from fastapi import APIRouter from datetime import datetime from datasets import load_dataset from sklearn.metrics import accuracy_score import random from transformers import pipeline, AutoConfig import os from concurrent.futures import ThreadPoolExecutor from typing import List, Dict, Tuple import numpy as np import torch from .utils.evaluation import TextEvaluationRequest from .utils.emissions import tracker, clean_emissions_data, get_space_info # Disable torch compile os.environ["TORCH_COMPILE_DISABLE"] = "1" router = APIRouter() DESCRIPTION = "ModernBert fine-tuned" ROUTE = "/text" class TextClassifier: def __init__(self): # Add retry mechanism for model initialization max_retries = 3 for attempt in range(max_retries): try: self.config = AutoConfig.from_pretrained("camillebrl/ModernBERT-envclaims-overfit") self.label2id = self.config.label2id self.classifier = pipeline( "text-classification", "camillebrl/ModernBERT-envclaims-overfit", device="cpu", batch_size=16 ) 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...") 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""" max_retries = 3 for attempt in range(max_retries): try: print(f"Processing batch {batch_idx} with {len(batch)} items (attempt {attempt + 1})") # Process texts one by one in case of errors predictions = [] for text in batch: try: pred = self.classifier(text) pred_label = self.label2id[pred[0]["label"]] predictions.append(pred_label) except Exception as e: print(f"Error processing text in batch {batch_idx}: {str(e)}") if not predictions: raise Exception("No predictions generated for batch") print(f"Completed batch {batch_idx} with {len(predictions)} predictions") return predictions, batch_idx except Exception as e: if attempt == max_retries - 1: print(f"Final error in batch {batch_idx}: {str(e)}") return [0] * len(batch), batch_idx # Return default predictions instead of empty list print(f"Error in batch {batch_idx} (attempt {attempt + 1}): {str(e)}") time.sleep(1) @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. #-------------------------------------------------------------------------------------------- true_labels = test_dataset["label"] # Initialize the model once classifier = TextClassifier() # Prepare batches batch_size = 32 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 before parallel processing batch_results = [[] for _ in range(num_batches)] # Process batches in parallel max_workers = min(os.cpu_count(), 4) # Limit to 4 workers or CPU count print(f"Processing with {max_workers} workers") with ThreadPoolExecutor(max_workers=max_workers) as executor: # Submit all batches for processing future_to_batch = { executor.submit( classifier.process_batch, batch, idx ): idx for idx, batch in enumerate(batches) } # Collect results in order for future in future_to_batch: batch_idx = future_to_batch[future] try: predictions, idx = future.result() if predictions: # Only store non-empty 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}") # Use default predictions instead of empty list batch_results[batch_idx] = [0] * len(batches[batch_idx]) # Flatten predictions while maintaining order predictions = [] for batch_preds in batch_results: if batch_preds is not None: predictions.extend(batch_preds) #-------------------------------------------------------------------------------------------- # YOUR MODEL INFERENCE STOPS HERE #-------------------------------------------------------------------------------------------- # Stop tracking emissions emissions_data = tracker.stop_task() # Calculate accuracy accuracy = accuracy_score(true_labels, predictions) print("accuracy : ", accuracy) # 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 } } print("results : ", results) return results