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| import gradio as gr | |
| import pandas as pd | |
| import faiss | |
| from sentence_transformers import SentenceTransformer | |
| #load the sentences data | |
| path = "debate_hansard_sentences.txt" | |
| file = open(path, "r") | |
| sentences = file.readlines() | |
| # remove duplicates | |
| sentences = [word for word in list(set(sentences)) if type(word)] | |
| sentences_dict = {idx:val for idx, val in enumerate(sentences)} | |
| sentences_df = pd.DataFrame.from_dict(sentences_dict, orient='index', columns=['sentences']) | |
| #load the index | |
| index = faiss.read_index("semantic_search_vector.index") | |
| def question_answer(query): | |
| model = SentenceTransformer('bert-base-nli-mean-tokens') | |
| k = 4 | |
| xq = model.encode([query]) | |
| D, I = index.search(xq, k) # search | |
| sentences_df["sentences"].iloc[I.tolist()[0]] | |
| a = list(sentences_df["sentences"].iloc[I.tolist()[0]]) | |
| results = "\n".join(a) | |
| answer = results | |
| return answer | |
| iface = gr.Interface( | |
| fn=question_answer, | |
| inputs=gr.inputs.Textbox(lines=2, placeholder="Type your query here"), | |
| outputs="text").launch() | |