import json
import random
import re
import os
from pymongo import MongoClient
import certifi
from bson import ObjectId
from dotenv import load_dotenv

load_dotenv()


CONNECTION_STRING = os.getenv("CONNECTION_STRING")
DB_NAME = os.getenv("DB_NAME")
COLLECTION_NAME = os.getenv("COLLECTION_NAME")


def get_data(filepath):
    with open(filepath, "r") as f:
        data = json.load(f)
    return data



def get_score(user_id):
    client = MongoClient(CONNECTION_STRING)
    db = client["sattvastha"]
    collection = db["completedassessments"]
    score = {}
    limit = 1
    user_id_obj = user_id
    score = {}
    limit = 1
    # user_id = ObjectId('69397eaa34ad3c5ebda7022a')
    doc = (collection.find({"userId": user_id_obj,"templateName": "Chitta Bhumi"}).sort("completedAt", -1).limit(limit))
    try:
        latest_entry = doc.next()
        # print(latest_entry)
    except StopIteration:
        return ("No documents found for this user.")

    score = {}
    interpret = {"1. Never true for me":1,"2. Rarely true for me":2,"3. Sometimes true for me":3,"4. Often true for me":4,"5. Always true for me":5}

    for item in latest_entry["answers"]:
        # print(item["answer"])
        score[item["questionId"]] = 0
    for item in latest_entry["answers"]:
        try:
            score[item["questionId"]] += interpret[item["answer"]]
        except:
            print("something not found here")

    # print(score)
    groups = {"K":"Kshipta", "Mudha":"Mudha", "Vikashipta":"Vikshipta", "Ekagra":"Ekagra", "Nirodha":"Nirodha"}
    # groups = ["", "Mudha", "Vikshipta", "Ekagra", "Nirodha"]

    normalized = {}

    for g,name in groups.items():
        group_keys = [f"{g}_{i}" for i in range(1,6)]
        total = sum(score[k] for k in group_keys)
        normalized[name] = total / 5

    return normalized





    # doc = (collection.find({"userId": user_id_obj}).sort("completedAt", -1).limit(limit))
    # docs = list(doc)
    # docs[0]
    # for i in range(len(docs[0]["answers"])):
    #     score[docs[0]["answers"][i]["questionId"]] = 0
    # for i in range(len(docs[0]["answers"])):
    #     score[docs[0]["answers"][i]["questionId"]] +=1
    # # print(score)
    # normalized_scores = {key: value / 5 for key, value in score.items()}
    # # print(normalized_scores)
    # # print("\n")
    # return normalized_scores


        # score = {}
    # for key in data.keys():
    #     print(f"Please enter a score between 1-5 for each of the questions under {key.upper()}, (1 = Never/Rarely True, 2 = Occasionally True, 3 = Sometimes True, 4 = Often True, 5 = Almost Always True)")
    #     score[key] = 0
    #     for i in range(len(data[key])):
    #         print(data[key][i])
    #         # user = int(input("How much do you resonate with it?"))
    #         user = random.randint(1,5)
    #         score[key] += user
    #         # print("\n")

    # normalized_scores = {key: value / 5 for key, value in score.items()}
    # return normalized_scores




def get_raw_interpretation(key):
    with open("interpretations.json", "r") as f:
        interpretations = json.load(f)
    return interpretations[key]




def get_definitions(key):
    with open("definitions.json", "r") as f:
        definitions = json.load(f)
    return definitions[key]




def get_curetasks(key):
    with open("task.json", "r") as f:
        task = json.load(f)
    return task[key]


def extract_tasks(text):
    daily_pattern = re.compile(
        r"(daily[_\s]*tasks?\b.*?)(?=\bweekly[_\s]*tasks?\b|\Z)",
        re.IGNORECASE | re.DOTALL
    )

    weekly_pattern = re.compile(
        r"(weekly[_\s]*tasks?\b.*)",
        re.IGNORECASE | re.DOTALL
    )

    daily_match = daily_pattern.search(text)
    weekly_match = weekly_pattern.search(text)

    daily_tasks = daily_match.group(1).strip() if daily_match else None
    weekly_tasks = weekly_match.group(1).strip() if weekly_match else None

    return daily_tasks, weekly_tasks