from fastapi import FastAPI, UploadFile, File, Form, HTTPException
from fastapi.middleware.cors import CORSMiddleware
from contextlib import asynccontextmanager
from typing import Dict, Optional

from models.base import BaseModelWrapper
from models.model_v1.wrapper import HybridDebertaWrapper
from models.model_v2.wrapper import ModelV2Wrapper
from models.model_v3.wrapper import MultimodalWrapper

AVAILABLE_MODELS: Dict[str, BaseModelWrapper] = {
    "Model V1": HybridDebertaWrapper(),
    "Model V2": ModelV2Wrapper(),
    "Model V3 (Multimodal)": MultimodalWrapper()
}

@asynccontextmanager
async def lifespan(app: FastAPI):
    print("Initializing available models...")
    for name, wrapper in AVAILABLE_MODELS.items():
        try:
            print(f"Loading {name}...")
            wrapper.load()
            print(f"{name} loaded successfully.")
        except Exception as e:
            print(f"Failed to load {name}: {e}")
    yield
    AVAILABLE_MODELS.clear()

app = FastAPI(lifespan=lifespan)

app.add_middleware(
    CORSMiddleware,
    allow_origins=["https://adtrack.onrender.com", "http://localhost:5173"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

@app.get("/models")
def list_models():
    return {"models": list(AVAILABLE_MODELS.keys())}

@app.post("/predict")
async def predict(
    model_name: str = Form(...),
    file: Optional[UploadFile] = File(None),       # .cha file (Optional)
    audio_file: Optional[UploadFile] = File(None), # Audio file (Optional)
    segmentation_file: Optional[UploadFile] = File(None) # Segmentation .csv (Optional)
):
    if model_name not in AVAILABLE_MODELS:
        raise HTTPException(status_code=404, detail=f"Model '{model_name}' not found")

    # --- Validation Logic ---
    if model_name in ["Model V1", "Model V2"]:
        if not file or not file.filename.endswith('.cha'):
             raise HTTPException(status_code=400, detail=f"{model_name} requires a .cha file.")
    
    if not file and not audio_file:
        raise HTTPException(status_code=400, detail="Please provide input files (CHA or Audio).")

    # --- Read Files ---
    text_content = b""
    filename = "upload"
    
    if file:
        text_content = await file.read()
        filename = file.filename
    elif audio_file:
        filename = audio_file.filename

    audio_content = None
    if audio_file:
        audio_content = await audio_file.read()

    segmentation_content = None
    if segmentation_file:
        segmentation_content = await segmentation_file.read()

    # --- Prediction ---
    try:
        # Pass all contents to the wrapper
        result = AVAILABLE_MODELS[model_name].predict(
            file_content=text_content, 
            filename=filename, 
            audio_content=audio_content,
            segmentation_content=segmentation_content
        )
        return result
    except ValueError as e:
        raise HTTPException(status_code=400, detail=str(e))
    except Exception as e:
        print(f"Prediction Error: {e}") 
        raise HTTPException(status_code=500, detail="Internal Server Error")

@app.get("/health")
def health_check():
    return {"status": "active", "loaded_models": list(AVAILABLE_MODELS.keys())}