import os
os.environ["TRANSFORMERS_CACHE"] = "/tmp/hf-cache"
os.environ["HF_HOME"] = "/tmp/hf-home"

import nltk
nltk.download("punkt", download_dir="/tmp/nltk_data")

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.cluster import KMeans
from sklearn.metrics.pairwise import cosine_similarity
from nltk.tokenize import sent_tokenize
from transformers import pipeline
import numpy as np

# Load summarizer model
summarizer = pipeline("summarization", model="sshleifer/distilbart-cnn-12-6")

def summarize_review(text):
    """Standard transformer-based summarization"""
    return summarizer(text, max_length=60, min_length=10, do_sample=False)[0]["summary_text"]

def smart_summarize(text, n_clusters=1):
    """
    Clustering + cosine similarity-based summarization
    Selects most representative sentence(s) from each cluster
    """
    sentences = sent_tokenize(text)
    if len(sentences) <= 1:
        return text

    tfidf = TfidfVectorizer(stop_words="english")
    tfidf_matrix = tfidf.fit_transform(sentences)

    if len(sentences) <= n_clusters:
        return " ".join(sentences)

    kmeans = KMeans(n_clusters=n_clusters, random_state=42).fit(tfidf_matrix)
    summary_sentences = []

    for i in range(n_clusters):
        idx = np.where(kmeans.labels_ == i)[0]
        if not len(idx):
            continue
        # Average vector from cluster, converted to ndarray
        avg_vector = tfidf_matrix[idx].mean(axis=0).A  # Convert matrix to ndarray
        # Compute cosine similarity between avg_vector and tfidf vectors in cluster
        sim = cosine_similarity(avg_vector, tfidf_matrix[idx].toarray())
        most_representative = sentences[idx[np.argmax(sim)]]
        summary_sentences.append(most_representative)

    # Preserve original sentence order
    return " ".join(sorted(summary_sentences, key=sentences.index))