Update func.py

func.py

@@ -15,24 +15,36 @@ def filter_by_ganre(df: pd.DataFrame, ganre_list: list):
     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 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)
+# 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 recommendation(filt_ind: list, embeddings:np.array, user_text: str, n=10):
+    tokens = tokenizer(user_text, return_tensors="pt", padding=True, truncation=True)
     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))
+        tokens = {key: value.to(model.device) for key, value in tokens.items()}
+        outputs = model(**tokens)
+        user_embedding = outputs.last_hidden_state.mean(dim=1).squeeze().cpu().detach().numpy()
+    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
+    return dict_topn