Delete app.py

app.py

@@ -1,166 +0,0 @@
-# -*- 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()
-