scripts/dataset_utils.py

import os
import pandas as pd
import numpy as np
from ast import literal_eval
from SPARQLWrapper import SPARQLWrapper, JSON
from tqdm import tqdm
from urllib.parse import urlparse
import requests
import re
from ast import literal_eval
from PIL import Image
import math
from tqdm import tqdm
tqdm.pandas()

from dotenv import load_dotenv
load_dotenv()

DATA_DIR = os.environ['DATA_DIR']

replacements = {"celebs":"the subject of this image",
                "brands":"the brand in this image",
                "landmarks":"the place in this image",
                "paintings":"the painting in this image",
                }

def best_obj_type(obj_types):
        if type(obj_types) == str:
            obj_types = literal_eval(obj_types)
        prioritized_obj_types = ["city", "capital city", 'metropolis', 'country', 'occupation', 'language', 'type of sport', 'music genre'] # 'cinematic technique', 'team sport'
        for ot in prioritized_obj_types:
            if ot in obj_types:
                return ot
            for ot_ in obj_types:
                if "university" in ot_:
                    return "university"
                if "city" in ot_:
                    return "city"
        return obj_types[0]

def replace_for_image(row):
    replace_with = replacements[row['type']]
    return row["template"].replace("[subj]", replace_with)

class SPARQL:
    def __init__(self):
        self.agent = "'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/50.0.2661.102 Safari/537.36'"
        self.sparql = SPARQLWrapper("https://query.wikidata.org/sparql", agent=self.agent)
        self.sparql.setReturnFormat(JSON)

    def parse_value(self, value):
        parsed_uri = urlparse(value)
        if all([parsed_uri.scheme, parsed_uri.netloc]):
            return parsed_uri.path.split('/')[-1]
        return value 

    def execute(self, query):
        records = []     
        try:
            self.sparql.setQuery(query)
            responses = self.sparql.query().convert()
            for response in responses['results']['bindings']:
                record = {}
                for key in response:
                    record[key] = self.parse_value(response[key]['value'])
                records.append(record)
            if records == 0:
                print("request failed")
        except Exception as e:
            print(e)
        return pd.DataFrame(records)


def add_aliases(df):
    def _query(uris):
        return f'''
SELECT ?s_uri ?alias
WHERE {{
    {{VALUES ?s_uri {{ {" ".join([f"wd:{uri}" for uri in uris])} }} }}
    ?s_uri skos:altLabel ?alias.
    FILTER(LANG(?alias) = "en")
}}
''' 
    sparql = SPARQL()

    uris = list(set(df["s_uri"].tolist()))
    uri_chunks = [uris[i:i+100] for i in range(0, len(uris), 100)]

    aliases = pd.concat([sparql.execute(_query(chunk)) for chunk in tqdm(uri_chunks)])
    aliases = aliases.groupby("s_uri")["alias"].agg(list).reset_index(name="aliases")
    res = pd.merge(df, aliases, how='left', on='s_uri')
    res['aliases'] = res['aliases'].fillna('[]')
    return res

def get_aliases(df):
    def _query(uris): 
        return f'''
SELECT ?uri ?alias
WHERE {{
    {{VALUES ?uri {{ {" ".join([f"wd:{uri}" for uri in uris])} }} }}
    ?uri skos:altLabel ?alias.
    FILTER(LANG(?alias) = "en")
}}
''' 
    sparql = SPARQL()

    uris = list(set(df["s_uri"].tolist()))# + df["a_uri"].tolist()))
    uri_chunks = [uris[i:i+100] for i in range(0, len(uris), 100)]

    aliases = pd.concat([sparql.execute(_query(chunk)) for chunk in tqdm(uri_chunks)])
    aliases = aliases.groupby("uri")["alias"].agg(list).reset_index(name="aliases")
    return aliases

def add_images(df):
    def _query(uris):
        return f'''
SELECT ?s_uri ?image
WHERE {{
    {{VALUES ?s_uri {{ {" ".join([f"wd:{uri}" for uri in uris])} }} }}
    ?s_uri wdt:P18 ?image .
}}
''' 
    sparql = SPARQL()

    uris = list(set(df["s_uri"].tolist()))
    uri_chunks = [uris[i:i+100] for i in range(0, len(uris), 100)]

    images = pd.concat([sparql.execute(_query(chunk)) for chunk in tqdm(uri_chunks)])
    images['image'] = 'http://commons.wikimedia.org/wiki/Special:FilePath/' + images['image']
    res = pd.merge(df, images, how='inner', on='s_uri')
    return res


def get_attribute(df, attribute_name, attribute_id):
    def _query(uris):
        return f'''
SELECT ?s_uri ?attribute_name
WHERE {{
    {{VALUES ?s_uri {{ {" ".join([f"wd:{uri}" for uri in uris])} }} }}
    ?s_uri wdt:{attribute_id} ?{attribute_name} .
}}
''' 
    sparql = SPARQL()

    uris = list(set(df["s_uri"].tolist()))
    uri_chunks = [uris[i:i+100] for i in range(0, len(uris), 100)]

