import os
import nltk
from nltk.corpus import stopwords
from sklearn.feature_extraction.text import CountVectorizer
import plotly.express as px

def preprocess_text(text):
    # Tokenize the text and remove stopwords
    tokens = nltk.word_tokenize(text.lower())
    stop_words = set(stopwords.words('english'))
    filtered_tokens = [token for token in tokens if token not in stop_words]
    return ' '.join(filtered_tokens)

def get_context_files(prompt):
    # Get all .md files in the current directory
    md_files = [file for file in os.listdir() if file.endswith('.md')]
    
    # Preprocess the prompt and context files
    processed_prompt = preprocess_text(prompt)
    processed_files = {}
    for file in md_files:
        with open(file, 'r') as f:
            content = f.read()
            processed_files[file] = preprocess_text(content)
    
    # Create a CountVectorizer to calculate word counts
    vectorizer = CountVectorizer()
    file_vectors = vectorizer.fit_transform(processed_files.values())
    prompt_vector = vectorizer.transform([processed_prompt])
    
    # Calculate the number of matching words for each file
    match_counts = prompt_vector.dot(file_vectors.T).toarray()[0]
    
    # Sort the files by the number of matching words
    sorted_files = sorted(zip(md_files, match_counts), key=lambda x: x[1], reverse=True)
    
    # Get the top ten files
    top_ten_files = [file for file, count in sorted_files[:10]]
    
    # Create a single prompt by concatenating the original prompt and the content of the top ten files
    context_prompt = prompt
    for file in top_ten_files:
        with open(file, 'r') as f:
            context_prompt += '\n\n' + f.read()
    
    # Create a plotly graph showing the counts of matching words for the top ten files
    fig = px.bar(x=[file for file, count in sorted_files[:10]], y=[count for file, count in sorted_files[:10]])
    fig.update_layout(xaxis_title='File', yaxis_title='Number of Matching Words')
    fig.show()
    
    return context_prompt

# Example usage
prompt = "What is the importance of machine learning in healthcare?"
context_prompt = get_context_files(prompt)
print(context_prompt)