import gradio as gr import pandas as pd import numpy as np import string import re import json import random import torch import hashlib, base64 from tqdm import tqdm from gradio.themes.base import Base import openai # bloomber vis import bloomberg_vis as bv # error messages from error_messages import * tqdm().pandas() # bias testing manager import mgr_bias_scoring as bt_mgr # managers for sentences and biases import mgr_requests as rq_mgr from mgr_requests import G_CORE_BIAS_NAME import mgr_biases as bmgr # cookie manager #import mgr_cookies as cookie_mgr use_paper_sentences = False G_TEST_SENTENCES = [] G_NUM_SENTENCES = 0 G_MISSING_SPEC = [] def getTermsFromGUI(group1, group2, att1, att2): bias_spec = { "social_groups": { "group 1": [t.strip(" ") for t in group1.split(",") if len(t.strip(' '))>0], "group 2": [t.strip(" ") for t in group2.split(",") if len(t.strip(' '))>0]}, "attributes": { "attribute 1": [t.strip(" ") for t in att1.split(",") if len(t.strip(' '))>0], "attribute 2": [t.strip(" ") for t in att2.split(",") if len(t.strip(' '))>0]} } return bias_spec # Select from example datasets def prefillBiasSpec(evt: gr.SelectData): global use_paper_sentences, G_MISSING_SPEC, G_CORE_BIAS_NAME G_MISSING_SPEC = [] G_CORE_BIAS_NAME = evt.value print(f"Setting core bias name to: {G_CORE_BIAS_NAME}") print(f"Selected {evt.value} at {evt.index} from {evt.target}") #bias_filename = f"{evt.value[1]}.json" bias_filename = f"{bmgr.bias2tag[evt.value]}.json" print(f"Filename: {bias_filename}") isCustom = bmgr.isCustomBias(bias_filename) if isCustom: print(f"Custom bias specification: {bias_filename}") bias_spec = bmgr.loadCustomBiasSpec(bias_filename) else: print(f"Core bias specification: {bias_filename}") bias_spec = bmgr.loadPredefinedBiasSpec(bias_filename) grp1_terms, grp2_terms = bmgr.getSocialGroupTerms(bias_spec) att1_terms, att2_terms = bmgr.getAttributeTerms(bias_spec) print(f"Grp 1: {grp1_terms}") print(f"Grp 2: {grp2_terms}") print(f"Att 1: {att1_terms}") print(f"Att 2: {att2_terms}") #use_paper_sentences = True return (', '.join(grp1_terms[0:50]), ', '.join(grp2_terms[0:50]), ', '.join(att1_terms[0:50]), ', '.join(att2_terms[0:50]), gr.update(interactive=False, visible=False)) def updateErrorMsg(isError, text): return gr.Markdown.update(visible=isError, value=text) def countBiasCustomSpec(bias_spec): if (bias_spec) == 0: return 0 elif 'custom_counts' in bias_spec: rq_count_1 = sum([v for v in bias_spec['custom_counts' ][0].values()]) rq_count_2 = sum([v for v in bias_spec['custom_counts' ][1].values()]) return rq_count_1+rq_count_2 else: return 0 def generateSentences(gr1, gr2, att1, att2, openai_key, num_sent2gen, progress=gr.Progress()): global use_paper_sentences, G_NUM_SENTENCES, G_MISSING_SPEC, G_TEST_SENTENCES print(f"GENERATE SENTENCES CLICKED!, requested sentence per attribute number: {num_sent2gen}") # No error messages by default err_update = updateErrorMsg(False, "") bias_test_label = "Test Model Using Imbalanced Sentences" # There are no sentences available at all if len(G_TEST_SENTENCES) == 0: bias_gen_states = [True, False] online_gen_visible = True test_model_visible = False else: bias_gen_states = [True, True] online_gen_visible = True test_model_visible = True info_msg_update = gr.Markdown.update(visible=False, value="") test_sentences = [] bias_spec = getTermsFromGUI(gr1, gr2, att1, att2) g1, g2, a1, a2 = bt_mgr.get_words(bias_spec) total_att_terms = len(a1)+len(a2) all_terms_len = len(g1)+len(g2)+len(a1)+len(a2) print(f"Length of all the terms: {all_terms_len}") if all_terms_len == 0: print("No terms entered!") err_update = updateErrorMsg(True, NO_TERMS_ENTERED_ERROR) #raise gr.Error(NO_TERMS_ENTERED_ERROR) else: if len(openai_key) == 0: print("Empty OpenAI key!!!") err_update = updateErrorMsg(True, OPENAI_KEY_EMPTY) elif len(openai_key) < 10: print("Wrong length OpenAI key!!!") err_update = updateErrorMsg(True, OPENAI_KEY_WRONG) else: progress(0, desc="ChatGPT generation...") print(f"Using Online Generator LLM...") print(f"Is custom spec? {countBiasCustomSpec(G_MISSING_SPEC)}") print(f"Custom spec: {G_MISSING_SPEC}") use_bias_spec = G_MISSING_SPEC if countBiasCustomSpec(G_MISSING_SPEC)>0 else bias_spec test_sentences, gen_err_msg = rq_mgr._generateOnline(use_bias_spec, progress, openai_key, num_sent2gen, isSaving=False) #print(f"Test sentences: {test_sentences}") num_sentences = len(test_sentences) print(f"Returned num sentences: {num_sentences}") G_NUM_SENTENCES = len(G_TEST_SENTENCES) + num_sentences if num_sentences == 0 and len(G_TEST_SENTENCES) == 0: print("Test sentences empty!") #raise gr.Error(NO_SENTENCES_ERROR) # Some error returned from OpenAI generator if gen_err_msg != None: err_update = updateErrorMsg(True, gen_err_msg) # No sentences returned, but no specific error else: err_update = updateErrorMsg(True, NO_GEN_SENTENCES_ERROR) elif num_sentences == 0 and len(G_TEST_SENTENCES) > 0: print(f"Has some retrieved sentences {G_TEST_SENTENCES}, but no sentnces generated {num_sentences}!") #raise