from fastapi import APIRouter
from datetime import datetime
from datasets import load_dataset
from sklearn.metrics import accuracy_score
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
from torch.utils.data import Dataset, DataLoader

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

router = APIRouter()

DESCRIPTION = "Climate Guard Toxic Agent Model"
ROUTE = "/text"

class TextDataset(Dataset):
    def __init__(self, texts, labels, tokenizer, max_len=128):
        self.texts = texts
        self.labels = labels
        self.tokenizer = tokenizer
        self.max_len = max_len

    def __len__(self):
        return len(self.texts)

    def __getitem__(self, idx):
        text = str(self.texts[idx])
        label = self.labels[idx]
        
        encoding = self.tokenizer(
            text,
            max_length=self.max_len,
            padding='max_length',
            truncation=True,
            return_tensors="pt"
        )
        
        return {
            'input_ids': encoding['input_ids'].squeeze(0),
            'attention_mask': encoding['attention_mask'].squeeze(0),
            'labels': torch.tensor(label, dtype=torch.long)
        }

@router.post(ROUTE, tags=["Text Task"], description=DESCRIPTION)
async def evaluate_text(request: TextEvaluationRequest):
    """
    Evaluate text classification for climate disinformation detection.
    """
    username, space_url = get_space_info()

    # 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 dataset
    dataset = load_dataset(request.dataset_name)
    
    # Convert string labels to integers
    dataset = dataset.map(lambda x: {"label": LABEL_MAPPING[x["label"]]})
    
    # Get test dataset
    test_dataset = dataset["test"]
    
    # Start tracking emissions
    tracker.start()
    
    try:
        # Load model and tokenizer
        model_name = "Tonic/climate-guard-toxic-agent"
        tokenizer = AutoTokenizer.from_pretrained(model_name)
        model = AutoModelForSequenceClassification.from_pretrained(model_name)
        
        # Prepare dataset
        test_data = TextDataset(
            texts=test_dataset["text"],
            labels=test_dataset["label"],
            tokenizer=tokenizer
        )
        
        test_loader = DataLoader(test_data, batch_size=16)
        
        # Model inference
        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        model = model.to(device)
        model.eval()
        
        predictions = []
        ground_truth = []
        
        with torch.no_grad():
            for batch in test_loader:
                input_ids = batch['input_ids'].to(device)
                attention_mask = batch['attention_mask'].to(device)
                labels = batch['labels'].to(device)
                
                outputs = model(input_ids=input_ids, attention_mask=attention_mask)
                _, predicted = torch.max(outputs.logits, 1)
                
                predictions.extend(predicted.cpu().numpy())
                ground_truth.extend(labels.cpu().numpy())
        
        # Calculate accuracy
        accuracy = accuracy_score(ground_truth, predictions)
        
        # Stop tracking emissions
        emissions_data = tracker.stop()
        
        # Prepare results
        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
        
    except Exception as e:
        tracker.stop()
        raise e