Duplicate from HarryLee/QueryExpansionForEtsy

Co-authored-by: harryhe <[email protected]>

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README.md

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+---
+title: QueryExpansion
+emoji: 👁
+colorFrom: pink
+colorTo: indigo
+sdk: streamlit
+sdk_version: 1.17.0
+app_file: app.py
+pinned: false
+duplicated_from: HarryLee/QueryExpansionForEtsy
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import streamlit as st
+from streamlit_tags import st_tags, st_tags_sidebar
+from keytotext import pipeline
+from PIL import Image
+
+import json
+from sentence_transformers import SentenceTransformer, CrossEncoder, util
+import gzip
+import os
+import torch
+import pickle
+import random
+import numpy as np
+
+############
+## Main page
+############
+
+st.write("# Demonstration for Etsy Query Expansion（Etsy-QE）")
+
+st.markdown("***Idea is to build a model which will take query as inputs and generate expansion information as outputs.***")
+image = Image.open('etsy-shop-LLC.png')
+st.image(image)
+
+st.sidebar.write("# Top-N Selection")
+maxtags_sidebar = st.sidebar.slider('Number of query allowed?', 1, 20, 1, key='ehikwegrjifbwreuk')
+#user_query = st_tags(
+#    label='# Enter Query:',
+#    text='Press enter to add more',
+#    value=['Mother'],
+#    suggestions=['gift', 'nike', 'wool'],
+#    maxtags=maxtags_sidebar,
+#    key="aljnf")
+
+user_query = st.text_input("Enter a query for the generated text: e.g., gift, home decoration ...")
+
+# Add selectbox in streamlit
+option1 = st.sidebar.selectbox(
+     'Which transformers model would you like to be selected?',
+     ('multi-qa-MiniLM-L6-cos-v1','null','null'))
+
+option2 = st.sidebar.selectbox(
+     'Which corss-encoder model would you like to be selected?',
+     ('cross-encoder/ms-marco-MiniLM-L-6-v2','null','null'))
+
+st.sidebar.success("Load Successfully!")
+
+#if not torch.cuda.is_available():
+#    print("Warning: No GPU found. Please add GPU to your notebook")
+
+#We use the Bi-Encoder to encode all passages, so that we can use it with sematic search
+bi_encoder = SentenceTransformer(option1,device='cpu')
+bi_encoder.max_seq_length = 256    #Truncate long passages to 256 tokens
+top_k = 32                          #Number of passages we want to retrieve with the bi-encoder
+
+#The bi-encoder will retrieve 100 documents. We use a cross-encoder, to re-rank the results list to improve the quality
+cross_encoder = CrossEncoder(option2, device='cpu')
+
+passages = []
+
+# load pre-train embeedings files
+embedding_cache_path = 'etsy-embeddings-cpu.pkl'
+print("Load pre-computed embeddings from disc")
+with open(embedding_cache_path, "rb") as fIn:
+  cache_data = pickle.load(fIn)
+  passages = cache_data['sentences']
+  corpus_embeddings = cache_data['embeddings']
+
+from rank_bm25 import BM25Okapi
+from sklearn.feature_extraction import _stop_words
+import string
+from tqdm.autonotebook import tqdm
+import numpy as np
+import re
+
+import yake
+
+language = "en"
+max_ngram_size = 3
+deduplication_threshold = 0.9
+deduplication_algo = 'seqm'
+windowSize = 3
+numOfKeywords = 3
+
+custom_kw_extractor = yake.KeywordExtractor(lan=language, n=max_ngram_size, dedupLim=deduplication_threshold, dedupFunc=deduplication_algo, windowsSize=windowSize, top=numOfKeywords, features=None)
+
+# We lower case our text and remove stop-words from indexing
+def bm25_tokenizer(text):
+    tokenized_doc = []
+    for token in text.lower().split():
+        token = token.strip(string.punctuation)
+
+        if len(token) > 0 and token not in _stop_words.ENGLISH_STOP_WORDS:
+            tokenized_doc.append(token)
+    return tokenized_doc
+
+tokenized_corpus = []