gr.Error(NO_SENTENCES_ERROR) # Some error returned from OpenAI generator if gen_err_msg != None: err_update = updateErrorMsg(True, gen_err_msg) # No sentences returned, but no specific error else: err_update = updateErrorMsg(True, NO_GEN_SENTENCES_ERROR) # has all sentences, can bias test bias_gen_states = [True, True] else: print("Combining generated and existing...") print(f"Existing sentences: {len(G_TEST_SENTENCES)}") print(f"Generated: {len(test_sentences)}") G_TEST_SENTENCES = G_TEST_SENTENCES + test_sentences print(f"Combined: {len(G_TEST_SENTENCES)}") # has all sentences, can bias test bias_gen_states = [False, True] online_gen_visible = False test_model_visible = True # show choise of tested model and the sentences info_msg, att1_missing, att2_missing, total_missing, c_bias_spec = _genSentenceCoverMsg(G_TEST_SENTENCES, total_att_terms, bias_spec, isGen=True) info_msg_update = gr.Markdown.update(visible=True, value=info_msg) bias_test_label = "Test Model For Social Bias" #cookie_mgr.saveOpenAIKey(openai_key) print(f"Online gen visible: {not err_update['visible']}") return (err_update, # err message if any info_msg_update, # infor message about the number of sentences and coverage gr.Row.update(visible=online_gen_visible), # online gen row #gr.Slider.update(minimum=8, maximum=24, value=4), # slider generation gr.Row.update(visible=test_model_visible), # tested model row #gr.Dropdown.update(visible=test_model_visible), # tested model selection dropdown gr.Accordion.update(visible=test_model_visible, label=f"Test sentences ({len(G_TEST_SENTENCES)})"), # accordion gr.update(visible=True), # Row sentences gr.DataFrame.update(value=G_TEST_SENTENCES), #DataFrame test sentences gr.update(visible=bias_gen_states[0]), # gen btn gr.update(visible=bias_gen_states[1], value=bias_test_label) # bias btn ) # Interaction with top tabs def moveStep1(): variants = ["primary","secondary","secondary"] #inter = [True, False, False] tabs = [True, False, False] return (gr.update(variant=variants[0]), gr.update(variant=variants[1]), gr.update(variant=variants[2]), gr.update(visible=tabs[0]), gr.update(visible=tabs[1]), gr.update(visible=tabs[2])) # Interaction with top tabs def moveStep1_clear(): variants = ["primary","secondary","secondary"] #inter = [True, False, False] tabs = [True, False, False] return (gr.update(variant=variants[0]), gr.update(variant=variants[1]), gr.update(variant=variants[2]), gr.update(visible=tabs[0]), gr.update(visible=tabs[1]), gr.update(visible=tabs[2]), gr.Textbox.update(value=""), gr.Textbox.update(value=""), gr.Textbox.update(value=""), gr.Textbox.update(value="")) def moveStep2(): variants = ["secondary","primary","secondary"] #inter = [True, True, False] tabs = [False, True, False] return (gr.update(variant=variants[0]), gr.update(variant=variants[1]), gr.update(variant=variants[2]), gr.update(visible=tabs[0]), gr.update(visible=tabs[1]), gr.update(visible=tabs[2]), gr.Checkbox.update(value=False)) def moveStep3(): variants = ["secondary","secondary","primary"] #inter = [True, True, False] tabs = [False, False, True] return (gr.update(variant=variants[0]), gr.update(variant=variants[1]), gr.update(variant=variants[2]), gr.update(visible=tabs[0]), gr.update(visible=tabs[1]), gr.update(visible=tabs[2])) def _genSentenceCoverMsg(test_sentences, total_att_terms, bias_spec, isGen=False): att_cover_dict = {} print(f"In Coverage: {test_sentences[0:2]}") for sent,alt_sent,gt1,gt2,att in test_sentences: num = att_cover_dict.get(att, 0) att_cover_dict[att] = num+1 att_by_count = dict(sorted(att_cover_dict.items(), key=lambda item: item[1])) num_covered_atts = len(list(att_by_count.keys())) lest_covered_att = list(att_by_count.keys())[0] least_covered_count = att_by_count[lest_covered_att] test_sentences_df = pd.DataFrame(test_sentences, columns=['sentence', 'alt_sentence', "grp_term1", "grp_term2", "att_term"]) # missing sentences for attributes att1_missing, att2_missing = bt_mgr.genMissingAttribBiasSpec(bias_spec, test_sentences_df) print(f"Att 1 missing: {att1_missing}") print(f"Att 2 missing: {att2_missing}") # missing pairs spec bt_mgr.genMissingPairsSpec(bias_spec, test_sentences_df) att1_missing_num = sum([v for k, v in att1_missing.items()]) att2_missing_num = sum([v for k, v in att2_missing.items()]) total_missing = att1_missing_num + att2_missing_num print(f"Total missing: {total_missing}") missing_info = f"Missing {total_missing} sentences to balance attributes " source_msg = "Found" if isGen==False else "Generated" if num_covered_atts >= total_att_terms: if total_missing > 0: info_msg = f"**{source_msg} {len(test_sentences)} sentences covering all bias specification attributes, but some attributes are underepresented. Generating additional {total_missing} sentences is suggested.**" else: info_msg = f"**{source_msg} {len(test_sentences)} sentences covering all bias specification attributes. Please select model to test.**" else: info_msg = f"**{source_msg} {len(test_sentences)} sentences covering {num_covered_atts} of {total_att_terms} attributes. Please select model to test.