# Configuration for HuggingFace Enabling Sessions

# Models
SENTIMENT_MODEL = "distilbert-base-uncased-finetuned-sst-2-english"
NER_MODEL = "dslim/bert-base-NER"
QA_MODEL = "deepset/roberta-base-squad2"
SUMMARIZATION_MODEL = "facebook/bart-large-cnn"
EMBEDDINGS_MODEL = "sentence-transformers/all-MiniLM-L6-v2"

# Task definitions
TASKS = {
    "sentiment": {
        "name": "Sentiment Analysis",
        "description": "Classify text sentiment (positive/negative/neutral)",
        "model": SENTIMENT_MODEL,
        "example": "I absolutely love this product! It's amazing and works perfectly.",
    },
    "ner": {
        "name": "Named Entity Recognition",
        "description": "Identify and classify named entities (Person, Location, Organization)",
        "model": NER_MODEL,
        "example": "Apple Inc. was founded by Steve Jobs in Cupertino, California.",
    },
    "qa": {
        "name": "Question Answering",
        "description": "Answer questions based on provided context",
        "model": QA_MODEL,
        "example_context": "The Hugging Face Hub is a platform for sharing machine learning models, datasets, and demos.",
        "example_question": "What is the Hugging Face Hub?",
    },
    "summarization": {
        "name": "Text Summarization",
        "description": "Generate concise summaries of longer texts",
        "model": SUMMARIZATION_MODEL,
        "example": "The Hugging Face transformers library provides state-of-the-art pre-trained models for natural language processing tasks. It supports PyTorch and TensorFlow, making it easy to use with either framework.",
    },
    "similarity": {
        "name": "Semantic Similarity",
        "description": "Compare semantic similarity between two sentences",
        "model": EMBEDDINGS_MODEL,
        "example1": "The cat is sleeping on the mat",
        "example2": "A feline is resting on the rug",
    },
}

# Sample data paths
SAMPLE_DATA_CSV = "data/sample_texts.csv"
DEMO_SAMPLES_DIR = "data/demo_samples"

# Session timings
SESSION1_DURATION = 45  # minutes
SESSION2_DURATION = 90  # minutes