mgr_requests.py

import pandas as pd
import gradio as gr
import hashlib, base64
import openai

# querying OpenAI for generation
from openAI_manager import initOpenAI, examples_to_prompt, genChatGPT, generateTestSentences

# bias testing manager
import mgr_bias_scoring as bt_mgr
import mgr_sentences as smgr

# error messages
from error_messages import *

# hashing
def getHashForString(text):
  d=hashlib.md5(bytes(text, encoding='utf-8')).digest()
  d=base64.urlsafe_b64encode(d)

  return d.decode('utf-8')

def getBiasName(gr1_lst, gr2_lst, att1_lst, att2_lst):
  full_spec = ''.join(gr1_lst)+''.join(gr2_lst)+''.join(att1_lst)+''.join(att2_lst)
  hash = getHashForString(full_spec)
  bias_name = f"{gr1_lst[0].replace(' ','-')}_{gr2_lst[0].replace(' ','-')}__{att1_lst[0].replace(' ','-')}_{att2_lst[0].replace(' ','-')}_{hash}"
  
  return bias_name


def _generateOnline(bias_spec, progress, key, num2gen, isSaving=False):
    test_sentences = []

    # Initiate with key
    try:
        models = initOpenAI(key)
        model_names = [m['id'] for m in models['data']]
        print(f"Model names: {model_names}")
    except openai.error.AuthenticationError as err:
        raise gr.Error(OPENAI_INIT_ERROR.replace("<ERR>", str(err)))
        
    if "gpt-3.5-turbo" in model_names:
        print("Access to ChatGPT")
    if "gpt-4" in model_names:
        print("Access to GPT-4")

    model_name = "gpt-3.5-turbo"

    # Generate one example
    gen = genChatGPT(model_name, ["man","math"], 2, 5, 
                [{"Keywords": ["sky","blue"], "Sentence": "the sky is blue"}
                ], 
                temperature=0.8)
    print(f"Test gen: {gen}")

    # Generate all test sentences
    print(f"Bias spec dict: {bias_spec}")

    g1, g2, a1, a2 = bt_mgr.get_words(bias_spec)
    gens = generateTestSentences(model_name, g1+g2, a1+a2, num2gen, progress)
    print("--GENS--")
    print(gens)

    for gt, at, s in gens:
        test_sentences.append([s,gt,at])

    # save the generations immediately
    print("Saving generations to HF DF...")
    save_df = pd.DataFrame(test_sentences, columns=["Test sentence",'Group term', "Attribute term"])

    ## make the templates to save
    # 1. bias specification
    print(f"Bias spec dict: {bias_spec}")

    # 2. convert to templates
    save_df['Template'] = save_df.apply(bt_mgr.sentence_to_template, axis=1)
    print(f"Data with template: {save_df}")

    # 3. convert to pairs
    test_pairs_df = bt_mgr.convert2pairs(bias_spec, save_df)
    print(f"Test pairs cols: {list(test_pairs_df.columns)}")

    bias_name = getBiasName(g1, g2, a1, a2)

    save_df = save_df.rename(columns={'Group term':'org_grp_term',
                            "Attribute term": 'att_term',
                            "Test sentence":'sentence',
                            "Template":"template"})
    
    save_df['grp_term1'] = test_pairs_df['att_term_1']
    save_df['grp_term2'] = test_pairs_df['att_term_2']
    save_df['label_1'] = test_pairs_df['label_1']
    save_df['label_2'] = test_pairs_df['label_2']
    save_df['bias_spec'] = bias_name
    save_df['type'] = 'tool'
    save_df['gen_model'] = model_name

    if isSaving == True:
        print(f"Save cols: {list(save_df.columns)}")
        print(f"Save: {save_df.head(1)}")
        #smgr.saveSentences(save_df) #[["Group term","Attribute term","Test sentence"]])

    num_sentences = len(test_sentences)
    print(f"Returned num sentences: {num_sentences}")

    return test_sentences

def _getSavedSentences(bias_spec, progress, use_paper_sentences):
    test_sentences = []

    print(f"Bias spec dict: {bias_spec}")

    g1, g2, a1, a2 = bt_mgr.get_words(bias_spec)
    for gi, g_term in enumerate(g1+g2):
        att_list = a1+a2
        # match "-" and no space
        att_list_dash = [t.replace(' ','-') for t in att_list]
        att_list.extend(att_list_dash)
        att_list_nospace = [t.replace(' ','') for t in att_list]
        att_list.extend(att_list_nospace)
        att_list = list(set(att_list))

        progress(gi/len(g1+g2), desc=f"{g_term}")

        _, sentence_df, _ = smgr.getSavedSentences(g_term)
        # only take from paper & gpt3.5
        flt_gen_models = ["gpt-3.5","gpt-3.5-turbo"]
        print(f"Before filter: {sentence_df.shape[0]}")
        if use_paper_sentences == True:
            if 'type' in list(sentence_df.columns):
                sentence_df = sentence_df.query("type=='paper' and gen_model in @flt_gen_models")
                print(f"After filter: {sentence_df.shape[0]}")
        else:
            if 'type' in list(sentence_df.columns):
                # only use GPT-3.5 generations for now - todo: add settings option for this
                sentence_df = sentence_df.query("gen_model in @flt_gen_models")
                print(f"After filter: {sentence_df.shape[0]}")

        if sentence_df.shape[0] > 0:
            sentence_df = sentence_df[['org_grp_term','att_term','sentence']]
            sentence_df = sentence_df.rename(columns={'org_grp_term': "Group term",
                                                        "att_term": "Attribute term",
                                                        "sentence": "Test sentence"})

            sel = sentence_df[sentence_df['Attribute term'].isin(att_list)].values
            if len(sel) > 0:
                for gt,at,s in sel:
                    test_sentences.append([s,gt,at])
        else:
            print("Test sentences empty!")
            #raise gr.Error(NO_SENTENCES_ERROR)

    return test_sentences