from fastapi import APIRouter
from datetime import datetime
from datasets import load_dataset
from sklearn.metrics import accuracy_score
import numpy as np
import random
import os
import torch
from torch.utils.data import DataLoader

from .utils.evaluation import AudioEvaluationRequest
from .utils.emissions import tracker, clean_emissions_data, get_space_info
from data import FFTDataset
from models import DualEncoder
from train import Trainer
from data_utils import collate_fn, Container
import yaml

from dotenv import load_dotenv
load_dotenv()

router = APIRouter()

DESCRIPTION = "Random Baseline"
ROUTE = "/audio"



@router.post(ROUTE, tags=["Audio Task"],
             description=DESCRIPTION)
async def evaluate_audio(request: AudioEvaluationRequest):
    """
    Evaluate audio classification for rainforest sound detection.
    
    Current Model: Random Baseline
    - Makes random predictions from the label space (0-1)
    - Used as a baseline for comparison
    """
    # Get space info
    username, space_url = get_space_info()

    # Define the label mapping
    LABEL_MAPPING = {
        "chainsaw": 0,
        "environment": 1
    }
    # Load and prepare the dataset
    # Because the dataset is gated, we need to use the HF_TOKEN environment variable to authenticate
    dataset = load_dataset(request.dataset_name,token=os.getenv("HF_TOKEN"))
    
    # Split dataset
    train_test = dataset["train"].train_test_split(test_size=request.test_size, seed=request.test_seed)
    test_dataset = train_test["test"]

    # Start tracking emissions
    tracker.start()
    tracker.start_task("inference")

    #--------------------------------------------------------------------------------------------
    # YOUR MODEL INFERENCE CODE HERE
    # Update the code below to replace the random baseline by your model inference within the inference pass where the energy consumption and emissions are tracked.
    #--------------------------------------------------------------------------------------------
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    args_path = 'config.yaml'
    data_args = Container(**yaml.safe_load(open(args_path, 'r'))['Data'])
    model_args = Container(**yaml.safe_load(open(args_path, 'r'))['CNNEncoder'])
    model_args_f = Container(**yaml.safe_load(open(args_path, 'r'))['CNNEncoder_f'])
    conformer_args = Container(**yaml.safe_load(open(args_path, 'r'))['Conformer'])

    test_dataset = FFTDataset(test_dataset)
    test_dl = DataLoader(test_dataset, batch_size=data_args.batch_size, collate_fn=collate_fn)

    model = DualEncoder(model_args, model_args_f, conformer_args)
    model = model.to(device)
    missing, unexpected = model.load_state_dict(torch.load(model_args.checkpoint_path))

    loss_fn = torch.nn.BCEWithLogitsLoss()
    optimizer = torch.optim.Adam(model.parameters(), lr=5e-4)
    trainer = Trainer(model=model, optimizer=optimizer,
                      criterion=loss_fn, output_dim=model_args.output_dim, scaler=None,
                      scheduler=None, train_dataloader=None,
                      val_dataloader=None, device=device,
                      exp_num='test', log_path=None,
                      range_update=None,
                      accumulation_step=1, max_iter=np.inf,
                      exp_name=f"frugal_cnnencoder_inference")
    predictions, acc = trainer.predict(test_dl, device=device)
    # Make random predictions (placeholder for actual model inference)
    true_labels = test_dataset["label"]

    #--------------------------------------------------------------------------------------------
    # YOUR MODEL INFERENCE STOPS HERE
    #--------------------------------------------------------------------------------------------   
    
    # Stop tracking emissions
    emissions_data = tracker.stop_task()
    
    # Calculate accuracy
    accuracy = accuracy_score(true_labels, predictions)
    
    # Prepare results dictionary
    results = {
        "username": username,
        "space_url": space_url,
        "submission_timestamp": datetime.now().isoformat(),
        "model_description": DESCRIPTION,
        "accuracy": float(accuracy),
        "energy_consumed_wh": emissions_data.energy_consumed * 1000,
        "emissions_gco2eq": emissions_data.emissions * 1000,
        "emissions_data": clean_emissions_data(emissions_data),
        "api_route": ROUTE,
        "dataset_config": {
            "dataset_name": request.dataset_name,
            "test_size": request.test_size,
            "test_seed": request.test_seed
        }
    }
    
    return results