**" #info_msg = missing_info + info_msg bias_spec['custom_counts'] = [att1_missing, att2_missing] return info_msg, att1_missing, att2_missing, total_missing, bias_spec def retrieveSentences(gr1, gr2, att1, att2, progress=gr.Progress()): global use_paper_sentences, G_NUM_SENTENCES, G_MISSING_SPEC, G_TEST_SENTENCES print("RETRIEVE SENTENCES CLICKED!") G_MISSING_SPEC = [] variants = ["secondary","primary","secondary"] inter = [True, True, False] tabs = [True, False] bias_gen_states = [True, False] bias_gen_label = "Generate New Sentences" bias_test_label = "Test Model for Social Bias" num2gen_update = gr.update(visible=True) #update the number of new sentences to generate prog_vis = [True] err_update = updateErrorMsg(False, "") info_msg_update = gr.Markdown.update(visible=False, value="") openai_gen_row_update = gr.Row.update(visible=True) tested_model_dropdown_update = gr.Dropdown.update(visible=False) tested_model_row_update = gr.Row.update(visible=False) # additinal sentences disabled by default gen_additional_sentence_checkbox_update = gr.Checkbox.update(visible=False) test_sentences = [] bias_spec = getTermsFromGUI(gr1, gr2, att1, att2) g1, g2, a1, a2 = bt_mgr.get_words(bias_spec) total_att_terms = len(a1)+len(a2) all_terms_len = len(g1)+len(g2)+len(a1)+len(a2) print(f"Length of all the terms: {all_terms_len}") if all_terms_len == 0: print("No terms entered!") err_update = updateErrorMsg(True, NO_TERMS_ENTERED_ERROR) variants = ["primary","secondary","secondary"] inter = [True, False, False] tabs = [True, False] prog_vis = [False] #raise gr.Error(NO_TERMS_ENTERED_ERROR) else: tabs = [False, True] progress(0, desc="Fetching saved sentences...") test_sentences = rq_mgr._getSavedSentences(bias_spec, progress, use_paper_sentences) #err_update, _, test_sentences = generateSentences(gr1, gr2, att1, att2, progress) print(f"Type: {type(test_sentences)}") num_sentences = len(test_sentences) print(f"Returned num sentences: {num_sentences}") err_update = updateErrorMsg(False, "") G_NUM_SENTENCES = num_sentences G_TEST_SENTENCES = test_sentences if G_NUM_SENTENCES == 0: print("Test sentences empty!") #raise gr.Error(NO_SENTENCES_ERROR) err_update = updateErrorMsg(True, NO_SENTENCES_ERROR) if len(test_sentences) > 0: info_msg, att1_missing, att2_missing, total_missing, c_bias_spec = _genSentenceCoverMsg(test_sentences, total_att_terms, bias_spec) G_MISSING_SPEC = c_bias_spec print(f"Saving global custom bias specification: {G_MISSING_SPEC}") info_msg_update = gr.Markdown.update(visible=True, value=info_msg) num2gen_update = gr.update(visible=False) bias_gen_label = f"Generate Additional {total_missing} Sentences" if total_missing == 0: print(f"Got {len(test_sentences)}, allowing bias test...") #print(test_sentences) bias_gen_states = [False, True] openai_gen_row_update = gr.Row.update(visible=False) tested_model_dropdown_update = gr.Dropdown.update(visible=True) tested_model_row_update = gr.Row.update(visible=True) # still give the option to generate more sentences gen_additional_sentence_checkbox_update = gr.Checkbox.update(visible=True) else: bias_test_label = "Test Model Using Imbalanced Sentences" bias_gen_states = [True, True] tested_model_dropdown_update = gr.Dropdown.update(visible=True) tested_model_row_update = gr.Row.update(visible=True) return (err_update, # error message openai_gen_row_update, # OpenAI generation gen_additional_sentence_checkbox_update, # optional generate additional sentences num2gen_update, # Number of sentences to genrate tested_model_row_update, #Tested Model Row #tested_model_dropdown_update, # Tested Model Dropdown info_msg_update, # sentences retrieved info update gr.update(visible=prog_vis), # progress bar top gr.update(variant=variants[0], interactive=inter[0]), # breadcrumb btn1 gr.update(variant=variants[1], interactive=inter[1]), # breadcrumb btn2 gr.update(variant=variants[2], interactive=inter[2]), # breadcrumb btn3 gr.update(visible=tabs[0]), # tab 1 gr.update(visible=tabs[1]), # tab 2 gr.Accordion.update(visible=bias_gen_states[1], label=f"Test sentences ({len(test_sentences)})"), # accordion gr.update(visible=True), # Row sentences gr.DataFrame.update(value=test_sentences), #DataFrame test sentences gr.Button.update(visible=bias_gen_states[0], value=bias_gen_label), # gen btn gr.Button.update(visible=bias_gen_states[1], value=bias_test_label), # bias test btn gr.update(value=', '.join(g1)), # gr1_fixed gr.update(value=', '.join(g2)), # gr2_fixed gr.update(value=', '.join(a1)), # att1_fixed gr.update(value=', '.join(a2)) # att2_fixed ) def startBiasTest(test_sentences_df, gr1, gr2, att1, att2, model_name, progress=gr.Progress()): global G_NUM_SENTENCES variants = ["secondary","secondary","primary"] inter = [True, True, True] tabs = [False, False, True] err_update = updateErrorMsg(False, "") if test_sentences_df.shape[0] == 0: G_NUM_SENTENCES = 0 #raise gr.Error(NO_SENTENCES_ERROR) err_update = updateErrorMsg(True, NO_SENTENCES_ERROR) progress(0, desc="Starting social