| import gradio as gr | |
| import pandas as pd | |
| import torch | |
| from datasets import load_dataset | |
| from sentence_transformers import SentenceTransformer, util, models | |
| q_encoder = SentenceTransformer(modules=[ | |
| models.Transformer(model_name_or_path="checkpoints/q_encoder", max_seq_length=512), | |
| models.Pooling(word_embedding_dimension=768, pooling_mode='cls'), | |
| ]) | |
| doc_embeddings = torch.load('checkpoints/doc_embeddings.pt', map_location=torch.device('cpu')) | |
| docs = pd.DataFrame(load_dataset("antoiloui/bsard", data_files="articles_fr.csv")['train']) | |
| def search(question): | |
| q_emb = q_encoder.encode(question, convert_to_tensor=True) | |
| hits = util.semantic_search(q_emb, doc_embeddings, top_k=100, score_function=util.cos_sim)[0] | |
| return {docs.loc[h['corpus_id'], 'article'] + '\n\n' + f"- Art. {docs.loc[h['corpus_id'], 'article_no']}, {docs.loc[h['corpus_id'], 'code']}" for h in hits[:5]} | |
| gr.Interface( | |
| fn=search, | |
| inputs=['text'], | |
| outputs=['textbox']*5, | |
| title="Legislation Search 🇧🇪", | |
| description="", | |
| allow_flagging="auto", | |
| examples=["Qu'est-ce que je risque si je viole le secret professionnel ?", "Mon employeur peut-il me licencier alors que je suis malade ?"] | |
| ).launch(share=False, enable_queue=False) | |