tasks/text.py

import os
import gc

os.environ["CUDA_VISIBLE_DEVICES"]="0,1"

import torch
from tqdm import tqdm
from typing import Optional, Union
import pandas as pd, numpy as np, torch
from datasets import Dataset, load_dataset
from dataclasses import dataclass
from transformers import AutoTokenizer, AutoConfig, AutoModelForSequenceClassification, AutoModel
from transformers import EarlyStoppingCallback
from transformers.tokenization_utils_base import PreTrainedTokenizerBase, PaddingStrategy
from transformers import AutoModelForMultipleChoice, TrainingArguments, Trainer
import numpy as np
from sklearn.metrics import recall_score, accuracy_score
from transformers import DataCollatorWithPadding
import logging
# import mylib
logger = logging.getLogger(__name__)

VER = 1
MAX_LEN = 256
TOKENIZER_BINARY = "crarojasca/BinaryAugmentedCARDS"
BINARY_MODEL = "Medissa/Roberta_Binary"
TOKENIZER_MULTI_CLASS = "crarojasca/TaxonomyAugmentedCARDS"
MULTI_CLASS_MODEL = "Medissa/Deberta_Taxonomy"

ID2LABEL = {
 0: '1_not_happening',
 1: '2_not_human',
 2: '3_not_bad',
 3: '4_solutions_harmful_unnecessary',
 4: '7_fossil_fuels_needed',
 5: '5_science_unreliable',
 6: '6_proponents_biased'}

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

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

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")

    #--------------------------------------------------------------------------------------------
    # YOUR MODEL INFERENCE CODE HERE
    # Update the code below to replace the random baseline by your model inference within the inference pass where the energy consumption and emissions are tracked.
    #--------------------------------------------------------------------------------------------   
    print('Start Binary')
    # Binary Model
    tokenizer = AutoTokenizer.from_pretrained(BINARY_MODEL)
    print('Loaded Tokenizer')
    model = AutoModelForSequenceClassification.from_pretrained(BINARY_MODEL)
    print(device)
    model.to(device)
    model.eval()
    print('Loaded Model')
    predictions = []
    for i,text in tqdm(enumerate(test_dataset["quote"])):
        print(i)
        with torch.no_grad():        
            tokenized_text = tokenizer(text, truncation=True, padding='max_length', return_tensors = "pt")
            inputt = {k:v.to(device) for k,v in tokenized_text.items()}
            # Running Binary Model
            outputs = model(**inputt)
            binary_prediction = torch.argmax(outputs.logits, axis=1)
            binary_predictions = binary_prediction.to('cpu').item()
            
            prediction = "0_not_relevant" if binary_prediction==0 else 1
            predictions.append(prediction)
            
    gc.collect()

    ## 2. Taxonomy Model
    print('Start Multi')
    tokenizer = AutoTokenizer.from_pretrained(MULTI_CLASS_MODEL)
    print('Loaded Tokenizer')
    model = AutoModelForSequenceClassification.from_pretrained(MULTI_CLASS_MODEL)
    model.to(device)
    model.eval()
    print('Loaded Model')
    for i,text in tqdm(enumerate(test_dataset["quote"])):
        if isinstance(predictions[i], str):
            continue
        with torch.no_grad():        
            tokenized_text = tokenizer(text, truncation=True, padding='max_length', return_tensors = "pt")
            inputt = {k:v.to(device) for k,v in tokenized_text.items()}
            outputs = model(**inputt)
            taxonomy_prediction = torch.argmax(outputs.logits, axis=1)
            taxonomy_prediction = taxonomy_prediction.to('cpu').item()
            
            prediction = ID2LABEL[taxonomy_prediction]
            predictions[i] = prediction
        if i%10:
            print(f'iteration: {i}')
    predictions = [LABEL_MAPPING[pred] for pred in predictions]
    #--------------------------------------------------------------------------------------------
    # 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