bias testing...") #print(f"Type: {type(test_sentences_df)}") #print(f"Data: {test_sentences_df}") # bloomberg vis att_freqs = {} for att in test_sentences_df["Attribute term"].tolist(): #if att == "speech-language-pathologist" or att == "speech-language pathologist" or att == "speech language pathologist": # print(f"Special case in bloomberg: {att}") # att = "speech-language pathologist" if att in att_freqs: att_freqs[att] += 1 else: att_freqs[att] = 1 #print(f"att_freqs: {att_freqs}") # 1. bias specification bias_spec = getTermsFromGUI(gr1, gr2, att1, att2) #print(f"Bias spec dict: {bias_spec}") g1, g2, a1, a2 = bt_mgr.get_words(bias_spec) # bloomberg vis attributes_g1 = a1 #list(set(a1 + [a.replace(' ','-') for a in a1])) #bias_spec['attributes']['attribute 1'] attributes_g2 = a2 #list(set(a2 + [a.replace(' ','-') for a in a2])) #bias_spec['attributes']['attribute 2'] #print(f"Attributes 1: {attributes_g1}") #print(f"Attributes 2: {attributes_g2}") # 2. convert to templates #test_sentences_df['Template'] = test_sentences_df.apply(bt_mgr.sentence_to_template_df, axis=1) test_sentences_df[['Template','grp_refs']] = test_sentences_df.progress_apply(bt_mgr.ref_terms_sentence_to_template, axis=1) print(f"Columns with templates: {list(test_sentences_df.columns)}") print(test_sentences_df[['Group term 1', 'Group term 2', 'Sentence', 'Alternative Sentence']]) # 3. convert to pairs test_pairs_df = bt_mgr.convert2pairsFromDF(bias_spec, test_sentences_df) print(f"Columns for test pairs: {list(test_pairs_df.columns)}") print(test_pairs_df[['grp_term_1', 'grp_term_2', 'sentence', 'alt_sentence']]) progress(0.05, desc=f"Loading model {model_name}...") # 4. get the per sentence bias scores print(f"Test model name: {model_name}") device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") print(f"Device: {device}") tested_model, tested_tokenizer = bt_mgr._getModelSafe(model_name, device) if tested_model == None: print("Tested model is empty!!!!") err_update = updateErrorMsg(True, MODEL_NOT_LOADED_ERROR) #print(f"Mask token id: {tested_toknizer.mask_token_id}") # sanity check bias test bt_mgr.testModelProbability(model_name, tested_model, tested_tokenizer, device) # testing actual sentences test_score_df, bias_stats_dict = bt_mgr.testBiasOnPairs(test_pairs_df, bias_spec, model_name, tested_model, tested_tokenizer, device, progress) print(f"Test scores: {test_score_df.head(3)}") num_sentences = test_sentences_df.shape[0] #score_templates_df.shape[0] model_bias_dict = {} tested_model = bias_stats_dict['tested_model'] #model_bias_dict[bias_stats_dict['tested_model']] = bias_stats_dict['model_bias'] model_bias_dict[f'Stereotype Score on {tested_model.upper()} using {num_sentences} sentences'] = bias_stats_dict['model_bias'] per_attrib_bias = bias_stats_dict['per_attribute'] #print(f"Per attribute bias:", per_attrib_bias) # bias score #test_pairs_df['bias_score'] = 0 test_pairs_df.loc[test_pairs_df['stereotyped'] == 1, 'bias_score'] = test_pairs_df['top_logit']-test_pairs_df['bottom_logit'] test_pairs_df.loc[test_pairs_df['stereotyped'] == 0, 'bias_score'] = test_pairs_df['bottom_logit']-test_pairs_df['top_logit'] test_pairs_df['stereotyped_b'] = "Unknown" test_pairs_df.loc[test_pairs_df['stereotyped'] == 1, 'stereotyped_b'] = "yes" test_pairs_df.loc[test_pairs_df['stereotyped'] == 0, 'stereotyped_b'] = "no" # Order group terms such that most probable is first def orderGroups(row): group_order = "None/None" sentence_order = ["none","none"] new_grp_refs = [] #list(row['grp_refs']) for grp_pair in list(row['grp_refs']): new_grp_refs.append(("R1","R2")) #print(f"Grp refs: {new_grp_refs}") if row['stereotyped'] == 1: if row["label_1"] == "stereotype": group_order = row['grp_term_1']+"/"+row['grp_term_2'] sentence_order = [row['sentence'], row['alt_sentence']] new_grp_refs = [] for grp_pair in list(row['grp_refs']): new_grp_refs.append((grp_pair[0], grp_pair[1])) else: group_order = row['grp_term_2']+"/"+row['grp_term_1'] sentence_order = [row['alt_sentence'], row['sentence']] new_grp_refs = [] for grp_pair in list(row['grp_refs']): new_grp_refs.append((grp_pair[1], grp_pair[0])) else: if row["label_1"] == "stereotype": group_order = row['grp_term_2']+"/"+row['grp_term_1'] sentence_order = [row['alt_sentence'], row['sentence']] new_grp_refs = [] for grp_pair in list(row['grp_refs']): new_grp_refs.append((grp_pair[1], grp_pair[0])) else: group_order = row['grp_term_1']+"/"+row['grp_term_2'] sentence_order = [row['sentence'], row['alt_sentence']] new_grp_refs = [] for grp_pair in list(row['grp_refs']): new_grp_refs.append((grp_pair[0], grp_pair[1])) return pd.Series([group_order, sentence_order[0], sentence_order[1], new_grp_refs]) test_pairs_df[['groups_rel','sentence', 'alt_sentence', 'grp_refs']] = test_pairs_df.progress_apply(orderGroups, axis=1) #test_pairs_df['groups_rel'] = test_pairs_df['grp_term_1']+"/"+test_pairs_df['grp_term_2'] # construct display dataframe score_templates_df = test_pairs_df[['att_term','template','sentence','alt_sentence']].copy() score_templates_df['Groups'] = test_pairs_df['groups_rel'] #score_templates_df['Bias