import my_new_openai
import my_1_writer
import json
import numpy as np


# sim search with dot_product and lin_distance
# the newly vectorized TERM will be added to the database
# database = .json file
def sim_search_load_db(database, term, add_to_db=True, debug=False):
    if type(term) == str:
        print("str")
        vector1 = my_new_openai.vectorize_data(term)
    elif type(term) == list:
        print("list")
        vector1 = term
    else:
        print("invalid search_term/search_vector format")
        return
    with open(database, "r") as f:
        table = json.load(f)
    sim_search_dict = {}
    for key in table.keys():
        vector2 = table[key]
        if debug:
            print("")
            print(f"{vector1}")
            print(f"{vector2}")
            print(f"doing dot product for {key} and {term}")
        dp = np.dot(vector1, vector2)
        distance = np.linalg.norm(np.array(vector1) - np.array(vector2))
        if debug:
            print(f"the dp is {dp}")
            print(f"the distance is{distance}")
            print("")
            print("")
            print("")
        sim_search_dict[key] = dp * distance

    # sort with the biggest similarity
    sorted_table = dict(sorted(sim_search_dict.items(), key=lambda item: item[1], reverse=True))

    if debug:
        for key, value in sorted_table[:5]:
            print(f"{key}: {value}")
    if add_to_db:

        if term in table.keys():
            print("the search term is in the database!")
            # add the newly vectorized term to the words, if not already in the vector table
        else:
            if database != "session/my_words_vec_table.json":
                database = "session/my_vecs.json"
                # table = load_df(database)  # ??
            table[str(term)] = vector1
            my_1_writer.safe_my_dict_as_json(database, table)
    # first_key, first_value = list(sortedTable.items())[0]
    print(f"the closest word to your input is: {list(sorted_table.keys())[0]}")
    return sorted_table


def dot_p_to_1(database, vector1=0, analysis_filename=0):

    with open(database, "r") as f:
        table = json.load(f)
    dot_product_to1 = {}

    if vector1 == 0:
        vector1 = [0.025515518153991442 for _ in range(1536)]
    elif vector1 == 1:
        vector1 = table[str(list(table.keys())[0])]

    for key in table.keys():
        dot_product_to1[key] = np.dot(vector1, table[key])
    my_1_writer.safe_my_dict_as_json(analysis_filename, dot_product_to1)
    print("dot p to 1 saved")


def lin_dist(database, vector1=0, analysis_filename=0):
    with open(database, "r") as f:
        table = json.load(f)
    lin_dist_to_1 = {}

    if vector1 == 0:
        vector1 = [0.025515518153991442 for _ in range(1536)]
    elif vector1 == 1:
        vector1 = table[str(list(table.keys())[0])]

    for key in table.keys():
        lin_dist_to_1[key] = np.linalg.norm(np.array(vector1) - np.array(table[key]))

    my_1_writer.safe_my_dict_as_json(analysis_filename, lin_dist_to_1)
    print("lin dist to 1 saved")


def manhattan_dist(database, vector1=0, analysis_filename=0):
    with open(database, "r") as f:
        table = json.load(f)
    manhattan_dist_to_1 = {}

    if vector1 == 0:
        vector1 = [0.025515518153991442 for _ in range(1536)]
    elif vector1 == 1:
        vector1 = table[str(list(table.keys())[0])]

    for key in table.keys():
        manhattan_dist_to_1[key] = sum(np.array(vector1) - np.array(table[key]))

    my_1_writer.safe_my_dict_as_json(analysis_filename, manhattan_dist_to_1)
    print("manhattan dist to 1 saved")


#vec_table
def sim_search_fly(vec_table, term, debug=False):
    if debug:
        print(type(vec_table))
        print(type(term))
        print(type(vec_table[list(vec_table.keys())[0]]))
        print("vec table:")
    print(vec_table[list(vec_table.keys())[5]][:4])
    print("search term")
    print(term[:4])
    if type(term) == str:
        print("str")
        vector1 = my_new_openai.vectorize_data(term)
    elif type(term) == list:
        print("list")
        vector1 = term
    else:
        print("invalid search_term/search_vector format")
        return

    sim_search_dict = {}
    for key in vec_table.keys():
        vector2 = vec_table[key]
        if debug:
            print("")
            print(f"{vector1}")
            print(f"{vector2}")
            print(f"doing dot product for {key} and {term}")
        if vector2[0] == vector2[1] and vector2[3] == vector2[4] and vector2[5] == vector2[6]:
            dp = 200
        else:
            dp = np.dot(vector1, vector2)
        #distance = np.linalg.norm(np.array(vector1) - np.array(vector2))
        if debug:
            print(f"the dp is {dp}")
            #print(f"the distance is{distance}")
            print("")
            print("")
            print("")
        sim_search_dict[key] = dp #* distance

    # sort with the biggest similarity
    sorted_table = dict(sorted(sim_search_dict.items(), key=lambda item: item[1], reverse=True))

    if debug:
        for key, value in sorted_table[:5]:
            print(f"{key}: {value}")

    # first_key, first_value = list(sortedTable.items())[0]
    print(f"the closest word to your input is: {list(sorted_table.keys())[0]}")
    return sorted_table