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| import gradio as gr | |
| import pandas as pd | |
| import numpy as np | |
| import torch.nn.functional as F | |
| from torch import Tensor | |
| from transformers import AutoTokenizer, AutoModel | |
| from sklearn.metrics.pairwise import cosine_similarity | |
| def average_pool(last_hidden_states: Tensor, | |
| attention_mask: Tensor) -> Tensor: | |
| last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0) | |
| return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None] | |
| df = pd.read_csv('wiki.csv') | |
| data_embeddings = np.load("wiki-embeddings.npy") | |
| print("loading the model...") | |
| tokenizer = AutoTokenizer.from_pretrained('intfloat/multilingual-e5-large') | |
| model = AutoModel.from_pretrained('intfloat/multilingual-e5-large') | |
| with gr.Blocks() as demo: | |
| chatbot = gr.Chatbot() | |
| msg = gr.Textbox(label="simple wikipedia semantic search query", placeholder="for example, \"medieval battles\"") | |
| clear = gr.ClearButton([msg, chatbot]) | |
| def _search(message, chat_history): | |
| batch_dict = tokenizer(["query: " + message], max_length=512, padding=True, truncation=True, return_tensors='pt') | |
| outputs = model(**batch_dict) | |
| input_embedding = average_pool(outputs.last_hidden_state, batch_dict['attention_mask']) | |
| # normalize embeddings | |
| input_embedding = F.normalize(input_embedding, p=2, dim=1) | |
| input_embedding = input_embedding[0].tolist() | |
| # Compute cosine similarities | |
| input_embedding = np.array(input_embedding).reshape(1, -1) | |
| cos_similarities = cosine_similarity(data_embeddings, input_embedding).flatten() | |
| # Get top k similar points' indices | |
| k = 5 # replace with your value of k | |
| top_k_idx = cos_similarities.argsort()[-k:][::-1] | |
| # Get corresponding 'text' for top k similar points | |
| top_k_text = df['title'].iloc[top_k_idx].tolist() | |
| bot_message = "\n".join(f"{i+1}. {top_k_text[i]} // {top_k_idx[i]}" for i in range(len(top_k_text))) | |
| chat_history.append((message, "results:\n" + bot_message)) | |
| return "", chat_history | |
| def _retrieve(message, chat_history): | |
| idx = int(message) | |
| for _, m in chat_history[::-1]: | |
| if m.startswith("results:\n"): | |
| for n in m.split("\n")[1:]: | |
| if str(idx) == n.split(".")[0]: | |
| df_idx = int(n.split(" // ")[-1]) | |
| chat_history.append((message, f"contents of {n}:\n{df[df_idx]}")) | |
| return "", chat_history | |
| def respond(message, chat_history): | |
| try: | |
| int(message) | |
| return _retrieve(message, chat_history) | |
| except: | |
| return _search(message, chat_history) | |
| msg.submit(respond, [msg, chatbot], [msg, chatbot]) | |
| demo.launch() |