Upload app.py

app.py

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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
+from datasets import load_dataset
+import gradio as gr
+import pandas as pd
+import torch
+import numpy as np
+
+
+"""### model all mini -- small dataset """
+
+model_all_mini = SentenceTransformer('all-MiniLM-L12-v2')
+
+sds = load_dataset("Santarabantoosoo/small_lyrics_dataset")
+
+sds = pd.DataFrame(sds['train'])
+# sds = pd.read_csv("data/small_dataset.csv")
+
+sds.head()
+
+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 = load_dataset("Santarabantoosoo/hf_song_lyrics_with_names")
+
+hf_data = pd.DataFrame(hf_data['train'])
+
+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('Santarabantoosoo/songs_fine-tuned-all-MiniLM-L12-v2')
+
+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(share = False, debug = True)
+
+
+
+from sentence_transformers import SentenceTransformer, util
+model = SentenceTransformer('multi-qa-MiniLM-L6-cos-v1')
+
+query_embedding = model.encode('I am so happy')
+passage_embedding = model.encode(sds['embeddings'])
+
+print("Similarity:", util.dot_score(query_embedding, passage_embedding))
+