from __future__ import annotations

import os
from typing import List

import numpy as np
import pandas as pd
from PIL import Image

import torch
import torch.nn as nn
import torchvision.transforms.v2 as T

from clearml import InputModel, Task

from huggingface_hub import hf_hub_download

from src.models.cnn_model import PlantCNN
from src.models.resnet18_finetune import make_resnet18

from src.utils.class_names import CLASS_NAMES


_MODEL_CACHE = {}
_CLASS_NAMES_CACHE = None


def _device_key(device: torch.device) -> str:
    return str(device)


def _get_device() -> torch.device:
    if torch.cuda.is_available():
        return torch.device("cuda")
    if getattr(torch.backends, "mps", None) and torch.backends.mps.is_available():
        return torch.device("mps")
    return torch.device("cpu")


def _build_val_transform(image_size: int = 256) -> T.Compose:
    return T.Compose([
        T.Resize((image_size, image_size)),
        T.ToImage(),
        T.ToDtype(torch.float32, scale=True),
    ])


def _load_model_from_checkpoint(
    model_path: str,
    num_classes: int,
    model_type: str,
    device: torch.device,
) -> nn.Module:
    if not os.path.isfile(model_path):
        raise FileNotFoundError(f"Model file not found - {model_path}")

    ckpt = torch.load(model_path, map_location=device)

    if model_type.lower() == "resnet18":
        model = make_resnet18(num_classes=num_classes)
    elif model_type.lower() == "cnn":
        model = PlantCNN(num_classes=num_classes)
    else:
        raise ValueError(f"Unknown model type - {model_type}. Must be 'resnet18' or 'cnn'.")

    if isinstance(ckpt, dict) and "state_dict" in ckpt:
        model.load_state_dict(ckpt["state_dict"])
    elif isinstance(ckpt, nn.Module):
        model = ckpt
    else:
        try:
            model.load_state_dict(ckpt)
        except Exception:
            raise ValueError(f"Unexpected checkpoint format in - {model_path}. ")

    model.to(device)
    model.eval()
    
    return model


def _load_model_from_clearml_model_id(
    model_id: str,
    num_classes: int,
    model_type: str,
    device: torch.device,
) -> nn.Module:

    model_obj = InputModel(model_id=model_id)
    downloaded_path = model_obj.get_local_copy()
    
    if downloaded_path is None:
        raise FileNotFoundError(f"Failed to download model from ClearML Model ID - {model_id}")

    if os.path.isdir(downloaded_path):
        model_files = [f for f in os.listdir(downloaded_path) if f.endswith((".pt", ".pth"))]
        if model_files:
            model_path = os.path.join(downloaded_path, model_files[0])
        else:
            for name in ["best_baseline.pt", "best_model.pt", "best_baseline.pth", "best_model.pth"]:
                candidate = os.path.join(downloaded_path, name)
                if os.path.isfile(candidate):
                    model_path = candidate
                    break
            if model_path is None:
                raise FileNotFoundError(f"No model file found in directory - {downloaded_path}")
    else:
        model_path = downloaded_path

    if model_type.lower() == "resnet18":
        model = make_resnet18(num_classes=num_classes)
    elif model_type.lower() == "cnn":
        model = PlantCNN(num_classes=num_classes)
    else:
        raise ValueError(f"Unknown model type - {model_type}. Must be 'resnet18' or 'cnn'.")

    state_dict = torch.load(model_path, map_location=device)
    if isinstance(state_dict, dict) and "state_dict" in state_dict:
        state_dict = state_dict["state_dict"]

    model.load_state_dict(state_dict)
    model.to(device)
    model.eval()

    return model


def _load_model_from_clearml_task_id(
    task_id: str,
    num_classes: int,
    model_type: str,
    device: torch.device,
) -> nn.Module:

    source_task = Task.get_task(task_id=task_id)
    artifact_names = ["best_model", "best_baseline", "model"]
    model_path = None
    
    for artifact_name in artifact_names:
        if artifact_name in source_task.artifacts:
            model_path = source_task.artifacts[artifact_name].get_local_copy()
            if model_path:
                break
    
    if model_path is None:
        raise FileNotFoundError(f"No model artifact found in Task ID - {task_id}")

    if model_type.lower() == "resnet18":
        model = make_resnet18(num_classes=num_classes)
    elif model_type.lower() == "cnn":
        model = PlantCNN(num_classes=num_classes)
    else:
        raise ValueError(f"Unknown model type - {model_type}. Must be 'resnet18' or 'cnn'.")

