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import pandas as pd |
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import numpy as np |
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import sqlite3, torch, json, re, os, torch, itertools, html2text |
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from ast import literal_eval as leval |
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from tqdm.auto import tqdm |
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from utils.verbalisation_module import VerbModule |
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from utils.sentence_retrieval_module import SentenceRetrievalModule |
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from utils.textual_entailment_module import TextualEntailmentModule |
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from importlib import reload |
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import llm_load |
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from html.parser import HTMLParser |
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from sentence_transformers import SentenceTransformer |
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from sklearn.metrics.pairwise import cosine_similarity |
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from tqdm import tqdm |
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import gradio as gr |
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def verbalisation(claim_df): |
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verb_module = VerbModule() |
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triples = [] |
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for _, row in claim_df.iterrows(): |
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triple = { |
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'subject': row['entity_label'], |
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'predicate': row['property_label'], |
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'object': row['object_label'] |
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} |
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triples.append(triple) |
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claim_df['verbalisation'] = verb_module.verbalise_triples(triples) |
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claim_df['verbalisation_unks_replaced'] = claim_df['verbalisation'].apply(verb_module.replace_unks_on_sentence) |
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claim_df['verbalisation_unks_replaced_then_dropped'] = claim_df['verbalisation'].apply(lambda x: verb_module.replace_unks_on_sentence(x, empty_after=True)) |
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return claim_df |
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def RelevantSentenceSelection(verbalised_claims_df_final, reference_text_df, update_progress): |
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join_df = pd.merge(verbalised_claims_df_final, reference_text_df[['reference_id', 'url', 'html']], on='reference_id', how='left') |
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tokenizer, model = llm_load.llmLoad(4096) |
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h = html2text.HTML2Text() |
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h.ignore_links = True |
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filtered_htmls = [] |
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answers = [] |
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verifications = [] |
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for idx, (html, verb) in enumerate(zip(join_df['html'], join_df['verbalisation'])): |
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try: |
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filtered_html = h.handle(html) |
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filtered_htmls.append(filtered_html) |
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instruct = "Find the most relevant sentences from the filtered HTML document based on the given target sentence. If there are no directly related sentences, try to find sentences that provide context or background information related to the target sentence. Only answer 'nothing' if there is absolutely no relevant information in the document. Do not include any HTML tags or markup in your answer." |
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question = f"target sentence:'{verb}', filtered HTML document:{filtered_html}" |
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answer = llm_load.llmQuestion(tokenizer, model, instruct, question, output_size=128) |
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answers.append(answer) |
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except: |
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answers.append('Malformed html') |
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instruct = "Determine whether the target sentence is supported by the given evidence or not. If so, answer 'supportive'. It not, answer 'No supports'. Or, you can't determine with the given evidence, then asnwer 'Not enough information'" |
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question = f"target sentence:'{verb}', evidence:{answers[-1]}" |
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verification = llm_load.llmQuestion(tokenizer, model, instruct, question, output_size=64) |
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verifications.append(verification) |
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update_progress(idx, len(join_df)) |
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return pd.DataFrame({'verbalisation': join_df['verbalisation'], 'verification': verifications, 'evidence_set': answers, 'url': join_df['url'], 'filtered_html': filtered_htmls}) |
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if __name__ == '__main__': |
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target_QID = 'Q42' |
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conn = sqlite3.connect('wikidata_claims_refs_parsed.db') |
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query = f"SELECT * FROM claim_text WHERE entity_id = '{target_QID}'" |
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claim_df = pd.read_sql_query(query, conn) |
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query = f"SELECT * FROM html_text Where entity_id = '{target_QID}'" |
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reference_text_df = pd.read_sql_query(query, conn) |
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verbalised_claims_df_final = verbalisation(claim_df) |
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progress = gr.Progress(len(verbalised_claims_df_final)) |
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def update_progress(curr_step, total_steps): |
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progress((curr_step + 1) / total_steps) |
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result = RelevantSentenceSelection(verbalised_claims_df_final, reference_text_df, update_progress) |
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conn.commit() |
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conn.close() |
