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

#packages needed for inference
from sentence_transformers import SentenceTransformer
from xgboost import XGBClassifier
import pickle
import torch
import os

router = APIRouter()

DESCRIPTION = "Embedding + Neural Network"
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"].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

    # Set the device to MPS (if available)
    device = torch.device("mps" if torch.backends.mps.is_available() else "cpu")
    print(f"Using device: {device}")

    model_name = "sentence-transformers/paraphrase-multilingual-mpnet-base-v2"  # You can use other Sentence Transformers models as needed
    sentence_model = SentenceTransformer(model_name)

    # Convert each sentence into a vector representation (embedding)
    embeddings = sentence_model.encode(test_dataset['quote'], convert_to_tensor=True)

    # Make random predictions (placeholder for actual model inference)
    true_labels = test_dataset["label"]

    """
    from torch import nn, optim

    class SimpleNN2(nn.Module):
        def __init__(self, input_dim, output_dim):
            super(SimpleNN2, self).__init__()
            self.fc1 = nn.Linear(input_dim, 128)  # Reduce hidden units
            self.fc2 = nn.Linear(128, 64)  # Further reduce units
            self.fc3 = nn.Linear(64, output_dim)
            self.relu = nn.ReLU()
            self.dropout = nn.Dropout(0.3)  # Add dropout
            self.batch_norm1 = nn.BatchNorm1d(128)
            self.batch_norm2 = nn.BatchNorm1d(64)

        def forward(self, x):
            x = self.relu(self.batch_norm1(self.fc1(x)))
            x = self.dropout(x)  # Apply dropout
            x = self.relu(self.batch_norm2(self.fc2(x)))
            x = self.dropout(x)  # Apply dropout
            x = self.fc3(x)  # Output raw logits
            return x
    """

    current_file_path = os.path.abspath(__file__)
    current_dir = os.path.dirname(current_file_path)

    # model_nn = torch.load(os.path.join(current_dir,"model_nn.pth"), map_location=device)
    model_nn = torch.jit.load(os.path.join(current_dir,"model_nn_scripted.pth"), map_location=device)  
        

    # Set the model to evaluation mode
    model_nn.eval()

    # Make predictions
    with torch.no_grad():
        outputs = model_nn(embeddings)
        _, predicted = torch.max(outputs, 1)  # Get the class with the highest score

    # Decode the predictions back to original labels using label_encoder
    predictions = predicted.cpu().numpy()

    #--------------------------------------------------------------------------------------------
    # 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