import torch from transformers import AutoTokenizer, AutoModelForSequenceClassification, ModernBertConfig from typing import Dict, Any import yaml import os from models import ModernBertForSentiment class SentimentInference: def __init__(self, config_path: str = "config.yaml"): """Load configuration and initialize model and tokenizer from local checkpoint or Hugging Face Hub.""" print(f"--- Debug: SentimentInference __init__ received config_path: {config_path} ---") # Add this with open(config_path, 'r') as f: config_data = yaml.safe_load(f) print(f"--- Debug: SentimentInference loaded config_data: {config_data} ---") # Add this model_yaml_cfg = config_data.get('model', {}) inference_yaml_cfg = config_data.get('inference', {}) model_hf_repo_id = model_yaml_cfg.get('name_or_path') tokenizer_hf_repo_id = model_yaml_cfg.get('tokenizer_name_or_path', model_hf_repo_id) local_model_weights_path = inference_yaml_cfg.get('model_path') # Path for local .pt file print(f"--- Debug: model_hf_repo_id: {model_hf_repo_id} ---") # Add this print(f"--- Debug: local_model_weights_path: {local_model_weights_path} ---") # Add this self.max_length = inference_yaml_cfg.get('max_length', model_yaml_cfg.get('max_length', 512)) # --- Tokenizer Loading (always from Hub for now, or could be made conditional) --- if not tokenizer_hf_repo_id and not model_hf_repo_id: raise ValueError("Either model.tokenizer_name_or_path or model.name_or_path (as fallback for tokenizer) must be specified in config.yaml") effective_tokenizer_repo_id = tokenizer_hf_repo_id or model_hf_repo_id print(f"Loading tokenizer from: {effective_tokenizer_repo_id}") self.tokenizer = AutoTokenizer.from_pretrained(effective_tokenizer_repo_id) # --- Model Loading --- # # Determine if we are loading from a local .pt file or from Hugging Face Hub load_from_local_pt = False if local_model_weights_path and os.path.isfile(local_model_weights_path): print(f"Found local model weights path: {local_model_weights_path}") print(f"--- Debug: Found local model weights path: {local_model_weights_path} ---") # Add this load_from_local_pt = True elif not model_hf_repo_id: raise ValueError("No local model_path found and model.name_or_path (for Hub) is not specified in config.yaml") print(f"--- Debug: load_from_local_pt is: {load_from_local_pt} ---") # Add this if load_from_local_pt: print("Attempting to load model from local .pt checkpoint...") print("--- Debug: Entering LOCAL .pt loading path ---") # Add this # Base BERT config must still be loaded, usually from a Hub ID (e.g., original base model) # This base_model_for_config_id is crucial for building the correct ModernBertForSentiment structure. base_model_for_config_id = model_yaml_cfg.get('base_model_for_config', model_hf_repo_id or tokenizer_hf_repo_id) print(f"--- Debug: base_model_for_config_id (for local .pt): {base_model_for_config_id} ---") # Add this if not base_model_for_config_id: raise ValueError("For local .pt loading, model.base_model_for_config must be specified in config.yaml (e.g., 'answerdotai/ModernBERT-base') to build the model structure.") print(f"Loading ModernBertConfig for structure from: {base_model_for_config_id}") bert_config = ModernBertConfig.from_pretrained(base_model_for_config_id) # Augment config with parameters from model_yaml_cfg bert_config.pooling_strategy = model_yaml_cfg.get('pooling_strategy', 'mean') bert_config.num_weighted_layers = model_yaml_cfg.get('num_weighted_layers', 4) bert_config.classifier_dropout = model_yaml_cfg.get('dropout') bert_config.num_labels = model_yaml_cfg.get('num_labels', 1) # bert_config.loss_function = model_yaml_cfg.get('loss_function') # If needed by __init__ print("Instantiating ModernBertForSentiment model structure...") self.model = ModernBertForSentiment(bert_config) print(f"Loading model weights from local checkpoint: {local_model_weights_path}") checkpoint = torch.load(local_model_weights_path, map_location=torch.device('cpu')) if isinstance(checkpoint, dict) and 'model_state_dict' in checkpoint: model_state_to_load = checkpoint['model_state_dict'] else: model_state_to_load = checkpoint # Assume it's the state_dict itself self.model.load_state_dict(model_state_to_load) print(f"Model loaded successfully from local checkpoint: {local_model_weights_path}.") else: # Load from Hugging Face Hub print(f"Attempting to load model from Hugging Face Hub: {model_hf_repo_id}...") print(f"--- Debug: Entering HUGGING FACE HUB loading path ---") # Add this print(f"--- Debug: model_hf_repo_id (for Hub loading): {model_hf_repo_id} ---") # Add this if not model_hf_repo_id: raise ValueError("model.name_or_path must be specified in config.yaml for Hub loading.") print(f"Loading base ModernBertConfig from: {model_hf_repo_id}") loaded_config = ModernBertConfig.from_pretrained(model_hf_repo_id) # Augment loaded_config loaded_config.pooling_strategy = model_yaml_cfg.get('pooling_strategy', 'mean') loaded_config.num_weighted_layers = model_yaml_cfg.get('num_weighted_layers', 6) # Default to 6 now loaded_config.classifier_dropout = model_yaml_cfg.get('dropout') loaded_config.num_labels = model_yaml_cfg.get('num_labels', 1) print(f"Instantiating and loading model weights for {model_hf_repo_id}...") self.model = AutoModelForSequenceClassification.from_pretrained( model_hf_repo_id, config=loaded_config, trust_remote_code=True, force_download=True # <--- TEMPORARY - remove when everything is working ) print(f"Model {model_hf_repo_id} loaded successfully from Hugging Face Hub.") self.model.eval() def predict(self, text: str) -> Dict[str, Any]: inputs = self.tokenizer(text, return_tensors="pt", truncation=True, max_length=self.max_length, padding=True) with torch.no_grad(): outputs = self.model(input_ids=inputs['input_ids'], attention_mask=inputs['attention_mask']) logits = outputs.get("logits") # Use .get for safety if logits is None: raise ValueError("Model output did not contain 'logits'. Check model's forward pass.") prob = torch.sigmoid(logits).item() return {"sentiment": "positive" if prob > 0.5 else "negative", "confidence": prob}