xai_utils.py

import shap
import torch
import numpy as np
import matplotlib.pyplot as plt
from transformers import AutoModelForSequenceClassification, AutoTokenizer

def get_token_importance(model, tokenizer, text):
    """
    Calculates token importance using SHAP.
    """
    # Placeholder implementation
    return {"token_importance": "Not yet implemented."}

def get_attention_maps(model, tokenizer, text):
    """
    Generates attention heatmaps for a given text.
    """
    # Placeholder implementation
    return {"attention_maps": "Not yet implemented."}

def get_aspect_wise_explanation(shap_values, aspects):
    """
    Aggregates SHAP values by ontology aspect.
    """
    # Placeholder implementation
    return {"aspect_wise_explanation": "Not yet implemented."}

def get_confidence_calibration_diagram(model, texts, labels):
    """
    Creates a confidence calibration diagram.
    """
    # Placeholder implementation
    return {"confidence_calibration": "Not yet implemented."}

def run_xai_analysis(model_path, text1, text2):
    """
    Runs the full XAI analysis pipeline.
    """
    # tokenizer = AutoTokenizer.from_pretrained(model_path)
    # model = AutoModelForSequenceClassification.from_pretrained(model_path)

    # token_importance = get_token_importance(model, tokenizer, text1)
    # attention_maps = get_attention_maps(model, tokenizer, text1)

    # For now, returning placeholders
    analysis_results = {
        "token_importance": "Not yet implemented",
        "attention_maps": "Not yet implemented",
        "aspect_wise_explanation": "Not yet implemented.",
        "confidence_calibration": "Not yet implemented."
    }
    return analysis_results