tasks/text.py

from fastapi import APIRouter
from datetime import datetime
from datasets import load_dataset
from sklearn.metrics import accuracy_score
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import Pipeline
import numpy as np

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

router = APIRouter()

DESCRIPTION = "Climate Disinformation Detection - TF-IDF + LogReg"
ROUTE = "/text"

def create_pipeline():
    """Create an efficient text classification pipeline"""
    return Pipeline([
        ('tfidf', TfidfVectorizer(
            max_features=10000,  # Limit features for efficiency
            ngram_range=(1, 2),  # Use unigrams and bigrams
            stop_words='english',
            min_df=2,  # Remove very rare terms
            max_df=0.95  # Remove very common terms
        )),
        ('classifier', LogisticRegression(
            C=1.0,
            multi_class='multinomial',
            max_iter=200,
            n_jobs=-1  # Use all CPU cores
        ))
    ])

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

    # Start tracking emissions
    tracker.start()
    tracker.start_task("inference")

    try:
        # 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
        )
        
        train_dataset = train_test["train"]
        test_dataset = train_test["test"]

        # Create and train pipeline
        pipeline = create_pipeline()
        
        # Train the model
        pipeline.fit(
            train_dataset["quote"],
            train_dataset["label"]
        )
        
        # Make predictions
        predictions = pipeline.predict(test_dataset["quote"])
        
        # Get true labels
        true_labels = test_dataset["label"]

        # 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
        
    except Exception as e:
        # Stop tracking in case of error
        tracker.stop_task()
        raise e