    attributes = pd.concat([sparql.execute(_query(chunk)) for chunk in tqdm(uri_chunks)])
    attributes = attributes.groupby("s_uri")[attribute_name].agg(list).reset_index(name=attribute_name)

    res = pd.merge(df, attributes, how='inner', on='s_uri')
    return res

def extract_year(timestamp):
    parts = timestamp.split('-')
    neg = False
    if parts[0] == '':
        year = parts[1]
        neg = True
    else:
        year = parts[0]
    if year.isdigit():
        return str(-int(year)) if neg else str(int(year)) 
    return np.nan

def get_all_properties(df):
    def _query(relation_ids): 
        return f'''
SELECT ?item ?itemLabel ?wd ?wdLabel ?ps_ ?ps_Label WHERE {{
  VALUES ?item {{ 
    {" ".join([f"wd:{id}" for id in relation_ids])}
  }}
  ?item ?p ?statement .
  ?statement ?ps ?ps_ .
  ?wd wikibase:claim ?p .
  ?wd wikibase:statementProperty ?ps .
  
  SERVICE wikibase:label {{ bd:serviceParam wikibase:language "[AUTO_LANGUAGE],en". }}
}}
'''
    sparql = SPARQL()
    
    # df = pd.read_csv(origin)
    subjects = df["s_uri"].to_list()
    subject_chunks = [subjects[i:i+20] for i in range(0, len(subjects), 20)]
    
    df = pd.concat([sparql.execute(_query(chunk)) for chunk in tqdm(subject_chunks)])
    df = df[~df["wdLabel"].str.contains(r"ID|category|template|username|instance of|gallery|article|handle|url|wiki|copyright|classification|website|described|tag|archive|reddit|profile|image|list|file", case=False, na=False)]
    tmp = df[(df['wd'] == 'P569') | (df['wd'] == 'P571')].copy()
    tmp['ps_Label'] = tmp['ps_Label'].apply(extract_year)
    tmp.dropna(subset=['ps_Label'], inplace=True)
    tmp['ps_'] = 'Q000'
    df = df[~((df['wd'] == 'P569') | (df['wd'] == 'P571'))]
    df = df[~df["ps_Label"].str.contains(r'\d', na=False)]
    df = df[df["ps_"].apply(lambda s: bool(re.fullmatch(r"Q\d+", s)))]
    df = pd.concat([df, tmp])
    df = df[["item", "itemLabel", "wd", "wdLabel", "ps_", "ps_Label"]]
    df = df.rename(
        columns = {
            "item": "s_uri",
            "itemLabel": "subject",
            "wd": "r_uri",
            "wdLabel": "relation",
            "ps_": "a_uri",
            "ps_Label": "attribute",
        }
    )
    return df


def attribute_type(df):
    def _query(uris):
        return f'''
SELECT ?uri ?typeLabel
WHERE {{
  {{VALUES ?uri {{ {" ".join([f"wd:{uri}" for uri in uris])} }} }}
  ?uri wdt:P31 ?type.
  SERVICE wikibase:label {{ bd:serviceParam wikibase:language "[AUTO_LANGUAGE],en". }}
}}   
''' 
    sparql = SPARQL()
    
    uris = df["a_uri"].drop_duplicates().to_list()
    uri_chunks = [uris[i:i+100] for i in range(0, len(uris), 100)]
    a_types = pd.concat([sparql.execute(_query(chunk)) for chunk in tqdm(uri_chunks)])
    a_types = a_types.groupby("uri")["typeLabel"].agg(list).reset_index(name="a_type")
    a_types['a_type'] = a_types['a_type'].apply(lambda x: x if type(x) == list else [])
    a_types = pd.concat([a_types, pd.DataFrame([{'uri': 'Q000', 'a_type': str(['year'])}])])
    return a_types


def get_wikidata_id(name):
    url = "https://www.wikidata.org/w/api.php"
    params = {
        "action": "wbsearchentities",
        "format": "json",
        "language": "en",
        "search": name
    }
    response = requests.get(url, params=params).json()
    if 'search' in response and response['search']:
        return response['search'][0]['id']
    return None

def add_wikidata_ids(df, name_col="subject"):
    df["wikidata_id"] = df[name_col].apply(get_wikidata_id)
    return df


def add_unesco_question(base_df):
    def _query(qids): 
        return f"""
    SELECT ?item ?itemLabel ?startTime WHERE {{
      VALUES ?item {{{' '.join(f'wd:{qid}' for qid in qids)}}}
      ?item p:P1435 ?heritageStatement.
      ?heritageStatement ps:P1435 wd:Q9259.
      OPTIONAL {{
        ?heritageStatement pq:P580 ?startTime.
      }}
      SERVICE wikibase:label {{ bd:serviceParam wikibase:language "[AUTO_LANGUAGE],en". }}
    }}
    """
    sparql = SPARQL()
    
    df = base_df[base_df['type'] == 'landmarks']
    subjects = df["s_uri"].to_list()
    subject_chunks = [subjects[i:i+20] for i in range(0, len(subjects), 20)]
    
