app.py

"""The Oracle — custom frontend via gradio.Server.

Architecture (deterministic core + LLM only for phrasing):
  1. engine.filter_candidates  — narrow the JSON candidate list by the answers
  2. engine.choose_attribute   — pick the attribute that best splits the set
  3. question_maker.make_question — LLM turns that attribute into a natural question
The LLM never decides elimination; the engine does, so filtering is always exact.

Outcomes: 1 left -> guess; 0 left -> "I don't know it yet" (discovery hook);
"I am not sure" answers don't filter and aren't re-asked.

Run:  python server.py
      ORACLE_QUESTION_LLM=1 python server.py   # natural questions via local LLM
"""

from __future__ import annotations

import json
import os

from gradio import Server
from fastapi.responses import HTMLResponse
import engine
import discovery
import question_maker

import subprocess
subprocess.run("pip install -V llama_cpp_python==0.3.0", shell=True)

MAX_QUESTIONS = int(os.environ.get("ORACLE_MAX_QUESTIONS", "20"))

app = Server()
HERE = os.path.dirname(os.path.abspath(__file__))

try:
    from fastapi.staticfiles import StaticFiles
    app.mount("/images", StaticFiles(directory=os.path.join(HERE, "images")), name="images")
except Exception as exc:  # noqa: BLE001
    print(f"[oracle] could not mount /images: {exc}")

# At boot (background): load the model, then pre-generate & cache every question
# once. After this, gameplay is instant dict lookups — no per-turn model calls —
# so it stays snappy even on a weak CPU. No-op if the LLM is disabled/unavailable.
if os.environ.get("ORACLE_QUESTION_LLM", "1") == "1":
    import threading

    def _boot_warm():
        import llm
        import question_maker
        llm.warmup()
        question_maker.prewarm_questions()

    threading.Thread(target=_boot_warm, daemon=True).start()


@app.api(name="next")
def next_turn(category: str = "animal", history_json: str = "[]", asked: int = 0) -> dict:
    try:
        history = json.loads(history_json) if history_json else []
    except (json.JSONDecodeError, ValueError):
        history = []

    # STEP 1 — deterministic filter by the answers so far
    facts = [{"attribute": h.get("attribute"), "answer": h.get("answer")} for h in history]
    items = engine.filter_candidates(engine.load_items(category), facts)
    names = [it["name"] for it in items]
    print(f"[oracle] {category}: {len(names)} remain -> {names[:20]}", flush=True)

    base = {"asked": asked, "max": MAX_QUESTIONS, "remaining": len(names), "items": names}

    # STEP 2 — outcomes decided in code
    if not names:
        # discovery: nothing in the DB matches -> ask the player to teach us
        return {"action": "giveup",
                "text": "Hmm, I don't know this one yet. What were you thinking of?",
                **base}
    if len(names) == 1 or asked >= MAX_QUESTIONS:
        yes_attrs = [h.get("attribute") for h in history
                     if str(h.get("answer", "")).strip().lower() == "yes" and h.get("attribute")]
        reveal = question_maker.make_reveal(category, yes_attrs)
        return {"action": "guess", "text": names[0], "reveal": reveal, **base}

    # STEP 3 — pick the best attribute, then have the LLM phrase the question
    asked_attrs = [h.get("attribute") for h in history if h.get("attribute")]
    attr = engine.choose_attribute(category, items, asked_attrs)
    if attr is None:
        return {"action": "guess", "text": names[0], **base}  # can't split further
    question = question_maker.make_question(category, attr, asked_attrs)
    return {"action": "ask", "attribute": attr, "text": question, "options": ["Yes", "No"], **base}


@app.api(name="learn")
def learn(category: str = "animal", name: str = "", history_json: str = "[]") -> dict:
    """Discovery mode: the player tells us what it was; we derive its attributes,
    add it to the JSON DB, and explain any answers that contradicted reality."""
    try:
        history = json.loads(history_json) if history_json else []
    except (json.JSONDecodeError, ValueError):
        history = []
    try:
        return discovery.learn_item(category, name, history)
    except Exception as exc:  # noqa: BLE001 — never crash the game on a teach
        print(f"[oracle] learn failed: {exc}")
        return {"status": "error", "message": str(exc)}


@app.get("/", response_class=HTMLResponse)
async def homepage():
    with open(os.path.join(HERE, "index.html"), "r", encoding="utf-8") as f:
        return f.read()


if __name__ == "__main__":
    # On Hugging Face Spaces the app must listen on 0.0.0.0:7860. Gradio reads
    # these env vars; setdefault keeps local runs unchanged.
    os.environ.setdefault("GRADIO_SERVER_NAME", "0.0.0.0")
    os.environ.setdefault("GRADIO_SERVER_PORT", "7860")
    app.launch(show_error=True)