt.strip():
        return _heuristic_plan("Untitled app", preferred_sdk)

    messages = [
        {"role": "system", "content": SYSTEM_PROMPT},
        {"role": "user", "content": USER_TEMPLATE.format(prompt=prompt, preferred_sdk=preferred_sdk)},
    ]

    try:
        raw = chat(messages, max_tokens=2600, temperature=0.4)
        data = _extract_json(raw)

        sdk = str(data.get("selected_sdk", "gradio")).lower().strip()
        if preferred_sdk in _VALID_SDKS:
            sdk = preferred_sdk
        if sdk not in _VALID_SDKS:
            sdk = "gradio"

        files = []
        for f in data.get("files", []) or []:
            path = str(f.get("path", "")).strip()
            content = f.get("content", "")
            if path and isinstance(content, str) and content.strip():
                files.append({"path": path, "content": content})

        return {
            "prompt": prompt,
            "app_name": _slugify(str(data.get("app_name") or prompt.split(".")[0])),
            "selected_sdk": sdk,
            "summary": str(data.get("summary", "")).strip() or f"Generated app for: {prompt}",
            "features": [str(x) for x in (data.get("features") or [])][:8],
            "files": files,
            "source": "llm",
        }
    except InferenceError:
        raise
    except Exception:
        return _heuristic_plan(prompt, preferred_sdk)