tasks/text.py

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, RobertaForSequenceClassification
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 = "RoBERTa Climate Disinformation Classifier"
ROUTE = "/text"

class FrugalDataClass(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]

        encodings = self.tokenizer(
            text,
            max_length=self.max_len,
            padding='max_length',
            truncation=True,
            return_tensors="pt"
        )

        return {
            'input_ids': encodings['input_ids'].flatten(),
            'attention_mask': encodings['attention_mask'].flatten(),
            'labels': torch.tensor(label, dtype=torch.long)
        }

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
tokenizer = AutoTokenizer.from_pretrained("roberta-base")
model = RobertaForSequenceClassification.from_pretrained(
    "roberta-base",
    num_labels=8
)
model.load_state_dict(torch.load('tasks/best_roberta_model.pth', map_location=device))
model.to(device)
model.eval()

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

    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
    }

    dataset = load_dataset(request.dataset_name)

    dataset = dataset.map(lambda x: {"label": LABEL_MAPPING[x["label"]]})

    train_test = dataset["train"].train_test_split(test_size=request.test_size, seed=request.test_seed)
    test_dataset = train_test["test"]
    
    tracker.start()
    tracker.start_task("inference")

    test_texts = test_dataset["quote"]
    true_labels = test_dataset["label"]
    
    test_dataset = FrugalDataClass(test_texts, true_labels, tokenizer)
    test_loader = DataLoader(test_dataset, batch_size=16, shuffle=False)
    
    predictions = []
    with torch.no_grad():
        for batch in test_loader:
            input_ids = batch['input_ids'].to(device)
            attention_mask = batch['attention_mask'].to(device)
            outputs = model(input_ids, attention_mask=attention_mask)
            preds = torch.argmax(outputs.logits, dim=1).cpu().numpy()
            predictions.extend(preds)
    
    emissions_data = tracker.stop_task()
    
    accuracy = accuracy_score(true_labels, predictions)
    
    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