Delete func.py

func.py

@@ -1,53 +0,0 @@
-import pandas as pd
-import numpy as np
-import torch
-from transformers import BertModel, BertTokenizer
-from sklearn.metrics.pairwise import cosine_similarity
-
-
-tokenizer = BertTokenizer.from_pretrained("DeepPavlov/rubert-base-cased-sentence")
-model = BertModel.from_pretrained("DeepPavlov/rubert-base-cased-sentence")
-device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
-
-
-def filter_by_ganre(df: pd.DataFrame, ganre_list: list):
-    filtered_df = df[df['ganres'].apply(lambda x: any(g in ganre_list for g in(x)))]
-    filt_ind = filtered_df.index.to_list()
-    return filt_ind
-
-# def mean_pooling(model_output, attention_mask):
-#     token_embeddings = model_output['last_hidden_state']
-#     input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
-#     sum_embeddings = torch.sum(token_embeddings * input_mask_expanded, 1)
-#     sum_mask = torch.clamp(input_mask_expanded.sum(1), min=1e-9)
-#     return sum_embeddings / sum_mask
-
-# def recommendation(filt_ind: list, embeddings: np.array, user_text: str, n=10):
-#     token_user_text = tokenizer(user_text, return_tensors='pt', padding='max_length', truncation=True, max_length=512)
-#     user_embeddings = torch.Tensor().to(device)
-#     model.to(device)
-#     model.eval()
-#     with torch.no_grad():
-#         batch = {k: v.to(device) for k, v in token_user_text.items()}
-#         outputs = model(**batch)
-#         user_embeddings = torch.cat([user_embeddings, mean_pooling(outputs, batch['attention_mask'])])
-#         user_embeddings = user_embeddings.cpu().numpy()
-#     cosine_similarities = cosine_similarity(embeddings[filt_ind], user_embeddings.reshape(1, -1))
-#     df_res = pd.DataFrame(cosine_similarities.ravel(), columns=['cos_sim']).sort_values('cos_sim', ascending=False)
-#     dict_topn = df_res.iloc[:n, :].cos_sim.to_dict()
-#     return dict_topn
-
-
-def embed_user(filt_ind: list, embeddings:np.array, user_text: str, n=10):
-    tokens = tokenizer(user_text, return_tensors="pt", padding=True, truncation=True).to(device)
-    model.to(device)
-    model.eval()
-    with torch.no_grad():
-        outputs = model(**tokens)
-        user_embedding = outputs.last_hidden_state.mean(dim=1).cpu().numpy().reshape(1, -1)
-    return user_embedding
-
-    # cosine_similarities = cosine_similarity(embeddings[filt_ind], user_embedding.reshape(1, -1))
-    # df_res = pd.DataFrame(cosine_similarities.ravel(), columns=['cos_sim']).sort_values('cos_sim', ascending=False)
-    # dict_topn = df_res.iloc[:n, :].cos_sim.to_dict()
-    # return dict_topn