tasks/text.py

from fastapi import APIRouter
from datetime import datetime
import time
from datasets import load_dataset
from sklearn.metrics import accuracy_score
import os
from concurrent.futures import ThreadPoolExecutor
from typing import List, Dict, Tuple
import torch
import torch.nn as nn
from transformers import AutoTokenizer, AutoModelForSequenceClassification, AutoConfig
from huggingface_hub import login
from dotenv import load_dotenv

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

# Load environment variables
load_dotenv()

# Authenticate with Hugging Face
HF_TOKEN = os.getenv('HUGGINGFACE_TOKEN')
if HF_TOKEN:
    login(token=HF_TOKEN)

# Disable torch compile
os.environ["TORCH_COMPILE_DISABLE"] = "1"

router = APIRouter()

DESCRIPTION = "ModernBERT Climate Claims Classifier"
ROUTE = "/text"

class TextClassifier:
    def __init__(self):
        self.device = "cuda" if torch.cuda.is_available() else "cpu"
        max_retries = 3
        model_name = "answerdotai/ModernBERT-base"
        
        for attempt in range(max_retries):
            try:
                # Load config with modified settings
                self.config = AutoConfig.from_pretrained(
                    model_name,
                    num_labels=8,
                    problem_type="single_label_classification",
                    trust_remote_code=True
                )
                
                # Remove problematic config attributes
                if hasattr(self.config, 'norm_bias'):
                    delattr(self.config, 'norm_bias')
                if hasattr(self.config, 'bias'):
                    delattr(self.config, 'bias')
                
                # Initialize tokenizer
                self.tokenizer = AutoTokenizer.from_pretrained(
                    model_name,
                    model_max_length=8192,
                    padding_side='right',
                    truncation_side='right',
                    trust_remote_code=True
                )
                
                # Initialize model with modified config
                self.model = AutoModelForSequenceClassification.from_pretrained(
                    model_name,
                    config=self.config,
                    trust_remote_code=True,
                    torch_dtype=torch.float32,
                    ignore_mismatched_sizes=True
                )
                
                # Move model to appropriate device
                self.model = self.model.to(self.device)
                self.model.eval()
                print("Model initialized successfully")
                break
                
            except Exception as e:
                if attempt == max_retries - 1:
                    raise Exception(f"Failed to initialize model after {max_retries} attempts: {str(e)}")
                print(f"Attempt {attempt + 1} failed, retrying... Error: {str(e)}")
                time.sleep(1)

    def process_batch(self, batch: List[str], batch_idx: int) -> Tuple[List[int], int]:
        """Process a batch of texts and return their predictions"""
        try:
            print(f"Processing batch {batch_idx} with {len(batch)} items")
            
            # Tokenize with padding and truncation
            inputs = self.tokenizer(
                batch,
                return_tensors="pt",
                truncation=True,
                max_length=512,
                padding=True
            )
            
            # Move inputs to device
            inputs = {k: v.to(self.device) for k, v in inputs.items()}
            
            # Get predictions
            with torch.no_grad():
                outputs = self.model(**inputs)
                predictions = torch.argmax(outputs.logits, dim=-1).cpu().tolist()
            
            print(f"Completed batch {batch_idx} with {len(predictions)} predictions")
            return predictions, batch_idx
            
        except Exception as e:
            print(f"Error in batch {batch_idx}: {str(e)}")
            return [0] * len(batch), batch_idx

    def __del__(self):
        # Clean up CUDA memory
        if hasattr(self, 'model'):
            del self.model
        if torch.cuda.is_available():
            torch.cuda.empty_cache()
    

@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
    }

    try:
        # Load and prepare the dataset
        dataset = load_dataset("QuotaClimat/frugalaichallenge-text-train", token=HF_TOKEN)
        
        # Convert string labels to integers with error handling
        def convert_label(example):
            try:
                return {"label": LABEL_MAPPING[example["label"]]}
            except KeyError as e:
                print(f"Warning: Unknown label {example['label']}")
                # Return default label or raise exception
                return {"label": 0}  # or raise e if you want to fail on unknown labels
                
        dataset = dataset.map(convert_label)

        # Split dataset
        test_dataset = dataset["test"]
        
        # Start tracking emissions
        tracker.start()
        tracker.start_task("inference")

        true_labels = test_dataset["label"]
        
        # Initialize the model once
        classifier = TextClassifier()

        # Prepare batches
        batch_size = 24
        quotes = test_dataset["quote"]
        num_batches = len(quotes) // batch_size + (1 if len(quotes) % batch_size != 0 else 0)
        batches = [
            quotes[i * batch_size:(i + 1) * batch_size]
            for i in range(num_batches)
        ]

        # Initialize batch_results
        batch_results = [[] for _ in range(num_batches)]
        
        # Process batches in parallel
        max_workers = min(os.cpu_count(), 4)
        print(f"Processing with {max_workers} workers")
        
        with ThreadPoolExecutor(max_workers=max_workers) as executor:
            future_to_batch = {
                executor.submit(classifier.process_batch, batch, idx): idx 
                for idx, batch in enumerate(batches)
            }

            for future in future_to_batch:
                batch_idx = future_to_batch[future]
                try:
                    predictions, idx = future.result()
                    if predictions:
                        batch_results[idx] = predictions
                        print(f"Stored results for batch {idx} ({len(predictions)} predictions)")
                except Exception as e:
                    print(f"Failed to get results for batch {batch_idx}: {e}")
                    batch_results[batch_idx] = [0] * len(batches[batch_idx])

        # Flatten predictions
        predictions = []
        for batch_preds in batch_results:
            if batch_preds is not None:
                predictions.extend(batch_preds)
        
        # Stop tracking emissions
        emissions_data = tracker.stop_task()
        
        # Calculate accuracy
        accuracy = accuracy_score(true_labels, predictions)
        print("accuracy:", accuracy)
        
        # Prepare results
        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
            }
        }

        print("results:", results)
        return results

    except Exception as e:
        print(f"Error in evaluate_text: {str(e)}")
        raise Exception(f"Failed to process request: {str(e)}")