from fastapi import APIRouter
from datetime import datetime
from datasets import load_dataset
from sklearn.metrics import accuracy_score
import random

from .utils.evaluation import TextEvaluationRequest
from .utils.emissions import tracker, clean_emissions_data, get_space_info

router = APIRouter()

DESCRIPTION = "Random Baseline"
ROUTE = "/text"

@router.post(ROUTE, tags=["Text Task"], 
             description=DESCRIPTION)
async def evaluate_text(request: TextEvaluationRequest):
    """
    Evaluate text classification for climate disinformation detection.
    
    Current Model: Random Baseline
    - Makes random predictions from the label space (0-7)
    - Used as a baseline for comparison
    """
    # Get space info
    username, space_url = get_space_info()

    # Define the label mapping
    LABEL_MAPPING = {
        "0_not_relevant": 0,
        "1_not_happening": 1,
        "2_not_human": 2,
        "3_not_bad": 3,
        "4_solutions_harmful_unnecessary": 4,
        "5_science_unreliable": 5,
        "6_proponents_biased": 6,
        "7_fossil_fuels_needed": 7
    }

    # Load and prepare the dataset
    dataset = load_dataset(request.dataset_name)

    # Convert string labels to integers
    dataset = dataset.map(lambda x: {"label": LABEL_MAPPING[x["label"]]})

    # Split dataset
    train_test = dataset["train"]
    test_dataset = dataset["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.
    #--------------------------------------------------------------------------------------------   
    
    # Make random predictions (placeholder for actual model inference)
    #true_labels = test_dataset["label"]
    #predictions = [random.randint(0, 7) for _ in range(len(true_labels))]

    from transformers import AutoModelForSequenceClassification, AutoTokenizer
    import torch
    from torch.utils.data import DataLoader
  
    # Load model and tokenizer from Hugging Face Hub
    MODEL_REPO = "ClimateDebunk/FineTunedDistilBert4SeqClass"

    tokenizer = AutoTokenizer.from_pretrained('distilbert-base-uncased', do_lower_case=True)
    MAX_LENGTH = 365

    model = AutoModelForSequenceClassification.from_pretrained(MODEL_REPO)
    #model.eval()  # Set to evaluation mode

    class QuotesDataset(Dataset):
        def __init__(self, encodings, labels):
            self.encodings = encodings
            self.labels = labels
    
        def __getitem__(self, idx):
            item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
            item['labels'] = torch.tensor(self.labels[idx], dtype=torch.long)
            return item
    
        def __len__(self):
            return len(self.labels)

    def encode_data(tokenizer, texts, labels, max_length):
        try:
            if isinstance(texts, pd.Series):
                texts = texts.tolist()
            if isinstance(labels, pd.Series):
                labels = labels.tolist()
                
            encodings = tokenizer(texts, truncation=True, padding='max_length', max_length=max_length, return_tensors='pt')
            return QuotesDataset(encodings, labels)
    
        except Exception as e:
            print(f"Error during tokenization: {e}")
            return None
            
    val_dataset = encode_data(tokenizer, test_dataset['quote'], test_dataset['label'], MAX_LENGTH)        
    val_loader = DataLoader(val_dataset, batch_size= batch_size, shuffle=False) 
  

    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    print(f"Using device: {device}")
    
    def validate_model(model, val_loader, device):
        model.eval()
        predictions = []
        with torch.no_grad():
            for batch in val_loader:
                batch = {k: v.to(device) for k, v in batch.items()}
                outputs = model(**batch)
                preds = torch.argmax(outputs.logits, dim=-1)
                predictions.extend(preds.cpu().numpy())
        return predictions

    
    # tokenize texts
    #test_encodings = tokenizer(test_dataset["quote"], padding='max_length', truncation=True, max_length=MAX_LENGTH, return_tensors="pt")
    #test_labels = torch.tensor(test_dataset["label"])

    #test_dataset = TensorDataset(test_encodings["input_ids"], test_encodings["attention_mask"], test_labels)
    #test_loader = DataLoader(test_dataset, batch_size=16)
    
    #predictions = []
    #with torch.no_grad():
    #for batch in test_loader:
    #    input_ids, attention_mask, labels = [x.to(device) for x in batch]
    #    outputs = model(input_ids, attention_mask=attention_mask)
    #   predictions = torch.argmax(outputs.logits, dim=1)

    predictions = validate_model(model, val_loader, device)
    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