tasks/text.py

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

    # Load the model and tokenizer
    model_name = "Tonic/climate-guard-toxic-agent"
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    model = AutoModelForSequenceClassification.from_pretrained(model_name)

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

    # Create dataset and dataloader
    test_dataset = TextDataset(texts, labels, tokenizer)
    test_loader = DataLoader(test_dataset, batch_size=16)

    # Model inference
    model.eval()
    predictions = []
    ground_truth = []
    DEVICE = 'cpu'
    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())

    #--------------------------------------------------------------------------------------------
    # YOUR MODEL INFERENCE STOPS HERE
    #--------------------------------------------------------------------------------------------   

    
    # Stop tracking emissions
    emissions_data = tracker.stop_task()
    
    # Calculate accuracy
    accuracy = accuracy_score(ground_truth, 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