given this code, how can i reduce the number of total word produced? for example, i would like to obtain less words from the clustered part.

import csv
import re
import os
import nltk
import pandas as pd
from collections import defaultdict, Counter
from nltk.stem import WordNetLemmatizer
from gensim.models import KeyedVectors, FastText
from sklearn.cluster import KMeans
import numpy as np

# ------------------------------#
#           Setup               #
# ------------------------------#

# Download NLTK data files (only need to run once)
# nltk.download('wordnet')
# nltk.download('omw-1.4')

# Initialize the lemmatizer
lemmatizer = WordNetLemmatizer()

# Function to load the appropriate word embedding model
def load_model(model_type, model_path):
    if model_type == 'word2vec':
        print("Loading Word2Vec model. This may take a few minutes...")
        model = KeyedVectors.load_word2vec_format(model_path, binary=True)
        print("Word2Vec model loaded successfully.")
    elif model_type == 'glove':
        print("Loading GloVe model. This may take a few minutes...")
        model = {}
        with open(model_path, 'r', encoding='utf-8') as f:
            for line in f:
                values = line.split()
                word = values[0]
                vector = np.asarray(values[1:], dtype='float32')
                model[word] = vector
        print("GloVe model loaded successfully.")
    elif model_type == 'fasttext':
        print("Loading FastText model. This may take a few minutes...")
        model = FastText.load_fasttext_format(model_path)
        print("FastText model loaded successfully.")
    else:
        raise ValueError(f"Unsupported model type: {model_type}")
    return model

# Function to get the vector for a word/phrase
def get_word_vector(word, model, model_type):
    if model_type in ['word2vec', 'fasttext']:
        if word in model:
            return model[word]
    elif model_type == 'glove':
        if word in model:
            return model[word]
    # Handle multi-word phrases by averaging their word vectors
    words = word.split()
    vectors = []
    for w in words:
        if model_type in ['word2vec', 'fasttext'] and w in model:
            vectors.append(model[w])
        elif model_type == 'glove' and w in model:
            vectors.append(model[w])
    if vectors:
        return np.mean(vectors, axis=0)
    else:
        return None

# Function to lemmatize a keyword
def lemmatize_keyword(keyword):
    return lemmatizer.lemmatize(keyword.lower())

# Function to process the CSV file and lemmatize keywords per row, and output lemmatized counts
def lemmatize_keywords(input_file, lemmatized_counts_output_file):
    # Read the CSV file using pandas
    df = pd.read_csv(input_file)
    
    # Ensure 'Image Name' and 'Keywords' columns exist
    if 'Image Name' not in df.columns or 'Keywords' not in df.columns:
        print("Error: 'Image Name' and 'Keywords' columns not found in input CSV.")
        return None, None
    
    per_row_data = []
    keyword_counts = Counter()
    
    # Iterate through each row to extract and lemmatize keywords
    for index, row in df.iterrows():
        image_name = row.get('Image Name', '')
        keywords_str = row.get('Keywords', '')
        if pd.isna(keywords_str):
            keywords_str = ''
        
        # Split keywords by comma and strip whitespace
        extracted_keywords = [kw.strip() for kw in keywords_str.split(',')]
        # Lemmatize keywords
        lemmatized_keywords = [lemmatize_keyword(kw) for kw in extracted_keywords if kw]
        # Update global counts
        keyword_counts.update(lemmatized_keywords)
        
        # Store per-row data
        per_row_data.append({
            'Image Name': image_name,
            'Keywords': ', '.join(extracted_keywords),
            'Lemmatized Keywords': ', '.join(lemmatized_keywords)
        })
    
    # Create a DataFrame from per_row_data
    per_row_df = pd.DataFrame(per_row_data)
    
    # Write the lemmatized keyword counts to CSV
    lemmatized_counts = [{'Lemmatized Keyword': kw, 'Count': count} 
                         for kw, count in keyword_counts.items()]
    lemmatized_counts_df = pd.DataFrame(lemmatized_counts)
    
    # Sort by Count in descending order
    lemmatized_counts_df = lemmatized_counts_df.sort_values(by='Count', ascending=False)
    
    # Write to CSV
    lemmatized_counts_df.to_csv(lemmatized_counts_output_file, index=False)
    print(f"Lemmatized keyword counts saved to '{lemmatized_counts_output_file}'.")
    
    return per_row_df, keyword_counts

# Function to cluster infrequent keywords (count <= threshold)
def cluster_infrequent_keywords(keywords, keyword_counts, model, model_type, num_clusters=5):
    vectors = []
    valid_keywords = []
    for keyword in keywords:
        vector = get_word_vector(keyword, model, model_type)
        if vector is not None:
            vectors.append(vector)
            valid_keywords.append(keyword)
    
    if not vectors:
        print("No valid vectors found for clustering.")
        return {}
    
    vectors = np.array(vectors)
    
