from sentence_transformers import SentenceTransformer, util, CrossEncoder from datasets import load_dataset import pandas as pd import torch import gradio as gr import whisper import pathlib, os auth_token = os.environ.get("auth_key") #Get the netflix dataset netflix = load_dataset('hugginglearners/netflix-shows',use_auth_token=auth_token) #load ASR model asr_model = whisper.load_model("small") #Filter for relevant columns and convert to pandas netflix_df = netflix['train'].to_pandas() netflix_df = netflix_df[['type','title','country','description','release_year','rating','duration','listed_in','cast']] passages = netflix_df['description'].tolist() #load mpnet model model = SentenceTransformer('all-mpnet-base-v2') #load embeddings flix_ds = load_dataset("nickmuchi/netflix-shows-mpnet-embeddings", use_auth_token=auth_token) dataset_embeddings = torch.from_numpy(flix_ds["train"].to_pandas().to_numpy()).to(torch.float) #load cross-encoder for reranking cross_encoder = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-12-v2') def display_df_as_table(model,top_k,score='score'): # Display the df with text and scores as a table df = pd.DataFrame([(hit[score],passages[hit['corpus_id']]) for hit in model[0:top_k]],columns=['Score','Text']) df['Score'] = round(df['Score'].astype(float),2) df = df.merge(netflix_df,how='inner',left_on='Text',right_on='description') df.drop('Text',inplace=True,axis=1) return df #function for transcribing audio inputs def asr(audio): results = asr_model.transcribe(audio) query = results['text'] return query #load ASR model def asr_inputs(audio, upload): if audio: query = asr(audio) elif upload: query = asr(upload) return query #function for generating similarity of query and netflix shows def semantic_search(query,top_k): '''Encode query and check similarity with embeddings''' question_embedding = model.encode(query, convert_to_tensor=True).cpu() hits = util.semantic_search(question_embedding, dataset_embeddings, top_k=top_k) hits = hits[0] ##### Re-Ranking ##### # Now, score all retrieved passages with the cross_encoder cross_inp = [[query, netflix_df['description'].iloc[hit['corpus_id']]] for hit in hits] cross_scores = cross_encoder.predict(cross_inp) # Sort results by the cross-encoder scores for idx in range(len(cross_scores)): hits[idx]['cross-score'] = cross_scores[idx] #Bi-encoder df hits = sorted(hits, key=lambda x: x['score'], reverse=True) bi_df = display_df_as_table(hits,top_k) #Cross encoder df hits = sorted(hits, key=lambda x: x['cross-score'], reverse=True) cross_df = display_df_as_table(hits,top_k,'cross-score') cross_df['Score'] = round(cross_df['Score'].astype(float),2) return bi_df, cross_df title = """

Voice Activated Netflix Semantic Search

""" description = """ Semantic Search is a way to generate search results based on the actual meaning of the query instead of a standard keyword search. I believe this way of searching provides more meaning results when trying to find a good show to watch on Netflix. For example, one could say "Success, rags to riches story" as provided in the example below to generate shows or movies with a description that is semantically similar to the query. The app uses OpenAI's SOTA ASR model, [Whisper](https://huggingface.co./spaces/openai/whisper), to convert speech to text. - The App generates embeddings using [All-Mpnet-Base-v2](https://huggingface.co./sentence-transformers/all-mpnet-base-v2) model from Sentence Transformers. - The model encodes the query and the discerption field from the [Netflix-Shows](https://huggingface.co./datasets/hugginglearners/netflix-shows) dataset which contains 8800 shows and movies currently on Netflix scraped from the web using Selenium. - Similarity scores are then generated, from highest to lowest. The user can select how many suggestions they need from the results. - A Cross Encoder then re-ranks the top selections to further improve on the similarity scores. - You will see 2 tables generated, one from the bi-encoder and the other from the cross encoder which further enhances the similarity score rankings Enjoy and Search like you mean it!! """ example_audio = [[path.as_posix()] for path in sorted(pathlib.Path('audio_examples').rglob('*.wav'))] twitter_link = """ [![](https://img.shields.io/twitter/follow/nickmuchi?label=@nickmuchi&style=social)](https://twitter.com/nickmuchi) """ css = ''' h1#title { text-align: center; } ''' demo = gr.Blocks(css=css) with demo: with gr.Box(): gr.Markdown(title) gr.Markdown(description) gr.Markdown(twitter_link) top_k = gr.Slider(minimum=3,maximum=10,value=3,step=1,label='Number of Suggestions to Generate') with gr.Row(): audio = gr.Audio(source='microphone',type='filepath',label='Audio Input: Describe the Netflix show you would like to watch..') audio_file = gr.Audio(source='upload',type='filepath',label='Audio Upload') btn = gr.Button("Transcribe") with gr.Row(): query = gr.Textbox(label='Transcribed Text') with gr.Row(): bi_output = gr.DataFrame(headers=['Similarity Score','Type','Title','Country','Description','Release Year','Rating','Duration','Category Listing','Cast'], label=f'Top-{top_k} Bi-Encoder Retrieval hits', wrap=True) with gr.Row(): cross_output = gr.DataFrame(headers=['Similarity Score','Type','Title','Country','Description','Release Year','Rating','Duration','Category Listing','Cast'], label=f'Top-{top_k} Cross-Encoder Re-ranker hits', wrap=True) with gr.Row(): examples = gr.Examples(examples=example_audio,inputs=[audio_file]) #sem_but = gr.Button('Search') btn.click(asr_inputs, inputs=[audio,audio_file], outputs=[query]) query.change(semantic_search,inputs=[query,top_k],outputs=[bi_output,cross_output],queue=True) gr.Markdown("![visitor badge](https://visitor-badge.glitch.me/badge?page_id=nickmuchi-netflix-shows-semantic-search)") demo.launch(debug=True,enable_queue=True)