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| import gradio as gr | |
| from sentence_transformers import SentenceTransformer, util | |
| threshold = 0.65 | |
| sentence_length = 6 | |
| questions = [ | |
| "Is it new or used", "Are there any wear & tear", "Does it come with dust bag, receipt & original box", | |
| "Are there any scratches, marks", "Are there any fading, stains, discolorization", | |
| "Is this item customized, repainted or has hardware been replaced", "Is it special edition", "Is there any odour", | |
| "Are there multiple items or extra add-ons in this listing?", | |
| "Is there a date code or serial number present on the item?" | |
| ] | |
| model = SentenceTransformer("all-MiniLM-L6-v2") | |
| def generate_phrases(desc: str, length: int): | |
| desc_list = desc.split() | |
| phrase_list = [] | |
| if len(desc_list) >= length: | |
| for i in range(len(desc_list) - (length - 1)): | |
| sub_list = [] | |
| for j in range(i, i + length): | |
| sub_list.append(desc_list[j]) | |
| phrase_list.append(' '.join(sub_list)) | |
| else: | |
| phrase_list.append(' '.join(desc_list)) | |
| return phrase_list | |
| def extract(description: str): | |
| sentences = generate_phrases(description, sentence_length) | |
| sentences_embedding = model.encode(sentences) | |
| answers = [] | |
| for question in questions: | |
| query_embedding = model.encode(question) | |
| similarities = util.cos_sim(query_embedding, sentences_embedding) | |
| similarity_i = 0 | |
| new_row = None | |
| for similarity in similarities[0]: | |
| model_answer = sentences[similarity_i] | |
| similarity_i += 1 | |
| if round(similarity.item(), 2) > threshold: | |
| if new_row is not None and similarity < new_row['Similarity']: | |
| continue | |
| new_row = {'ModelAnswer': model_answer, 'Similarity': similarity.item()} | |
| if new_row is not None: | |
| answers.append(new_row['ModelAnswer']) | |
| else: | |
| answers.append('No answer') | |
| return answers | |
| def map_question_to_text(question): | |
| return gr.Text(label=question) | |
| demo = gr.Interface(fn=extract, inputs=gr.Textbox(label="Description"), | |
| outputs=list(map(map_question_to_text, questions))) | |
| demo.launch() | |