from fastapi import FastAPI, Query
from pydantic import BaseModel
from typing import List, Optional
from src.ontology.matcher import ConceptMatcher
from src.parser.parser import Parser
from src.enrichment.enricher import Enricher
from src.prompt_builder.builder import PromptBuilder
from src.embeddings.engine import EmbeddingEngine
from src.ontology.models import PromptIR

from src.knowledge.engine import KnowledgeEngine

from src.runtime.onnx_runtime import ONNXEmbeddingEngine

app = FastAPI(title="Prompt Compiler API v3.3")

# Initialize components
ONTOLOGY_DIR = "data/ontology"
INDEX_PATH = "data/faiss_indices_onnx"
KNOWLEDGE_DB = "data/knowledge/build/knowledge.db"

matcher = ConceptMatcher(ONTOLOGY_DIR)
knowledge_engine = KnowledgeEngine(KNOWLEDGE_DB)
embedding_engine = ONNXEmbeddingEngine(index_dir=INDEX_PATH)
embedding_engine.load_index()

class CompileRequest(BaseModel):
    text: str
    safe_mode: bool = True
    semantic: bool = True
    profile: str = "generic"
    language: str = "es" # Default to 'es' to enable the Always-English translator

class CompileResponse(BaseModel):
    ir: PromptIR
    prompt: dict

@app.post("/compile", response_model=CompileResponse)
async def compile_prompt(request: CompileRequest):
    # Select parser based on semantic flag and language
    # We create a new parser instance or use a cached one to handle the translation state
    current_parser = Parser(matcher, embedding_engine=embedding_engine, language=request.language)
    enricher = Enricher(matcher, knowledge_engine=knowledge_engine)
    builder = PromptBuilder(profile_name=request.profile)
    
    ir = current_parser.parse(request.text, safe_mode=request.safe_mode)
    ir = enricher.enrich(ir)
    prompt = builder.build(ir)
    
    return CompileResponse(ir=ir, prompt=prompt)

@app.get("/health")
async def health():
    return {"status": "ok"}