# Updated NamedEntityRecognitionTool in ner_tool.py

from transformers import pipeline
from transformers import Tool

class NamedEntityRecognitionTool(Tool):
    name = "ner_tool"
    description = "Identifies and labels various entities in a given text."
    inputs = ["text"]
    outputs = ["text"]

    def __call__(self, text: str):
        # Initialize the named entity recognition pipeline
        ner_analyzer = pipeline("ner")

        # Perform named entity recognition on the input text
        entities = ner_analyzer(text)

        # Prepare a list to store word-level entities
        word_entities = []

        # Initialize variables to track the current word and its label
        current_word = ""
        current_label = None

        for entity in entities:
            label = entity.get("entity", "UNKNOWN")
            word = entity.get("word", "")
            start = entity.get("start", -1)
            end = entity.get("end", -1)

            # Extract the complete entity text
            entity_text = text[start:end].strip()

            # Check for multi-token entities
            if "##" in word:
                # Concatenate sub-tokens to form the complete word
                current_word += entity_text
                current_label = label
            else:
                # If it's the first token of a new word, add the previous word to the list
                if current_word:
                    word_entities.append({"word": current_word, "label": current_label, "entity_text": current_word})
                    current_word = ""
                    current_label = None

                # Add the current token as a new word
                word_entities.append({"word": word, "label": label, "entity_text": entity_text})

        # Check for any remaining word
        if current_word:
            word_entities.append({"word": current_word, "label": current_label, "entity_text": current_word})

        # Print the identified word-level entities
        print(f"Word-level Entities: {word_entities}")

        return {"entities": word_entities}  # Return a dictionary with the specified output component