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| import numpy as np # linear algebra | |
| import os | |
| from sklearn.metrics.pairwise import cosine_similarity | |
| from sentence_transformers import SentenceTransformer | |
| import pickle | |
| import gradio as gr | |
| with open('embeddings.pkl', "rb") as fIn: | |
| stored_data = pickle.load(fIn) | |
| embeddings = stored_data['embeddings'] | |
| with open('answers.pkl', "rb") as fIn: | |
| stored_data = pickle.load(fIn) | |
| answers = stored_data['answers'] | |
| model = SentenceTransformer('msmarco-MiniLM-L-6-v3') | |
| def predict(ques): | |
| embeded_ques = model.encode(ques) | |
| em_vec = np.vstack([embeded_ques]*10) | |
| scores = cosine_similarity(embeddings, em_vec) | |
| idx = np.argmax([np.mean(arr) for arr in scores]) | |
| return answers[idx] | |
| examples = ['What is the date of his death?', 'Did Einstein have siblings?', 'Who was his wife?', 'What was Einstein\'s father\'s name?', 'At what institutions did he study?'] | |
| inputs = gr.Textbox(label='query') | |
| outputs = gr.Textbox(label='Answers') | |
| title = "Similar faq" | |
| description = "Retreive answers of similar queries using sentence transformers" | |
| gr.Interface(fn = predict, inputs = inputs, examples=examples, outputs = outputs, title = title, description = description).launch() | |