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import nltk
import spacy
from transformers import pipeline
# Global models dictionary for persistent access
models = {
"nlp": None,
"sentiment_analyzer": None,
"emotion_classifier": None,
"summarizer": None,
"qa_pipeline": None,
"translation_pipeline": None,
"text_generator": None,
"zero_shot": None,
"embedding_model": None
}
def download_nltk_resources():
"""Download and initialize NLTK resources"""
resources = ['punkt', 'stopwords', 'vader_lexicon', 'wordnet', 'averaged_perceptron_tagger', 'sentiwordnet']
for resource in resources:
try:
if resource == 'punkt':
nltk.data.find(f'tokenizers/{resource}')
elif resource in ['stopwords', 'wordnet']:
nltk.data.find(f'corpora/{resource}')
elif resource == 'vader_lexicon':
nltk.data.find(f'sentiment/{resource}')
elif resource == 'averaged_perceptron_tagger':
nltk.data.find(f'taggers/{resource}')
elif resource == 'sentiwordnet':
nltk.data.find(f'corpora/{resource}')
except LookupError:
print(f"Downloading required NLTK resource: {resource}")
nltk.download(resource)
def load_spacy():
"""Load spaCy model"""
if models["nlp"] is None:
try:
models["nlp"] = spacy.load("en_core_web_sm")
except:
print("SpaCy model not found. Please run: python -m spacy download en_core_web_sm")
return models["nlp"]
def load_sentiment_analyzer():
"""Load sentiment analysis model"""
if models["sentiment_analyzer"] is None:
try:
models["sentiment_analyzer"] = pipeline("sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english")
except Exception as e:
print(f"Failed to load sentiment analyzer: {e}")
return models["sentiment_analyzer"]
def load_emotion_classifier():
"""Load emotion classification model"""
if models["emotion_classifier"] is None:
try:
models["emotion_classifier"] = pipeline(
"text-classification",
model="cardiffnlp/twitter-roberta-base-emotion",
return_all_scores=True
)
except Exception as e:
print(f"Failed to load emotion classifier: {e}")
return models["emotion_classifier"]
def load_summarizer():
"""Load summarization model"""
if models["summarizer"] is None:
try:
models["summarizer"] = pipeline("summarization", model="facebook/bart-large-cnn")
except Exception as e:
print(f"Failed to load summarizer: {e}")
return models["summarizer"]
def load_qa_pipeline():
"""Load or initialize the question answering pipeline."""
if models["qa_pipeline"] is None:
try:
from transformers import pipeline
# Use a smaller model to reduce memory usage and improve speed
models["qa_pipeline"] = pipeline(
"question-answering",
model="deepset/roberta-base-squad2", # You can change this to a different model if needed
tokenizer="deepset/roberta-base-squad2"
)
except Exception as e:
print(f"Error loading QA pipeline: {e}")
models["qa_pipeline"] = None
raise e
return models["qa_pipeline"]
def load_translation_pipeline():
"""Load translation model"""
if models["translation_pipeline"] is None:
try:
models["translation_pipeline"] = pipeline("translation_en_to_fr", model="Helsinki-NLP/opus-mt-en-fr")
except Exception as e:
print(f"Failed to load translation model: {e}")
return models["translation_pipeline"]
def load_translator(source_lang="auto", target_lang="en"):
"""
Load a machine translation model for the given language pair.
Args:
source_lang (str): Source language code, or 'auto' for automatic detection
target_lang (str): Target language code
Returns:
A translation pipeline or model
"""
from transformers import pipeline, AutoModelForSeq2SeqLM, AutoTokenizer
try:
# For auto language detection, use a more general model
if source_lang == "auto":
# Using Helsinki-NLP's opus-mt model for translation
model_name = "Helsinki-NLP/opus-mt-mul-en" # Multilingual to English
translator = pipeline("translation", model=model_name)
else:
# For specific language pairs
model_name = f"Helsinki-NLP/opus-mt-{source_lang}-{target_lang}"
# Load the model and tokenizer
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Create the translation pipeline
translator = pipeline("translation", model=model, tokenizer=tokenizer)
return translator
except Exception as e:
# Fallback to a more general model if language pair isn't available
try:
# Use MarianMT model for many language pairs
model_name = "Helsinki-NLP/opus-mt-mul-en" # Multilingual to English
translator = pipeline("translation", model=model_name)
return translator
except Exception as nested_e:
# If all else fails, return a simple callable object that returns an error message
class ErrorTranslator:
def __call__(self, text, **kwargs):
return [{"translation_text": f"Error loading translation model: {str(e)}. Fallback also failed: {str(nested_e)}"}]
return ErrorTranslator()
def load_text_generator():
"""Load text generation model"""
if models["text_generator"] is None:
try:
models["text_generator"] = pipeline("text-generation", model="gpt2")
except Exception as e:
print(f"Failed to load text generator: {e}")
return models["text_generator"]
def load_zero_shot():
"""Load zero-shot classification model"""
if models["zero_shot"] is None:
try:
models["zero_shot"] = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")
except Exception as e:
print(f"Failed to load zero-shot classifier: {e}")
return models["zero_shot"]
def load_embedding_model():
"""Load sentence embedding model for semantic search"""
if models.get("embedding_model") is None:
try:
from sentence_transformers import SentenceTransformer
models["embedding_model"] = SentenceTransformer('all-MiniLM-L6-v2')
except Exception as e:
print(f"Failed to load embedding model: {e}")
return models["embedding_model"]
def initialize_all_models():
"""Initialize all models for better performance"""
print("Initializing NLP models...")
# Download NLTK resources first
download_nltk_resources()
# Load spaCy model
try:
load_spacy()
print("✓ spaCy model loaded")
except Exception as e:
print(f"✗ Failed to load spaCy: {e}")
# Load transformer models (these might take time)
models_to_load = [
("Sentiment Analyzer", load_sentiment_analyzer),
("Emotion Classifier", load_emotion_classifier),
("Summarizer", load_summarizer),
("QA Pipeline", load_qa_pipeline),
("Text Generator", load_text_generator),
("Zero-shot Classifier", load_zero_shot),
("Embedding Model", load_embedding_model)
]
for name, loader_func in models_to_load:
try:
loader_func()
print(f"✓ {name} loaded")
except Exception as e:
print(f"✗ Failed to load {name}: {e}")
print("Model initialization complete!")
def get_model_status():
"""Get status of all models"""
status = {}
for model_name, model in models.items():
status[model_name] = model is not None
return status
def clear_models():
"""Clear all loaded models to free memory"""
for key in models:
models[key] = None
print("All models cleared from memory")
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