    df = pd.concat([sparql.execute(_query(chunk)) for chunk in tqdm(subject_chunks)])
    df.dropna(subset=['startTime'], inplace=True)
    df['startTime'] = df['startTime'].apply(extract_year)
    df = df.rename(
        columns = {
            "item": "s_uri",
            "startTime": "attribute",
            "itemLabel": "subject",
        }
    )
    df['possible_answers'] = df['attribute'].apply(lambda x: str([x]))
    df['r_uri'] = 'P580'
    df['relation'] = 'start time'
    df['a_uri'] = 'P580'
    df['a_type'] = str(['year'])
    return df    


def aggregate_triplets(base, aliases, relations, attributes, add_unesco=False):
    subjects = base[['s_uri']]
    relations = relations.merge(subjects, on="s_uri")
    aliases = pd.read_csv("data/all_aliases.csv", index_col=0)
    if type(aliases.iloc[0]['aliases']) == str:
        aliases["aliases"] = aliases["aliases"].apply(lambda x: literal_eval(x))
    if type(attributes.iloc[0]['a_type']) == str:
        attributes["a_type"] = attributes["a_type"].apply(lambda x: literal_eval(x))

    relations = relations.merge(aliases, left_on="a_uri", right_on="uri", how="left")
    relations = relations.drop(columns=["uri"])
    relations["possible_answers"] = relations['aliases'].apply(lambda x: x if type(x) == list else [])
    relations["possible_answers"] = relations.progress_apply(lambda x: x["possible_answers"] + [x["attribute"]], axis=1)
    
    agg_funcs = {col: 'first' for col in relations.columns if col not in ['s_uri', 'r_uri', 'possible_answers']}
    agg_funcs['possible_answers'] = sum
    relations = relations.groupby(['s_uri', 'r_uri'], as_index=False).agg(agg_funcs)
    
    relations = relations.drop(columns=["aliases"])
    relations = relations.merge(attributes, left_on="a_uri", right_on="uri", how="left")
    relations = relations.drop(columns=["uri"])
    
    if add_unesco:
        unesco = add_unesco_question(base)
        relations = pd.concat([relations, unesco])
    
    return relations


def subj_substitute(row):
    if row['type'] == 'brands':
        return f"the brand {row['subject']}"
    if row['type'] == 'paintings':
        return f"the painting {row['subject']}"
    return row['subject']


def build_prompts(base_df, triplets, templates):
    subjects = base_df[["s_uri", "subject"]]
    base_df = base_df[["s_uri", "type"]]
    triplets = triplets.drop("subject", axis=1)
    triplets = triplets.merge(subjects, on=["s_uri"])
    triplets = triplets.merge(base_df, on=["s_uri"], how='left')
    triplets = triplets.merge(templates[["uri", "template"]], left_on="r_uri", right_on="uri")
    triplets = triplets.drop(columns=["uri"])
    triplets = triplets.dropna()
    
    query_counts = triplets.drop_duplicates(["s_uri", "r_uri"]).groupby(["s_uri"])["r_uri"].count().reset_index(name="count")
    triplets = triplets.merge(query_counts[query_counts["count"] > 1][["s_uri"]], on="s_uri")

    triplets["question_for_image"] = triplets.progress_apply(replace_for_image, axis=1)
    triplets["question_for_image"] = triplets.progress_apply(lambda row: row["question_for_image"].replace("[obj_type]", best_obj_type(row["a_type"])) if len(row["a_type"]) > 0 else row["question"], axis=1)
    triplets["question"] = triplets.progress_apply(lambda row: row["template"].replace("[subj]", subj_substitute(row)), axis=1)
    triplets["question"] = triplets.progress_apply(lambda row: row["question"].replace("[obj_type]", best_obj_type(row["a_type"])) if len(row["a_type"]) > 0 else row["question"], axis=1)
    triplets = triplets.drop(columns=["template"])
    triplets = triplets[['type','subject','question_for_image','question','possible_answers', 'relation', 's_uri', 'r_uri','a_uri','attribute','a_type']]
    return triplets


def resize_square(image, size=336, resample=Image.LANCZOS):
    """
    Resize an image to a square of the given size, first adding a black background if needed.
    image:  a Pillow image instance
    size:   an integer, the desired output size (width and height will be the same)
    """
    img_format = image.format
    image = image.copy()
    
    size = [size, size]
    img_size = image.size
    ratio = min(size[0] / img_size[0], size[1] / img_size[1])
    new_size = [
        int(math.ceil(img_size[0] * ratio)),
        int(math.ceil(img_size[1] * ratio))
    ]
    
    image = image.resize((new_size[0], new_size[1]), resample)
    
    # Make the image square by adding black padding
    max_dim = max(image.size)
    new_img = Image.new("RGB", (max_dim, max_dim), (0, 0, 0))
    new_img.paste(image, ((max_dim - image.size[0]) // 2, (max_dim - image.size[1]) // 2))
    
    # Resize to target size
    # new_img = new_img.resize((size, size), resample)
    new_img.format = img_format
    return new_img