Score'] = np.round(test_pairs_df['bias_score'],2) score_templates_df['Stereotyped'] = test_pairs_df['stereotyped_b'] score_templates_df = score_templates_df.rename(columns = {'att_term': "Attribute", "template": "Template", "sentence": "Sentence", "alt_sentence": "Alternative"}) #'Bias Score' score_templates_df = score_templates_df[['Stereotyped','Attribute','Groups','Sentence',"Alternative"]] # bloomberg vis attrib_by_score = dict(sorted(per_attrib_bias.items(), key=lambda item: item[1], reverse=True)) #print(f"Attrib by score:", attrib_by_score) per_attrib_bias_HTML_stereo = "" num_atts = 0 for att, score in attrib_by_score.items(): if att in attributes_g1: #print(f"Attribute 1: {att}") #per_attrib_bias_HTML_stereo += bv.att_bloombergViz(att, score, att_freqs[att]) #num_atts += 1 #if num_atts >= 8: # break per_attrib_bias_HTML_stereo += bv.att_bloombergViz(att, score, att_freqs[att], test_pairs_df, False, False) num_atts += 1 #if num_atts >= 8: # break per_attrib_bias_HTML_antistereo = "" num_atts = 0 for att, score in attrib_by_score.items(): if att in attributes_g2: #print(f"Attribute 2: {att}") #per_attrib_bias_HTML_antistereo += bv.att_bloombergViz(att, score, att_freqs[att], True) #num_atts += 1 #if num_atts >= 8: # break per_attrib_bias_HTML_antistereo += bv.att_bloombergViz(att, score, att_freqs[att], test_pairs_df, True, True) num_atts += 1 #if num_atts >= 8: # break interpret_msg = bt_mgr._constructInterpretationMsg(bias_spec, num_sentences, model_name, bias_stats_dict, per_attrib_bias, score_templates_df ) saveBiasTestResult(test_sentences_df, gr1, gr2, att1, att2, model_name) return (err_update, # error message gr.Markdown.update(visible=True), # bar progress gr.Button.update(variant=variants[0], interactive=inter[0]), # top breadcrumb button 1 gr.Button.update(variant=variants[1], interactive=inter[1]), # top breadcrumb button 2 gr.Button.update(variant=variants[2], interactive=inter[2]), # top breadcrumb button 3 gr.update(visible=tabs[0]), # content tab/column 1 gr.update(visible=tabs[1]), # content tab/column 2 gr.update(visible=tabs[2]), # content tab/column 3 model_bias_dict, # per model bias score gr.update(value=per_attrib_bias_HTML_stereo), # per attribute bias score stereotyped gr.update(value=per_attrib_bias_HTML_antistereo), # per attribute bias score antistereotyped gr.update(value=score_templates_df, visible=True), # Pairs with scores gr.update(value=interpret_msg, visible=True), # Interpretation message gr.update(value=', '.join(g1)), # gr1_fixed gr.update(value=', '.join(g2)), # gr2_fixed gr.update(value=', '.join(a1)), # att1_fixed gr.update(value=', '.join(a2)) # att2_fixed ) # Loading the Interface first time def loadInterface(): print("Loading the interface...") #open_ai_key = cookie_mgr.loadOpenAIKey() #return gr.Textbox.update(value=open_ai_key) # Selecting an attribute label in the label component def selectAttributeLabel(evt: gr.SelectData): print(f"Selected {evt.value} at {evt.index} from {evt.target}") object_methods = [method_name for method_name in dir(evt) if callable(getattr(evt, method_name))] print("Attributes:") for att in dir(evt): print (att, getattr(evt,att)) print(f"Methods: {object_methods}") return () # Editing a sentence in DataFrame def editSentence(test_sentences, evt: gr.EventData): print(f"Edit Sentence: {evt}") #print("--BEFORE---") #print(test_sentences[0:10]) #print("--AFTER--") #print(f"Data: {evt._data['data'][0:10]}") # print("Attributes:") # for att in dir(evt): # print (att, getattr(evt,att)) # object_methods = [method_name for method_name in dir(evt) # if callable(getattr(evt, method_name))] # print(f"Methods: {object_methods}") # exports dataframe as CSV def export_csv(test_pairs, gr1, gr2, att1, att2): bias_spec = getTermsFromGUI(gr1, gr2, att1, att2) g1, g2, a1, a2 = bt_mgr.get_words(bias_spec) b_name = rq_mgr.getBiasName(g1, g2, a1, a2) print(f"Exporting test pairs for {b_name}") fname = f"test_pairs_{b_name}.csv" test_pairs.to_csv(fname) return gr.File.update(value=fname, visible=True) # Enable Generation of new sentences, even though not required. def useOnlineGen(value): online_gen_row_update = gr.Row.update(visible=False) num_sentences2gen_update = gr.Slider.update(visible=False) gen_btn_update = gr.Button.update(visible=False) gen_title_update = gr.Markdown.update(visible=False) openai_key_update = gr.Textbox.update(visible=False) if value == True: print("Check is true...") online_gen_row_update = gr.Row.update(visible=True) num_sentences2gen_update = gr.Slider.update(visible=True) gen_btn_update = gr.Button.update(visible=True, value="Generate Additional Sentences") gen_title_update = gr.Markdown.update(visible=True) openai_key_update = gr.Textbox.update(visible=True) else: print("Check is false...") return (online_gen_row_update, num_sentences2gen_update, gen_btn_update #gen_title_update, #openai_key_update, ) def changeTerm(evt: gr.EventData): global G_CORE_BIAS_NAME print("Bias is custom