import os
from io import StringIO

import pandas as pd
import requests
from dagster import asset
import dagster as dg

import datetime
import pendulum

IE_ENDPOINT = "https://publicreporting.elections.ny.gov/IndependentExpenditure"

def get_cookies(s: requests.Session, from_date: datetime.date, to_date: datetime.date):
    """Fetch cookies into session"""
    cookie_postdata = {
        'lstUCOfficeType': '0',
        'ddlType': '',
        'txtName': '',
        'txtExpenderName': '',
        'txtExpenseRecipientName': '',
        'lstUCCounty': '',
        'lstUCMuncipality': '',
        'lstUCOffice': '',
        'lstUCDistrict': '',
        'txtDateFrom': from_date.strftime('%m/%d/%Y'),
        'txtDateTo': to_date.strftime('%m/%d/%Y'),
        'lstUCYear': '- Select -',
        'ddlDateType': 'Submitted',
        'ddlSearchBy': 'All'
    }

    return s.post(f"{IE_ENDPOINT}/BindIndExpData/", json=cookie_postdata)

def gen_ie_query(from_date: datetime.date, to_date: datetime.date):
    """Fill in query parameters for independent expenditures and date range"""
    return {
        'lstUCOfficeType': '0',
        'lstUCCounty': '',
        'lstUCMuncipality': '',
        'ddlSearchBy': '1',
        'txtFilerId': '',
        'txtName': '',
        'txtExpenderName': '',
        'ddlAutoCompleteConName': '',
        'txtExpenseRecipientName': '',
        'lstAutoCompleteCommittee': '',
        'lstElectionType': '',
        'lstUCDistrict': '',
        'ddlSelectDate': '2',
        'lstUCYear': '- Select -',
        'txtDateFrom': from_date.strftime('%m/%d/%Y'),
        'txtDateTo': to_date.strftime('%m/%d/%Y'),
        'ddlDateType': '2',
        'Command': 'CSV',
        'gridView24HourIE_length': '10',
    }

@asset(
       group_name="nyboe",
       compute_kind="NYBOE API",
       partitions_def=dg.DailyPartitionsDefinition(start_date="2025-05-10")
   )
def fetch_expenditures(context: dg.AssetExecutionContext) -> None:
    """Fetch the day before the partition date"""
    end_date = pendulum.parse(context.partition_key).subtract(days=1)

    start_date = end_date.subtract(days=1)
    with requests.Session() as s:
        res = get_cookies(s, start_date, end_date)
        if not res.json()["aaData"]:
            return None

        req = s.get(f"{IE_ENDPOINT}/IndependentExpenditure",
            params=gen_ie_query(start_date, end_date),
        )
        df = pd.read_csv(StringIO(req.text), index_col=False)
        os.makedirs("data", exist_ok=True)
        with open(f"data/expenditures_{end_date.format("YYYYMMDD")}.parquet", "wb") as f:
            df.to_parquet(f)

    return None

# @asset(deps=[topstory_ids], group_name="nyboe", compute_kind="HackerNews API")
# def topstories(context: AssetExecutionContext) -> MaterializeResult:
#     """Get items based on story ids from the HackerNews items endpoint. It may take 30 seconds to fetch all 100 items.

#     API Docs: https://github.com/HackerNews/API#items
#     """
#     with open("data/topstory_ids.json") as f:
#         topstory_ids = json.load(f)

#     results = []
#     for item_id in topstory_ids:
#         item = requests.get(f"https://hacker-news.firebaseio.com/v0/item/{item_id}.json").json()
#         results.append(item)

#         if len(results) % 20 == 0:
#             context.log.info(f"Got {len(results)} items so far.")

#     df = pd.DataFrame(results)
#     df.to_csv("data/topstories.csv")

#     return MaterializeResult(
#         metadata={
#             "num_records": len(df),  # Metadata can be any key-value pair
#             "preview": MetadataValue.md(df.head().to_markdown()),
#             # The `MetadataValue` class has useful static methods to build Metadata
#         }
#     )


# @asset(deps=[topstories], group_name="nyboe", compute_kind="Plot")
# def most_frequent_words(context: AssetExecutionContext) -> MaterializeResult:
#     """Get the top 25 most frequent words in the titles of the top 100 HackerNews stories."""
#     stopwords = ["a", "the", "an", "of", "to", "in", "for", "and", "with", "on", "is"]

#     topstories = pd.read_csv("data/topstories.csv")

#     # loop through the titles and count the frequency of each word
#     word_counts = {}
#     for raw_title in topstories["title"]:
#         title = raw_title.lower()
#         for word in title.split():
#             cleaned_word = word.strip(".,-!?:;()[]'\"-")
#             if cleaned_word not in stopwords and len(cleaned_word) > 0:
#                 word_counts[cleaned_word] = word_counts.get(cleaned_word, 0) + 1

#     # Get the top 25 most frequent words
#     top_words = {
#         pair[0]: pair[1]
#         for pair in sorted(word_counts.items(), key=lambda x: x[1], reverse=True)[:25]
#     }

#     # Make a bar chart of the top 25 words
#     plt.figure(figsize=(10, 6))
#     plt.bar(list(top_words.keys()), list(top_words.values()))
#     plt.xticks(rotation=45, ha="right")
#     plt.title("Top 25 Words in Hacker News Titles")
#     plt.tight_layout()

#     # Convert the image to a saveable format
#     buffer = BytesIO()
#     plt.savefig(buffer, format="png")
#     image_data = base64.b64encode(buffer.getvalue())

#     # Convert the image to Markdown to preview it within Dagster
#     md_content = f"![img](data:image/png;base64,{image_data.decode()})"

#     with open("data/most_frequent_words.json", "w") as f:
#         json.dump(top_words, f)

#     # Attach the Markdown content as metadata to the asset
#     return MaterializeResult(metadata={"plot": MetadataValue.md(md_content)})