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Runtime error
| from sentence_transformers import SentenceTransformer | |
| import numpy as np | |
| import faiss | |
| import gradio as gr | |
| def read_text_from_file(file_path): | |
| with open(file_path, "r") as text_file: | |
| text = text_file.read() | |
| return text | |
| text_file_path = "info.txt" | |
| texts = read_text_from_file(text_file_path) | |
| texts = texts.split("&&") | |
| model = SentenceTransformer('sentence-transformers/multi-qa-MiniLM-L6-cos-v1') | |
| doc_emb = model.encode(texts) | |
| d = doc_emb.shape[1] # Dimension of vectors | |
| print(doc_emb.shape) | |
| index = faiss.IndexFlatL2(d) | |
| index.add(doc_emb) | |
| def embed_query(query): | |
| query_emb = model.encode(query) | |
| return query_emb | |
| def question(query): | |
| query_vector = np.asarray(embed_query(query)) | |
| query_vector=np.expand_dims(query_vector,axis=0) | |
| print(query_vector.shape) | |
| k = 3 # Number of nearest neighbors to retrieve | |
| D, I = index.search(query_vector, k) | |
| relevant_paragraph="" | |
| for i in range(k): | |
| relevant_paragraph_index = I[0][i] | |
| relevant_paragraph += texts[relevant_paragraph_index] + "\n" | |
| return relevant_paragraph | |
| demo = gr.Interface(fn=question, inputs="text", outputs="text") | |
| if __name__ == "__main__": | |
| demo.launch() |