| import gradio as gr | |
| from sentence_transformers import SentenceTransformer, util | |
| ts_model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2') | |
| def similarity(*data): | |
| question = data[0] | |
| q = data[1::2] | |
| a = data[2::2] | |
| similarities = [] | |
| for i in q: | |
| embedding_1= ts_model.encode(i, convert_to_tensor=True) | |
| embedding_2 = ts_model.encode(question, convert_to_tensor=True) | |
| similarities.append(float(util.pytorch_cos_sim(embedding_1, embedding_2))) | |
| max_similarity = max(similarities) | |
| max_similarity_index = similarities.index(max_similarity) | |
| if max_similarity <= 0.5: | |
| return "It seems that, I don't have a specific answer for that Question" | |
| else: | |
| return a[max_similarity_index] | |
| gr.Interface( | |
| fn = similarity, | |
| inputs = [gr.Textbox(label = "Main Q"),gr.Textbox(label = "Q1"),gr.Textbox(label = "A1"),gr.Textbox(label = "Q2"),gr.Textbox(label = "A2")], | |
| outputs = "text" | |
| ).launch() |