Key improvements and explanations:

* **Clear Class Structure:** Encapsulated the hallucination detection logic within a `HallucinationDetector` class for better organization and reusability.
* **Knowledge Base Loading:** Implemented `_load_knowledge_base()` to load the knowledge base from a file.  Crucially, it now handles `FileNotFoundError` and other potential exceptions during file loading, printing a warning and continuing (though with a less effective detector). This makes the code more robust.  It also correctly handles UTF-8 encoding.  It now correctly parses the knowledge base file, splitting it into individual documents.
* **Preprocessing:** Added `_preprocess_text()` for text cleaning (lowercasing, removing punctuation, stop words, and lemmatization) to improve similarity comparisons.
* **Knowledge Base Verification:** Implemented `_knowledge_base_verification()` using cosine similarity to compare claims against the knowledge base. Includes a check for an empty knowledge base.
* **Self-Consistency Check:**  Implemented `_self_consistency_check()` to evaluate the internal consistency of the generated text. Handles cases with fewer than two sentences gracefully and avoids potential `IndexError`.  Critically, now calculates the average similarity correctly, avoiding division by zero.
* **Source Attribution:** Implemented `_source_attribution()` to compare claims against provided sources using cosine similarity. Returns 0 if no sources are provided, preventing errors.
* **Hallucination Classification:** Added `_classify_hallucination()` using a Hugging Face Transformers zero-shot classification pipeline (BART-large-mnli). This significantly improves hallucination detection performance.  Includes CUDA device selection for faster processing if a GPU is available.  Properly extracts the hallucination probability from the zero-shot classification results.
* **Hallucination Probability Calculation:** Combines scores from different methods to calculate an overall hallucination probability.  Includes weights that can be tuned.
* **Comprehensive Report:** Returns a dictionary-based report with flagged segments and confidence scores for each claim.  The `confidence_scores` dictionary now includes scores from all the methods.
* **Clearer Logic:**  Improved the clarity and readability of the code with better variable names and comments.
* **Error Handling:** Added more robust error handling, especially for file loading and knowledge base processing.
* **Complete Example:** The `if __name__ == '__main__':` block provides a complete, runnable example of how to use the `HallucinationDetector`. It creates a dummy `knowledge_base.txt` file, runs the analysis with and without sources, and prints the report. This makes it much easier to test and understand the code.
* **Dependency Management:**  The code now imports all necessary libraries (`re`, `nltk`, `sklearn`, `spacy`, `torch`, `transformers`) at the beginning, making it clear what dependencies are required.  It also includes `nltk.download('stopwords')` and `nltk.download('wordnet')` to ensure the necessary NLTK data is available.  These lines are commented out to avoid automatically downloading the data, which might not be desirable in all cases.
* **Comments and Docstrings:**  Added detailed comments and docstrings to explain the purpose of each function and class.

To run this code:

1.  **Install Dependencies:**