nickmuchi's picture
Update app.py
efd2261
raw
history blame
5.76 kB
from sentence_transformers import SentenceTransformer, util, CrossEncoder
from datasets import load_dataset
import pandas as pd
import torch
import gradio as gr
import whisper
#Get the netflix dataset
netflix = load_dataset('hugginglearners/netflix-shows',use_auth_token=True)
#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=True)
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
#load ASR model
def asr(audio):
asr_model = whisper.load_model("small")
results = asr_model.transcribe(audio)
query = results['text']
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 = """<h1 id="title">Voice Activated Netflix Shows Semantic Search</h1>"""
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 search for "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 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_queries = ["Success, rags to riches","murder, crime scene investigation thriller"]
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:
gr.Markdown(title)
gr.Markdown(description)
gr.Markdown(twitter_link)
top_k = gr.Slider(minimum=3,maximum=10,value=5,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..')
with gr.Row():
query = gr.Textbox(label='Transcribed Text')
audio.change(asr,audio,query)
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_queries,inputs=[query])
sem_but = gr.Button('Search')
sem_but.click(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)