now...") G_CORE_BIAS_NAME = None return gr.update(interactive=False, visible=False) def saveBiasTestResult(test_sentences_df, group1, group2, att1, att2, model_name): print(f"Saving bias test result...") #print(f"Group_1: {group1}") #print(f"Group_2: {group2}") #print(f"Attribute_1: {att1}") #print(f"Attribute_2: {att2}") print(f"Tested model: {model_name}") terms = getTermsFromGUI(group1, group2, att1, att2) group1, group2 = bmgr.getSocialGroupTerms(terms) att1, att2 = bmgr.getAttributeTerms(terms) bias_name = rq_mgr.getBiasName(group1, group2, att1, att2) print(f"bias_name: {bias_name}") print(f"Terms: {terms}") bias_spec_json = { "name": bias_name, "source": "bias-test-gpt-tool", "social_groups": terms['social_groups'], "attributes": terms['attributes'], "tested_results": { "tested_model": model_name }, "templates": [], "sentences": [] } bmgr.save_custom_bias(f"{bias_name}.json", bias_spec_json) #return gr.update(value="Bias test result saved!", visible=True) theme = gr.themes.Soft().set( button_small_radius='*radius_xxs', background_fill_primary='*neutral_50', border_color_primary='*primary_50' ) soft = gr.themes.Soft( primary_hue="slate", spacing_size="sm", radius_size="md" ).set( # body_background_fill="white", button_primary_background_fill='*primary_400' ) css_adds = "#group_row {background: white; border-color: white;} \ #attribute_row {background: white; border-color: white;} \ #tested_model_row {background: white; border-color: white;} \ #button_row {background: white; border-color: white} \ #examples_elem .label {display: none}\ #att1_words {border-color: white;} \ #att2_words {border-color: white;} \ #group1_words {border-color: white;} \ #group2_words {border-color: white;} \ #att1_words_fixed {border-color: white;} \ #att2_words_fixed {border-color: white;} \ #group1_words_fixed {border-color: white;} \ #group2_words_fixed {border-color: white;} \ #att1_words_fixed input {box-shadow:None; border-width:0} \ #att1_words_fixed .scroll-hide {box-shadow:None; border-width:0} \ #att2_words_fixed input {box-shadow:None; border-width:0} \ #att2_words_fixed .scroll-hide {box-shadow:None; border-width:0} \ #group1_words_fixed input {box-shadow:None; border-width:0} \ #group1_words_fixed .scroll-hide {box-shadow:None; border-width:0} \ #group2_words_fixed input {box-shadow:None; border-width:0} \ #group2_words_fixed .scroll-hide {box-shadow:None; border-width:0} \ #tested_model_drop {border-color: white;} \ #gen_model_check {border-color: white;} \ #gen_model_check .wrap {border-color: white;} \ #gen_model_check .form {border-color: white;} \ #open_ai_key_box {border-color: white;} \ #gen_col {border-color: white;} \ #gen_col .form {border-color: white;} \ #res_label {background-color: #F8FAFC;} \ #per_attrib_label_elem {background-color: #F8FAFC;} \ #accordion {border-color: #E5E7EB} \ #err_msg_elem p {color: #FF0000; cursor: pointer} \ #res_label .bar {background-color: #35d4ac; } \ #bloomberg_legend {background: white; border-color: white} \ #bloomberg_att1 {background: white; border-color: white} \ #bloomberg_att2 {background: white; border-color: white} \ .tooltiptext_left {visibility: hidden;max-width:50ch;min-width:25ch;top: 100%;left: 0%;background-color: #222;text-align: center;border-radius: 6px;padding: 5px 0;position: absolute;z-index: 1;} \ .tooltiptext_right {visibility: hidden;max-width:50ch;min-width:25ch;top: 100%;right: 0%;background-color: #222;text-align: center;border-radius: 6px;padding: 5px 0;position: absolute;z-index: 1;} \ #filled:hover .tooltiptext_left {visibility: visible;} \ #empty:hover .tooltiptext_left {visibility: visible;} \ #filled:hover .tooltiptext_right {visibility: visible;} \ #empty:hover .tooltiptext_right {visibility: visible;}" #'bethecloud/storj_theme' with gr.Blocks(theme=soft, title="Social Bias Testing in Language Models", css=css_adds) as iface: with gr.Row(): with gr.Group(): s1_btn = gr.Button(value="Step 1: Bias Specification", variant="primary", visible=True, interactive=True, size='sm')#.style(size='sm') s2_btn = gr.Button(value="Step 2: Test Sentences", variant="secondary", visible=True, interactive=False, size='sm')#.style(size='sm') s3_btn = gr.Button(value="Step 3: Bias Testing", variant="secondary", visible=True, interactive=False, size='sm')#.style(size='sm') err_message = gr.Markdown("", visible=False, elem_id="err_msg_elem") bar_progress = gr.Markdown(" ") # Page 1 with gr.Column(visible=True) as tab1: with gr.Column(): gr.Markdown("### Social Bias Specification") gr.Markdown("Use one of the predefined specifications or enter own terms for social groups and attributes") with gr.Row(): example_biases = gr.Dropdown( value="Select a predefined bias to test", allow_custom_value=False, interactive=True, choices=[ #"Flowers/Insects <> Pleasant/Unpleasant", #"Instruments/Weapons <> Pleasant/Unpleasant", "Male/Female <> Professions", "Male/Female <> Science/Art", "Male/Female <> Career/Family", "Male/Female <> Math/Art", "Eur.-American/Afr.-American <> Pleasant/Unpleasant #1", "Eur.