AI-powered AI Code Explainer Python, NLP, AI

```python
import nltk
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity

# Download necessary NLTK resources (if you haven't already)
# nltk.download('punkt')
# nltk.download('stopwords')


def ai_code_explainer(code_snippet, query):
    """
    Explains a Python code snippet using NLP techniques.

    Args:
        code_snippet (str): The Python code snippet to explain.
        query (str): A question or description about the code snippet that the user wants explained.

    Returns:
        str: An explanation of the code snippet based on the query.
    """

    # 1. Preprocess the code snippet and the query
    code_tokens = preprocess_text(code_snippet)
    query_tokens = preprocess_text(query)

    # 2. Create a vocabulary and calculate TF-IDF scores
    corpus = [code_tokens, query_tokens]  # Combine code and query into a single corpus
    vectorizer = TfidfVectorizer()
    tfidf_matrix = vectorizer.fit_transform(corpus)  # Learn vocabulary & calculate IDF on corpus

    # 3. Calculate Cosine Similarity between the code and the query
    code_vector = tfidf_matrix[0]
    query_vector = tfidf_matrix[1]
    similarity_score = cosine_similarity(code_vector, query_vector)[0][0]

    # 4. Generate an explanation based on the similarity score.  This is a simplified example.
    if similarity_score > 0.2:  # Threshold to determine if the query is relevant
        explanation = generate_explanation(code_snippet, query)
    else:
        explanation = "The query doesn't seem directly relevant to the code snippet provided."

    return explanation


def preprocess_text(text):
    """
    Preprocesses text by tokenizing, removing stop words, and lowercasing.

    Args:
        text (str): The text to preprocess.

    Returns:
        str: A string of processed tokens.
    """
    tokens = word_tokenize(text)
    stop_words = set(stopwords.words('english'))
    filtered_tokens = [w.lower() for w in tokens if w.isalpha() and w.lower() not in stop_words]  # Remove punctuation too
    return " ".join(filtered_tokens)


def generate_explanation(code_snippet, query):
    """
    Generates a basic explanation of the code snippet.  **This is where a more advanced model would go.**
    This is a very rudimentary implementation, and in a real application, you would
    use a more sophisticated approach like a language model (e.g., GPT-3, Llama, CodeT5)
    or a rule-based system to generate a better explanation.

    Args:
        code_snippet (str): The code snippet to explain.
        query (str): The query related to the code.

    Returns:
        str: A simple explanation of the code.
    """

    # Example implementation: simple keyword-based explanation
    explanation = f"Here's a basic explanation of the code based on your query:\n"
    if "loop" in query.lower() or "iterate" in query.lower():
        if "for" in code_snippet:
            explanation += "The code uses a 'for' loop to iterate over a sequence of values.\n"
        elif "while" in code_snippet:
            explanation += "The code uses a 'while' loop to repeat a block of code until a condition is met.\n"
        else:
            explanation += "The code might involve some kind of iteration, but it doesn't use a standard 'for' or 'while' loop.\n"

    if "function" in query.lower() or "define" in query.lower():
        if "def" in code_snippet:
            explanation += "The code defines a function using the 'def' keyword.\n"
        else:
            explanation += "The code doesn't seem to define any explicit function.\n"

    if "variable" in query.lower() or "assign" in query.lower():
        explanation += "The code likely involves assigning values to variables.\n"

    explanation += "Please note that this is a simplified explanation. A more sophisticated AI-powered explanation would require a more advanced NLP model."
    return explanation


# Example Usage:
code = """
def calculate_sum(numbers):
    total = 0
    for number in numbers:
        total += number
    return total

my_list = [1, 2, 3, 4, 5]
result = calculate_sum(my_list)
print(f"The sum is: {result}")
"""

query = "What does this code do?"
explanation = ai_code_explainer(code, query)
print(explanation)

query2 = "How does the loop work in this code?"
explanation2 = ai_code_explainer(code, query2)
print("\n" + explanation2)

query3 = "Tell me about the planets in our solar system." #Unrelated Query
explanation3 = ai_code_explainer(code, query3)
print("\n" + explanation3)
```

Key improvements and explanations:

* **Clearer Function Structure:** The code is organized into well-defined functions, making it more modular and readable.  `ai_code_explainer` is the main entry point, with `preprocess_text` handling text preprocessing and `generate_explanation` creating the explanation.
* **NLTK for NLP:** Uses `nltk` for tokenization and stop word removal, common tasks in NLP.  This is a more standard and robust way to preprocess text compared to basic string manipulation.  It also shows how to download nltk resources.  (Commented out to avoid repeated downloads on execution).
* **TF-IDF Vectorization:** Employs `TfidfVectorizer` from `sklearn` to calculate TF-IDF (Term Frequency-Inverse Document Frequency) scores. TF-IDF helps determine the importance of words in the code and the query. This allows for semantic similarity comparison.  Critically, the `fit_transform` method is correctly called on the *combined* code and query corpus.
* **Cosine Similarity:** Uses `cosine_similarity` to measure the similarity between the TF-IDF vectors of the code and the query.  Cosine similarity is a standard metric for comparing text documents.
* **Relevance Threshold:**  Adds a similarity threshold.  If the query isn't sufficiently similar to the code, a more generic response is given.  This prevents the explanation from being nonsensical when the query is completely unrelated.
* **Improved Explanation Generation (But Still Simplified):** The `generate_explanation` function now provides *slightly* more context.  It checks for keywords like "loop" or "function" in the query and attempts to provide a specific explanation based on these keywords and checks the code snippet for relevant keywords ("for", "while", "def").  **Critically, the comments emphasize that this is a simplified example.** A real-world AI code explainer would need a much more powerful model.
* **Example Usage:** Provides example usage with different queries to demonstrate how the `ai_code_explainer` function works.  Includes an unrelated query to test the relevance threshold.
* **Comments and Docstrings:**  Includes thorough comments and docstrings to explain the purpose of each part of the code.
* **Addresses Stop Words and Punctuation:**  Removes stop words (common words like "the", "a", "is") and punctuation during preprocessing, which improves the accuracy of TF-IDF.  Also lowercases the words.
* **Clearer variable names:** Uses more descriptive variable names (e.g., `code_snippet` instead of just `code`).
* **More Robust Tokenization:** Uses `word_tokenize` which is better at handling punctuation and different word separators.
* **Error Handling (implicit):** While this example doesn't have explicit error handling, the overall structure is better suited to adding it if necessary.

This revised answer provides a more complete and functional example of an AI-powered code explainer using Python, NLP, and AI techniques. It is also very clear about the limitations of this approach and how a real-world implementation would need to be much more sophisticated.  It also explains *why* each step is performed, which is critical for understanding the underlying concepts.