    state_dict = torch.load(model_path, map_location=device)
    if isinstance(state_dict, dict) and "state_dict" in state_dict:
        state_dict = state_dict["state_dict"]

    model.load_state_dict(state_dict)
    model.to(device)
    model.eval()

    return model


def _load_model_from_huggingface(
    repo_id: str,
    filename: str,
    num_classes: int,
    model_type: str,
    device: torch.device,
) -> nn.Module:

    model_path = hf_hub_download(repo_id=repo_id, filename=filename, repo_type="model")

    if model_type.lower() == "resnet18":
        model = make_resnet18(num_classes=num_classes)
    elif model_type.lower() == "cnn":
        model = PlantCNN(num_classes=num_classes)
    else:
        raise ValueError(f"Unknown model type - {model_type}. Must be 'resnet18' or 'cnn'.")

    state_dict = torch.load(model_path, map_location=device)
    if isinstance(state_dict, dict) and "state_dict" in state_dict:
        state_dict = state_dict["state_dict"]

    model.load_state_dict(state_dict)
    model.to(device)
    model.eval()

    return model



def _get_class_names() -> List[str]:
    return CLASS_NAMES


def predict_image(img: Image.Image, k: int = 5) -> pd.DataFrame:
    """
    Predict top-k for a single PIL image.
    Returns a DataFrame with columns: Img, Rank, Disease, Probability, Model
    """
    if img is None:
        return pd.DataFrame({"Disease": [], "Probability": []})

    try:
        class_names = _get_class_names()
        if not class_names:
            raise ValueError("class_names list is empty.")

        model_type = os.getenv("MODEL_TYPE", "resnet18")
        model_path = os.getenv("MODEL_PATH", "")
        clearml_model_id = os.getenv("CLEARML_MODEL_ID", "")
        clearml_task_id = os.getenv("CLEARML_TASK_ID", "")
        hf_repo_id = os.getenv("HF_REPO_ID", "")
        hf_filename = os.getenv("HF_FILENAME", "")

        device = _get_device()
        device_k = _device_key(device)
        num_classes = len(class_names)
        transform = _build_val_transform(image_size=256)

        x = transform(img.convert("RGB")).unsqueeze(0).to(device)

        model = None

        #  ClearML Model ID
        if clearml_model_id and clearml_model_id.strip():
            cache_key = ("clearml_model", model_type, clearml_model_id, num_classes, device_k)
            if cache_key not in _MODEL_CACHE:
                try:
                    _MODEL_CACHE[cache_key] = _load_model_from_clearml_model_id(clearml_model_id, num_classes, model_type, device)
                except Exception:
                    _MODEL_CACHE[cache_key] = None
            model = _MODEL_CACHE.get(cache_key)

        # ClearML Task ID
        if model is None and clearml_task_id and clearml_task_id.strip():
            cache_key = ("clearml_task", model_type, clearml_task_id, num_classes, device_k)
            if cache_key not in _MODEL_CACHE:
                try:
                    _MODEL_CACHE[cache_key] = _load_model_from_clearml_task_id(clearml_task_id, num_classes, model_type, device)
                except Exception:
                    _MODEL_CACHE[cache_key] = None
            model = _MODEL_CACHE.get(cache_key)

        # Hugging Face
        if model is None and hf_repo_id and hf_repo_id.strip() and hf_filename and hf_filename.strip():
            cache_key = ("huggingface", model_type, hf_repo_id, hf_filename, num_classes, device_k)
            if cache_key not in _MODEL_CACHE:
                try:
                    _MODEL_CACHE[cache_key] = _load_model_from_huggingface(hf_repo_id, hf_filename, num_classes, model_type, device)
                except Exception:
                    _MODEL_CACHE[cache_key] = None
            model = _MODEL_CACHE.get(cache_key)

        # Local checkpoint
        if model is None:
            if model_path and os.path.isfile(model_path):
                cache_key = ("local", model_type, model_path, num_classes, device_k)
                if cache_key not in _MODEL_CACHE:
                    _MODEL_CACHE[cache_key] = _load_model_from_checkpoint(model_path, num_classes, model_type, device)
                model = _MODEL_CACHE[cache_key]
            else:
                raise FileNotFoundError(
                    f"All loading methods failed. Model ID - {clearml_model_id}, Task ID - {clearml_task_id}, HF - {hf_repo_id}/{hf_filename}, Local path - {model_path}"
                )


        with torch.no_grad():
            logits = model(x)
            probs = torch.softmax(logits, dim=1)[0]

        topk = min(int(k), len(class_names))
        top_probs, top_indices = torch.topk(probs, k=topk)

        results = [
            (class_names[idx.item()], float(prob.item()))
            for prob, idx in zip(top_probs, top_indices)
        ]

        return pd.DataFrame({
            "Disease": [r[0] for r in results],
            "Probability": [r[1] for r in results],
        })

    except Exception as e:
        return pd.DataFrame({"Disease": [f"Error: {str(e)}"], "Probability": [0.0]})