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| import gradio as gr | |
| import sentence_transformers | |
| from sentence_transformers import SentenceTransformer | |
| import torch | |
| from sentence_transformers.util import semantic_search | |
| import pandas as pd | |
| import requests | |
| #Import corpus embeddings | |
| corpus_ger = pd.read_json('corpus_embed_ger_model5.json') | |
| corpus_eng = pd.read_json('corpus_embed_eng_mboth.json') | |
| corpus_fr = pd.read_json('corpus_embed_fr_Sahajtomar.json') | |
| #Import models | |
| model_ger = SentenceTransformer('JoBeer/model5') | |
| model_eng = SentenceTransformer('mboth/distil-eng-quora-sentence') | |
| model_fr = SentenceTransformer('Sahajtomar/french_semantic') | |
| #Definition of search function | |
| def predict(name, description, language, classCode='nofilter', top_k=10): | |
| #language detection | |
| if language == 'german': | |
| model = model_ger | |
| corpus = corpus_ger | |
| if language == 'english': | |
| model = model_eng | |
| corpus = corpus_eng | |
| if language == 'french': | |
| model = model_fr | |
| corpus = corpus_fr | |
| text = name + '; ' + description #Verkettung name und description | |
| query_embedding = model.encode(text, convert_to_tensor=True) #Erzeugung Query Embedding | |
| #Filterung ECLASS Corpus | |
| if classCode == 'nofilter': | |
| corpus_filtered = corpus | |
| else: | |
| url = f'https://bcon2-api.azurewebsites.net/api/eclass?codedname={classCode}' | |
| response = requests.get(url) #http request um auf Filter API zuzugreifen | |
| lines = response.text.split('\n') | |
| properties_filtered_list = [line[-21:-1] for line in lines] | |
| corpus_filtered = corpus[corpus['irdi'].isin(properties_filtered_list)] #Der ECLASS Corpus wird mit Hilfe der Liste gefiltert | |
| corpus_filtered.reset_index(drop=True,inplace=True) #Index wird zurückgesetzt | |
| #Umwandlung corpus Embeddings in Tensor | |
| corpus_embeddings = torch.Tensor(corpus_filtered["Embeddings"]) | |
| #Einspeisung Modell | |
| output = sentence_transformers.util.semantic_search(query_embedding, corpus_embeddings, top_k = top_k) | |
| #Auslesen der Modellausgabe | |
| preferedNames = [] | |
| definitions = [] | |
| irdis = [] | |
| scores = [] | |
| for i in range(0,top_k): | |
| preferedNames.append(corpus_filtered.iloc[output[0][i].get('corpus_id'),1]) | |
| definitions.append(corpus_filtered.iloc[output[0][i].get('corpus_id'),2]) | |
| irdis.append(corpus_filtered.iloc[output[0][i].get('corpus_id'),0]) | |
| scores.append(output[0][i].get('score')) | |
| predictions = pd.DataFrame({'preferedName' : preferedNames, 'irdi' : irdis,'score' : scores,'definition' : definitions}) | |
| return predictions | |
| #gradio user interface | |
| with gr.Blocks() as demo: | |
| with gr.Row(): | |
| gr.Markdown("""# ECLASS-Search-Demo | |
| This is a semantic search application that maps unknown product properties to the ECLASS standard. It is created by ECLASS e.V. in collaboration with the GART-labortory of the cologne university of applied science.""") #This demo provides a semantic search application for ECLASS features. | |
| with gr.Row(): | |
| #inputs | |
| name_tx = gr.Textbox(label="Name:",placeholder="Name of the Property", lines=1) | |
| description_tx = gr.Textbox(label="Description:", placeholder="Description of the Property", lines=1) | |
| with gr.Row(): | |
| #inputs | |
| classcode_tx = gr.Textbox(value='nofilter', label="Filter with ECLASS ClassCode", placeholder="type nofilter", lines=1) | |
| top_k_nu = gr.Number(value=10, label="Number of Matches") | |
| language_drop = gr.Dropdown(["german", "english","french"], value='german', label="Select language") | |
| #button | |
| search = gr.Button("search") | |
| #output | |
| prediction_df = gr.Dataframe(headers = ['preferedName', 'irdi', 'score', 'definition']) | |
| #Hinterlegt Search-Function für button "search" | |
| search.click(fn=predict, inputs=[name_tx,description_tx,language_drop,classcode_tx,top_k_nu], outputs=prediction_df) | |
| demo.launch(debug=True) |