-American/Afr.-American <> Pleasant/Unpleasant #2", "Eur.-American/Afr.-American <> Pleasant/Unpleasant #3", "African-Female/European-Male <> Intersectional", "African-Female/European-Male <> Emergent", "Mexican-Female/European-Male <> Intersectional", "Mexican-Female/European-Male <> Emergent", "Young/Old Name <> Pleasant/Unpleasant", #"Mental/Physical Disease <> Temporary/Permanent", # Custom Biases "Male/Female <> Care/Expertise", "Hispanic/Caucasian <> Treatment-Adherence", "Afr.-American/Eur.American <> Risky-Health-Behaviors" ], label="Example Biases", #info="Select a predefied bias specification to fill-out the terms below." ) with gr.Row(elem_id="group_row"): group1 = gr.Textbox(label="Social Group 1", max_lines=1, elem_id="group1_words", elem_classes="input_words", placeholder="brother, father") group2 = gr.Textbox(label='Social Group 2', max_lines=1, elem_id="group2_words", elem_classes="input_words", placeholder="sister, mother") with gr.Row(elem_id="attribute_row"): att1 = gr.Textbox(label='Stereotype for Group 1', max_lines=1, elem_id="att1_words", elem_classes="input_words", placeholder="science, technology") att2 = gr.Textbox(label='Anti-stereotype for Group 1', max_lines=1, elem_id="att2_words", elem_classes="input_words", placeholder="poetry, art") with gr.Row(): gr.Markdown(" ") get_sent_btn = gr.Button(value="Get Sentences", variant="primary", visible=True) gr.Markdown(" ") # Page 2 with gr.Column(visible=False) as tab2: info_sentences_found = gr.Markdown(value="", visible=False) gr.Markdown("### Tested Social Bias Specification", visible=True) with gr.Row(): group1_fixed = gr.Textbox(label="Social Group 1", max_lines=1, elem_id="group1_words_fixed", elem_classes="input_words", interactive=False, visible=True) group2_fixed = gr.Textbox(label='Social Group 2', max_lines=1, elem_id="group2_words_fixed", elem_classes="input_words", interactive=False, visible=True) with gr.Row(): att1_fixed = gr.Textbox(label='Stereotype for Group 1', max_lines=1, elem_id="att1_words_fixed", elem_classes="input_words", interactive=False, visible=True) att2_fixed = gr.Textbox(label='Anti-stereotype for Group 1', max_lines=1, elem_id="att2_words_fixed", elem_classes="input_words", interactive=False, visible=True) with gr.Row(): with gr.Column(): additional_gen_check = gr.Checkbox(label="Generate Additional Sentences with ChatGPT (requires Open AI Key)", visible=False, interactive=True, value=False, elem_id="gen_model_check") with gr.Row(visible=False) as online_gen_row: with gr.Column(): gen_title = gr.Markdown("### Generate Additional Sentences", visible=True) # OpenAI Key for generator openai_key = gr.Textbox(lines=1, label="OpenAI API Key", value=None, placeholder="starts with sk-", info="Please provide the key for an Open AI account to generate new test sentences", visible=True, interactive=True, elem_id="open_ai_key_box") num_sentences2gen = gr.Slider(1, 20, value=5, step=1, interactive=True, visible=True, info="Five or more per attribute are recommended for a good bias estimate.", label="Number of test sentences to generate per attribute", container=True)#.style(container=True) #, info="Number of Sentences to Generate") with gr.Row(visible=False) as tested_model_row: with gr.Column(): gen_title = gr.Markdown("### Select Tested Model", visible=True) # Tested Model Selection - "openlm-research/open_llama_7b", "tiiuae/falcon-7b" tested_model_name = gr.Dropdown( ["bert-base-uncased","bert-large-uncased","gpt2","gpt2-medium","gpt2-large","emilyalsentzer/Bio_ClinicalBERT","microsoft/biogpt","openlm-research/open_llama_3b","openlm-research/open_llama_7b"], value="bert-base-uncased", multiselect=None, interactive=True, label="Tested Language Model", elem_id="tested_model_drop", visible=True #info="Select the language model to test for social bias." ) with gr.Row(): gr.Markdown(" ") gen_btn = gr.Button(value="Generate New Sentences", variant="primary", visible=True) bias_btn = gr.Button(value="Test Model for Social Bias", variant="primary", visible=False) gr.Markdown(" ") with gr.Row(visible=False) as row_sentences: with gr.Accordion(label="Test Sentences", open=False, visible=False) as acc_test_sentences: test_sentences = gr.DataFrame( headers=["Sentence", "Alternative Sentence", "Group term 1", "Group term 2", "Attribute term"], datatype=["str", "str", "str", "str", "str"], row_count=(1, 'dynamic'), col_count=(5, 'fixed'), interactive=True, visible=True, #label="Generated Test Sentences", max_rows=2, overflow_row_behaviour="paginate") # Page 3 with gr.Column(visible=False) as tab3: gr.Markdown("### Tested Social Bias Specification") with gr.Row(): group1_fixed2 = gr.Textbox(label="Social Group 1", max_lines=1, elem_id="group1_words_fixed", elem_classes="input_words", interactive=False) group2_fixed2 = gr.Textbox(label='Social Group 2', max_lines=1, elem_id="group2_words_fixed", elem_classes="input_words", interactive=False) with gr.Row(): att1_fixed2 = gr.Textbox(label='Stereotype for Group 1', max_lines=1, elem_id="att1_words_fixed", elem_classes="input_words", interactive=False) att2_fixed2 = gr.Textbox(label='Anti-stereotype for Group 1', max_lines=1, elem_id="att2_words_fixed", elem_classes="input_words", interactive=False) with gr.Row(): with gr.Column(scale=2): gr.Markdown("### Bias Test Results") #with gr.Column(scale=1): # gr.Markdown("### Interpretation") with gr.Row(): with gr.Column(scale=2): lbl_model_bias = gr.Markdown("**Model Bias** - % stereotyped choices (↑ more bias)") model_bias_label = gr.Label(num_top_classes=1, label="% stereotyped choices (↑ more bias)", elem_id="res_label", show_label=False) with gr.Accordion("Additional Interpretation", open=False, visible=True): interpretation_msg = gr.HTML(value="Interpretation: Stereotype Score metric details in Nadeem'20", visible=False) lbl_attrib_bias = gr.Markdown("**Bias in the Context of Attributes** - % stereotyped choices (↑ more bias)") #gr.Markdown("**Legend**") #attribute_bias_labels = gr.Label(num_top_classes=8, label="Per attribute: % stereotyped choices (↑ more bias)", # elem_id="per_attrib_label_elem", # show_label=False) #with gr.Column(scale=1): with gr.Row(): with gr.Column(variant="compact", elem_id="bloomberg_legend"): gr.HTML("
  Group 1 more probable in the sentence
  
  Group 2 more probable in the sentence
") with gr.Row(): with gr.Column(variant="compact", elem_id="bloomberg_att1"): gr.Markdown("#### Attribute Group 1") attribute_bias_html_stereo = gr.HTML() with gr.Column(variant="compact", elem_id="bloomberg_att2"): gr.Markdown("#### Attribute Group 2") attribute_bias_html_antistereo = gr.HTML() gr.HTML(value="Visualization inspired by Bloomberg article on bias in text-to-image models.") save_msg = gr.HTML(value="Bias test result saved! ", visible=False) with gr.Row(): with gr.Column(scale=2): with gr.Accordion("Per Sentence Bias Results", open=False, visible=True): test_pairs = gr.DataFrame( headers=["group_term", "template", "att_term_1", "att_term_2","label_1","label_2"], datatype=["str", "str", "str", "str", "str", "str"], row_count=(1, 'dynamic'), #label="Bias Test Results Per Test Sentence Template", max_rows=2, overflow_row_behaviour="paginate" ) with gr.Row(): # export button gr.Markdown(" ") with gr.Column(): exp_button = gr.Button("Export Test Sentences as CSV", variant="primary") csv = gr.File(interactive=False, visible=False) new_bias_button = gr.Button("Try New Bias Test", variant="primary") gr.Markdown(" ") # initial interface load #iface.load(fn=loadInterface, # inputs=[], # outputs=[openai_key]) # select from predefined bias specifications example_biases.select(fn=prefillBiasSpec, inputs=None, outputs=[group1, group2, att1, att2, csv]) # Get sentences get_sent_btn.click(fn=retrieveSentences, inputs=[group1, group2, att1, att2], outputs=[err_message, online_gen_row, additional_gen_check, num_sentences2gen, tested_model_row, #tested_model_name, info_sentences_found, bar_progress, s1_btn, s2_btn, s3_btn, tab1, tab2, acc_test_sentences, row_sentences, test_sentences, gen_btn, bias_btn, group1_fixed, group2_fixed, att1_fixed, att2_fixed ]) # request getting sentences gen_btn.click(fn=generateSentences, inputs=[group1, group2, att1, att2, openai_key, num_sentences2gen], outputs=[err_message, info_sentences_found, online_gen_row, #num_sentences2gen, tested_model_row, #tested_model_name, acc_test_sentences, row_sentences, test_sentences, gen_btn, bias_btn ]) # Test bias bias_btn.click(fn=startBiasTest, inputs=[test_sentences,group1,group2,att1,att2,tested_model_name], outputs=[err_message, bar_progress, s1_btn, s2_btn, s3_btn, tab1, tab2, tab3, model_bias_label, attribute_bias_html_stereo, attribute_bias_html_antistereo, test_pairs, interpretation_msg, group1_fixed2, group2_fixed2, att1_fixed2, att2_fixed2] ) # top breadcrumbs s1_btn.click(fn=moveStep1, inputs=[], outputs=[s1_btn, s2_btn, s3_btn, tab1, tab2, tab3]) # top breadcrumbs s2_btn.click(fn=moveStep2, inputs=[], outputs=[s1_btn, s2_btn, s3_btn, tab1, tab2, tab3, additional_gen_check]) # top breadcrumbs s3_btn.click(fn=moveStep3, inputs=[], outputs=[s1_btn, s2_btn, s3_btn, tab1, tab2, tab3]) # start testing new bias new_bias_button.click(fn=moveStep1_clear, inputs=[], outputs=[s1_btn, s2_btn, s3_btn, tab1, tab2, tab3, group1, group2, att1, att2]) # Additional Interactions #attribute_bias_labels.select(fn=selectAttributeLabel, # inputs=[], # outputs=[]) # Editing a sentence test_sentences.change(fn=editSentence, inputs=[test_sentences], outputs=[] ) # tick checkbox to use online generation additional_gen_check.change(fn=useOnlineGen, inputs=[additional_gen_check], outputs=[online_gen_row, num_sentences2gen, gen_btn])#, gen_title, openai_key]) exp_button.click(export_csv, inputs=[test_pairs, group1, group2, att1, att2], outputs=[csv]) # Changing any of the bias specification terms group1.change(fn=changeTerm, inputs=[], outputs=[csv]) group2.change(fn=changeTerm, inputs=[], outputs=[csv]) att1.change(fn=changeTerm, inputs=[], outputs=[csv]) att2.change(fn=changeTerm, inputs=[], outputs=[csv]) iface.queue(concurrency_count=2).launch()