    # Perform K-Means clustering
    kmeans = KMeans(n_clusters=num_clusters, random_state=42)
    labels = kmeans.fit_predict(vectors)
    
    # Organize keywords by clusters
    clusters = defaultdict(list)
    for label, keyword in zip(labels, valid_keywords):
        clusters[label].append(keyword)
    
    # Select the most frequent keyword in each cluster as the representative
    representative_keywords = {}
    for label, words in clusters.items():
        most_frequent = max(words, key=lambda w: keyword_counts[w])
        representative_keywords[label] = most_frequent
    
    # Create a mapping from keyword to representative
    keyword_to_representative = {}
    for label, words in clusters.items():
        rep = representative_keywords[label]
        for word in words:
            keyword_to_representative[word] = rep
    
    return keyword_to_representative

# Function to perform clustering with frequency threshold
def perform_clustering_with_threshold(keyword_counts, model, model_type, threshold=5, num_clusters=5):
    # Separate frequent and infrequent keywords
    frequent_keywords = [kw for kw, count in keyword_counts.items() if count > threshold]
    infrequent_keywords = [kw for kw, count in keyword_counts.items() if count <= threshold]
    
    print(f"Number of frequent keywords (> {threshold}): {len(frequent_keywords)}")
    print(f"Number of infrequent keywords (<= {threshold}): {len(infrequent_keywords)}")
    
    # Keep frequent keywords as-is
    clustered = {kw: kw for kw in frequent_keywords}
    
    # Cluster infrequent keywords and map them to their representatives
    if infrequent_keywords:
        mapping = cluster_infrequent_keywords(infrequent_keywords, keyword_counts, model, model_type, num_clusters)
        clustered.update(mapping)
        print(f"Number of cluster representatives: {len(set(mapping.values()))}")
    else:
        print("No infrequent keywords to cluster.")
    
    return clustered

# Function to generate clustered keywords per row
def generate_clustered_keywords(per_row_df, clustered_keyword_mapping):
    clustered_keywords_list = []
    for idx, row in per_row_df.iterrows():
        lemmatized_keywords = row['Lemmatized Keywords'].split(', ')
        clustered_keywords = [clustered_keyword_mapping.get(kw, kw) for kw in lemmatized_keywords]
        clustered_keywords_list.append(', '.join(clustered_keywords))
    per_row_df['Clustered Keywords'] = clustered_keywords_list
    return per_row_df

# ------------------------------#
#             Main               #
# ------------------------------#


def main():
    # File paths (replace these with your actual file paths)
    original_input_file = '/home/danilo/Projects/IR_MD/cleaned_/home/danilo/Projects/IR_MD/cleaned_/home/danilo/Projects/IR_MD/TESTING/cleaned_graffe_better_prompt_KeyExt_Qwen2VL7B_20240912-131955_.csv'
    lemmatized_counts_output_file = '/home/danilo/Projects/IR_MD/TESTING/5keywords_categorized_keywords_lemmatized.csv'
    output_file = '/home/danilo/Projects/IR_MD/TESTING/100_cluster_5keywords_categorized_keywords_clustered.csv'
    
    # Clustering parameters
    model_type = 'glove'  # Choose from 'word2vec', 'glove', 'fasttext'
    model_path = '/home/danilo/Projects/IR_MD/models/glove.6B.300d.txt'  # Path to the model file
    frequency_threshold = 5  # Keywords with count >5 are kept as-is
    num_clusters = 100         # Number of clusters for infrequent keywords
    
    # Step 1: Lemmatization and saving lemmatized counts
    per_row_df, keyword_counts = lemmatize_keywords(original_input_file, lemmatized_counts_output_file)
    if per_row_df is None:
        return  # Exit if there was an error
    
    # Step 2: Load the appropriate model
    model = load_model(model_type, model_path)
    
    # Step 3: Perform clustering on infrequent keywords
    clustered_keyword_mapping = perform_clustering_with_threshold(keyword_counts, model, model_type, threshold=frequency_threshold, num_clusters=num_clusters)
    
    # Step 4: Generate Clustered Keywords per row
    per_row_df = generate_clustered_keywords(per_row_df, clustered_keyword_mapping)
    
    # Step 5: Write the output CSV
    per_row_df.to_csv(output_file, index=False)
    print(f"\nProcess completed. Output saved to '{output_file}'.")

if __name__ == "__main__":
    main()