import duckdb
import json, sys
import re
from pathlib import Path
ROOT_FOLDER = Path(__file__).resolve().parent.parent

sys.path.append(str(ROOT_FOLDER))
from src.semantic import semantic_search

def decode_ratings(page_content):
    block_pattern = r'\[\d\.0★\].*'
    matches = re.findall(block_pattern, page_content)
    if matches:
        pattern = r'\[(\d\.0)★\]\s*(.*?)\s*—\s*(.*)'
        parsed = []

        for r in matches[:3]:
            match = re.match(pattern, r)
            if match:
                rating, title, text = match.groups()
                parsed.append({
                    'rating': float(rating),
                    'title': title.strip(),
                    'text': text.strip()
                })

        return(parsed)
    else:
        return {}

def enrich_search_results(vector_store, query: str, k: int, hf_dataset):
    """
    Perform similarity search and enrich results with HuggingFace dataset metadata.
    
    Args:
        vector_store: LangChain vector store instance
        query: Search query string
        k: Number of results to return
        filter: Filter dict for similarity search
        hf_dataset: HuggingFace Arrow dataset (datasets.Dataset)
    
    Returns:
        List of enriched metadata objects as dicts
    """
    results = semantic_search(query, vector_store, k=k)

    # 1. Extract parent_asins from metadata
    parent_asins = [doc.metadata.get("parent_asin") for doc, score in results]

    # 2. Query HuggingFace dataset via DuckDB
    con = duckdb.connect()
    arrow_table = hf_dataset.data.table  # Get underlying PyArrow table
    con.register("hf_table", arrow_table)

    asin_list = ", ".join(f"'{asin}'" for asin in parent_asins if asin)
    query_sql = f"SELECT * FROM hf_table WHERE parent_asin IN ({asin_list})"
    hf_rows = con.execute(query_sql).fetchdf()

    # Build lookup: parent_asin -> metadata dict
    asin_to_metadata = {
        row["parent_asin"]: row.to_dict()
        for _, row in hf_rows.iterrows()
    }

    enriched_results = []

    for doc, score in results:
        parent_asin = doc.metadata.get("parent_asin")
        total_reviews = doc.metadata.get("total_reviews")
        metadata_object = asin_to_metadata.get(parent_asin, {}).copy()
        metadata_object['score'] = score
        metadata_object['total_reviews'] = total_reviews

        # 3. Extract 3 lines after "Top Reviews\n" from page_content
        page_content = doc.page_content
        metadata_object["reviews"] = decode_ratings(page_content)

        enriched_results.append(metadata_object)

    con.close()

    # 4. Return JSON metadata objects
    return [json.loads(json.dumps(obj, default=str)) for obj in enriched_results]


def enrich_bm25_search_results(retriever, query: str, k: int, hf_dataset):
    """
    Perform BM25 search and enrich results with HuggingFace dataset metadata.
    
    Args:
        retriever: LangChain BM25Retriever instance
        query: Search query string
        k: Number of results to return
        hf_dataset: HuggingFace Arrow dataset (datasets.Dataset)
    
    Returns:
        List of enriched metadata objects as dicts
    """
    # Get BM25 scores via underlying rank_bm25 library
    query_tokens = query.split()
    scores = retriever.vectorizer.get_scores(query_tokens)  # numpy array

    top_k_indices = sorted(enumerate(scores), key=lambda x: x[1], reverse=True)[:k]
    results = [(retriever.docs[i], score) for i, score in top_k_indices]

    # 1. Extract parent_asins from metadata
    parent_asins = [doc.metadata.get("parent_asin") for doc, score in results]

    # 2. Query HuggingFace dataset via DuckDB
    con = duckdb.connect()
    arrow_table = hf_dataset.data.table
    con.register("hf_table", arrow_table)

    asin_list = ", ".join(f"'{asin}'" for asin in parent_asins if asin)
    query_sql = f"SELECT * FROM hf_table WHERE parent_asin IN ({asin_list})"
    hf_rows = con.execute(query_sql).fetchdf()

    # Build lookup: parent_asin -> metadata dict
    asin_to_metadata = {
        row["parent_asin"]: row.to_dict()
        for _, row in hf_rows.iterrows()
    }

    enriched_results = []

    for doc, score in results:
        parent_asin = doc.metadata.get("parent_asin")

        metadata_object = {
            **doc.metadata,
            **asin_to_metadata.get(parent_asin, {}),
            "score": score,
        }

        metadata_object['reviews'] = metadata_object.pop('top_reviews', {}) or {}

        enriched_results.append(metadata_object)

    con.close()

    # 4. Return JSON metadata objects
    return [json.loads(json.dumps(obj, default=str)) for obj in enriched_results]

def _format_docs(results, hf_dataset):
    """
    Perform similarity search and enrich results with HuggingFace dataset metadata.
    
    Args:
        vector_store: LangChain vector store instance
        query: Search query string
        k: Number of results to return
        filter: Filter dict for similarity search
        hf_dataset: HuggingFace Arrow dataset (datasets.Dataset)
    
    Returns:
        List of enriched metadata objects as dicts
    """

    # 1. Extract parent_asins from metadata
    parent_asins = [doc.metadata.get("parent_asin") for doc in results]

    # 2. Query HuggingFace dataset via DuckDB
    con = duckdb.connect()
    arrow_table = hf_dataset.data.table  # Get underlying PyArrow table
    con.register("hf_table", arrow_table)

    asin_list = ", ".join(f"'{asin}'" for asin in parent_asins if asin)
    query_sql = f"SELECT * FROM hf_table WHERE parent_asin IN ({asin_list})"
    hf_rows = con.execute(query_sql).fetchdf()

    # Build lookup: parent_asin -> metadata dict
    asin_to_metadata = {
        row["parent_asin"]: row.to_dict()
        for _, row in hf_rows.iterrows()
    }

    enriched_results = []

    for doc in results:
        parent_asin = doc.metadata.get("parent_asin")
        total_reviews = doc.metadata.get("total_reviews")
        metadata_object = asin_to_metadata.get(parent_asin, {}).copy()
        metadata_object['total_reviews'] = total_reviews

        # 3. Extract 3 lines after "Top Reviews\n" from page_content
        page_content = doc.page_content
        metadata_object["reviews"] = decode_ratings(page_content)

        enriched_results.append(metadata_object)

    con.close()

    # 4. Return JSON metadata objects
    return [json.loads(json.dumps(obj, default=str)) for obj in enriched_results]