app.py

from __future__ import annotations

import os
import re
from typing import Any, Dict, Tuple

from fastapi import FastAPI, Request
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import HTMLResponse, JSONResponse

from context_parser import detect_help_mode
from conversation_logic import generate_response
from models import ChatRequest
from ui_html import HOME_HTML
from utils import clamp01, get_user_text

from retrieval_engine import RetrievalEngine

retriever = RetrievalEngine()

app = FastAPI(title="GMAT Solver v3", version="3.1.0")

app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)


def split_unity_message(full_text: str) -> Tuple[str, str]:
    """
    Splits the Unity payload into:
    - hidden_context
    - actual_user_message

    Expected Unity format:
        <hidden context>

        USER_MESSAGE:
        <user text>

    Falls back safely if marker is missing.
    """
    if not full_text:
        return "", ""

    marker = "USER_MESSAGE:"
    idx = full_text.find(marker)

    if idx == -1:
        return "", full_text.strip()

    hidden_context = full_text[:idx].strip()
    actual_user_message = full_text[idx + len(marker):].strip()
    return hidden_context, actual_user_message


def extract_game_context_fields(hidden_context: str) -> Dict[str, str]:
    """
    Pull simple fields out of the hidden Unity context so downstream logic
    can use the real question/options instead of the whole raw blob.
    """
    fields = {
        "category": "",
        "difficulty": "",
        "question": "",
        "options": "",
    }

    if not hidden_context:
        return fields

    category_match = re.search(r"Category:\s*(.+)", hidden_context)
    difficulty_match = re.search(r"Difficulty:\s*(.+)", hidden_context)
    question_match = re.search(r"Question:\s*(.+)", hidden_context, re.DOTALL)
    options_match = re.search(
        r"Options:\s*(.+?)(?:\nPlayer balance:|\nLast outcome:|$)",
        hidden_context,
        re.DOTALL,
    )

    if category_match:
        fields["category"] = category_match.group(1).strip()

    if difficulty_match:
        fields["difficulty"] = difficulty_match.group(1).strip()

    if question_match:
        question_text = question_match.group(1).strip()
        question_text = question_text.split("\nOptions:")[0].strip()
        question_text = question_text.split("\nPlayer balance:")[0].strip()
        question_text = question_text.split("\nLast outcome:")[0].strip()
        fields["question"] = question_text

    if options_match:
        fields["options"] = options_match.group(1).strip()

    return fields


@app.get("/health")
def health() -> Dict[str, Any]:
    return {"ok": True, "app": "GMAT Solver v3 LIVE CHECK 777"}


@app.get("/", response_class=HTMLResponse)
def home() -> str:
    return HOME_HTML


@app.post("/chat")
async def chat(request: Request) -> JSONResponse:
    raw_body: Any = None

    try:
        raw_body = await request.json()
    except Exception:
        try:
            raw_body = (await request.body()).decode("utf-8", errors="ignore")
        except Exception:
            raw_body = None

    req_data: Dict[str, Any] = raw_body if isinstance(raw_body, dict) else {}

    try:
        req = ChatRequest(**req_data)
    except Exception:
        req = ChatRequest()

    full_text = get_user_text(req, raw_body)
    hidden_context, actual_user_message = split_unity_message(full_text)
    game_fields = extract_game_context_fields(hidden_context)

    # NEW
    context = retriever.search(actual_user_message)
    context_text = "\n".join(context)

    tone = clamp01(req_data.get("tone", req.tone), 0.5)
    verbosity = clamp01(req_data.get("verbosity", req.verbosity), 0.5)
    transparency = clamp01(req_data.get("transparency", req.transparency), 0.5)

    help_mode = detect_help_mode(
        actual_user_message,
        req_data.get("help_mode", req.help_mode),
    )

    result = generate_response(
        raw_user_text=actual_user_message,
        tone=tone,
        verbosity=verbosity,
        transparency=transparency,
        help_mode=help_mode,
        hidden_context=hidden_context,
        chat_history=req_data.get("chat_history", []),
        question_text=game_fields["question"],
        options_text=game_fields["options"],
        question_category=game_fields["category"],
        question_difficulty=game_fields["difficulty"],
        retrieval_context=context_text,
    )

    return JSONResponse(
        {
            "reply": result.reply,
            "meta": {
                "domain": result.domain,
                "solved": result.solved,
                "help_mode": result.help_mode,
                "answer_letter": result.answer_letter,
                "answer_value": result.answer_value,
            },
        }
    )


if __name__ == "__main__":
    import uvicorn

    port = int(os.getenv("PORT", "7860"))
    uvicorn.run(app, host="0.0.0.0", port=port)