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| # -*- coding: utf-8 -*- | |
| """semantic_song_search.ipynb | |
| Automatically generated by Colaboratory. | |
| Original file is located at | |
| https://colab.research.google.com/drive/17IwipreOw_cvu1TsA4WUrfzxTBBHMiVh | |
| """ | |
| from sentence_transformers import SentenceTransformer, util | |
| import gradio as gr | |
| import pandas as pd | |
| import torch | |
| import numpy as np | |
| from google.colab import drive | |
| drive.mount('/content/gdrive') | |
| # Commented out IPython magic to ensure Python compatibility. | |
| # %cd gdrive/MyDrive/song_sentiment | |
| """### model all mini -- small dataset """ | |
| model_all_mini = SentenceTransformer('all-MiniLM-L12-v2') | |
| sds = pd.read_csv("data/small_dataset.csv") | |
| embeddings_sds = model_all_mini.encode(sds['lyrics']) | |
| sds['embeddings'] = list(embeddings_sds) | |
| def relevance_scores(query_embed): | |
| scores = [cosine_similarity(query_embed, v2) for v2 in sds['embeddings']] | |
| scores = pd.Series(scores) | |
| return(scores) | |
| def semantic_search(query_sentence, df = sds, return_top = False): | |
| query_embed = model_all_mini.encode(query_sentence) | |
| scores = relevance_scores(query_embed) | |
| df['scores'] = scores | |
| sorted_df = df.sort_values(by = 'scores', ascending = False) | |
| if return_top == False: | |
| sorted_df['scores'] = round(sorted_df['scores'],3) | |
| return sorted_df[['title','artist','scores']].head(3) | |
| else: | |
| return sorted_df.iloc[0]['lyrics'][:200] | |
| def cosine_similarity(v1, v2): | |
| d = np.dot(v1, v2) | |
| cos_theta = d / (np.linalg.norm(v1) * np.linalg.norm(v2)) | |
| return(cos_theta) | |
| semantic_search("i'm pleased you are doing well after we left each other") | |
| print(semantic_search("i'm pleased you are doing well after we left each other", return_top = True)) | |
| """### model msmarco-distilbert-base-dot-prod-v3 with hf dataset (with song name)""" | |
| query = ["i'm pleased you are doing well after we left each other"] | |
| hf_data = pd.read_csv('data/hf_train_with_SName.csv') | |
| hf_data['Lyric'] = hf_data['Lyric'].str.replace('\\n', "") | |
| hf_data.head() | |
| model_msmarco_v3 = SentenceTransformer('msmarco-distilbert-base-dot-prod-v3') | |
| query_embedding = model_msmarco_v3.encode(query) | |
| passage_embedding = model_msmarco_v3.encode(hf_data[:1000].Lyric) | |
| corpus_embeddings = torch.from_numpy(passage_embedding).float().to('cuda') | |
| corpus_embeddings = util.normalize_embeddings(corpus_embeddings) | |
| # query_embeddings = torch.from_numpy(query_embedding).float().to('cuda') | |
| # query_embeddings = util.normalize_embeddings(query_embeddings) | |
| # hits = util.semantic_search(query_embeddings, corpus_embeddings, score_function=util.dot_score) | |
| # best_match = hits[0][0]['corpus_id'] | |
| hf_data.iloc[best_match, :] | |
| hf_data.iloc[best_match]['Lyric'] | |
| hf_data.head() | |
| def msmarco_match(query, return_top = True): | |
| query_embedding = model_msmarco_v3.encode(query) | |
| query_embeddings = torch.from_numpy(query_embedding).float().to('cuda') | |
| query_embeddings = util.normalize_embeddings(query_embeddings) | |
| hits = util.semantic_search(query_embeddings, corpus_embeddings, score_function=util.dot_score) | |
| top_hits = hits[0][0:3] | |
| ids = [item['corpus_id'] for item in top_hits] | |
| scores = pd.Series([round(item['score'],3) for item in top_hits]) | |
| if return_top == True: | |
| return hf_data.iloc[ids[0]]['Lyric'][:200] | |
| else: | |
| songs = hf_data.iloc[ids].reset_index() | |
| songs = pd.concat([songs, scores], axis = 1) | |
| songs.rename(columns={0: 'Score'}, | |
| inplace=True) | |
| return songs.drop(columns = 'index') | |
| msmarco_match(query, return_top= False) | |
| msmarco_match(query) | |
| msmarco_match(query, return_top = False) | |
| """## Fine-tuned all-mini -- small dataset""" | |
| model_fine_tuned = SentenceTransformer('models/finetune_mnr_final') | |
| embeddings_sds_ft = model_fine_tuned.encode(sds['lyrics']) | |
| sds['embeddings_ft'] = list(embeddings_sds_ft) | |
| def relevance_scores_ft(query_embed): | |
| scores = [cosine_similarity(query_embed, v2) for v2 in sds['embeddings_ft']] | |
| scores = pd.Series(scores) | |
| return(scores) | |
| def semantic_search_ft(query_sentence, df = sds, return_top = False): | |
| query_embed = model_fine_tuned.encode(query_sentence) | |
| scores = relevance_scores(query_embed) | |
| df['scores'] = scores | |
| sorted_df = df.sort_values(by = 'scores', ascending = False) | |
| if return_top == False: | |
| sorted_df['scores'] = round(sorted_df['scores'],3) | |
| return sorted_df[['title','artist','scores']].head(3) | |
| else: | |
| return sorted_df.iloc[0]['lyrics'][:200] | |
| """## Gradio App """ | |
| def get_recom(choice, query): | |
| if choice == "all-MiniLM": | |
| return semantic_search(query), semantic_search(query, return_top = True) | |
| elif choice == "all-MiniLM-fine-tuned": | |
| return semantic_search_ft(query), semantic_search_ft(query, return_top = True) | |
| else: | |
| list_query = [] | |
| query2 = query | |
| list_query.append([query, query2]) | |
| return msmarco_match(list_query, return_top = False) , msmarco_match(list_query) | |
| iface = gr.Interface( | |
| title = 'Enjoy our recommendations', | |
| description = 'Do you think we can guess what you like?', | |
| fn=get_recom, | |
| inputs= [ gr.Radio(choices = ["all-MiniLM", "all-MiniLM-fine-tuned", "msmarco"], label="Choose ur model"), | |
| gr.Textbox(lines=4, placeholder="Enter ur query...", label = 'Query', show_label = True)], | |
| outputs = [gr.Dataframe(label = "Top songs", show_label = True), | |
| gr.Text(label = 'A glimpse of the closest match', show_label = True)] | |
| ,live = False, | |
| interpretation="default", | |
| ) | |
| iface.launch() | |