+for passage in tqdm(passages):
+    tokenized_corpus.append(bm25_tokenizer(passage))
+
+bm25 = BM25Okapi(tokenized_corpus)
+
+def word_len(s):
+    return len([i for i in s.split(' ') if i])
+
+
+# This function will search all wikipedia articles for passages that
+# answer the query
+def search(query):
+    print("Input query:", query)
+    total_qe = []
+
+    ##### BM25 search (lexical search) #####
+    bm25_scores = bm25.get_scores(bm25_tokenizer(query))
+    top_n = np.argpartition(bm25_scores, -5)[-5:]
+    bm25_hits = [{'corpus_id': idx, 'score': bm25_scores[idx]} for idx in top_n]
+    bm25_hits = sorted(bm25_hits, key=lambda x: x['score'], reverse=True)
+    
+    #print("Top-10 lexical search (BM25) hits")
+    qe_string = []
+    for hit in bm25_hits[0:1000]:
+      if passages[hit['corpus_id']].replace("\n", " ") not in qe_string:
+        qe_string.append(passages[hit['corpus_id']].replace("\n", ""))
+
+    sub_string = []
+    for item in qe_string:
+      for sub_item in item.split(","):
+        sub_string.append(sub_item)
+    #print(sub_string)
+    total_qe.append(sub_string)
+
+    ##### Sematic Search #####
+    # Encode the query using the bi-encoder and find potentially relevant passages
+    query_embedding = bi_encoder.encode(query, convert_to_tensor=True)
+    hits = util.semantic_search(query_embedding, corpus_embeddings, top_k=top_k)
+    hits = hits[0]  # Get the hits for the first query
+
+    ##### Re-Ranking #####
+    # Now, score all retrieved passages with the cross_encoder
+    cross_inp = [[query, passages[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]
+
+    # Output of top-10 hits from bi-encoder
+    #print("\n-------------------------\n")
+    #print("Top-N Bi-Encoder Retrieval hits")
+    hits = sorted(hits, key=lambda x: x['score'], reverse=True)
+    qe_string = []
+    for hit in hits[0:1000]:
+      if passages[hit['corpus_id']].replace("\n", " ") not in qe_string:
+        qe_string.append(passages[hit['corpus_id']].replace("\n", ""))
+    #print(qe_string)
+    total_qe.append(qe_string)
+
+    # Output of top-10 hits from re-ranker
+    #print("\n-------------------------\n")
+    #print("Top-N Cross-Encoder Re-ranker hits")
+    hits = sorted(hits, key=lambda x: x['cross-score'], reverse=True)
+    qe_string = []
+    for hit in hits[0:1000]:
+      if passages[hit['corpus_id']].replace("\n", " ") not in qe_string:
+        qe_string.append(passages[hit['corpus_id']].replace("\n", ""))
+    #print(qe_string)
+    total_qe.append(qe_string)
+
+    # Total Results
+    total_qe.append(qe_string)
+    st.write("E-Commerce Query Expansion Results: \n")
+
+    res = []
+    for sub_list in total_qe:
+      for i in sub_list:
+        rs = re.sub("([^\u0030-\u0039\u0041-\u007a])", ' ', i)
+        rs_final = re.sub("\x20\x20", "\n", rs)
+        #st.write(rs_final.strip())
+        res.append(rs_final.strip())
+
+    res_clean = []
+    for out in res:
+      if len(out) > 20:
+        keywords = custom_kw_extractor.extract_keywords(out)
+        for key in keywords:
+          res_clean.append(key[0])
+      else:
+        res_clean.append(out)
+
+    show_out = []
+    for i in res_clean:
+      num = word_len(i)
+      if num > 1:
+        show_out.append(i)
+    unique_list = list(set(show_out))
+    new_unique_list = [item for item in unique_list if item != query]
+    Lowercasing_list = [item.lower() for item in new_unique_list]
+    st.write(Lowercasing_list[0:maxtags_sidebar])
+
+    return Lowercasing_list
+
+def search_nolog(query):
+    total_qe = []
+    ##### BM25 search (lexical search) #####
+    bm25_scores = bm25.get_scores(bm25_tokenizer(query))
+    top_n = np.argpartition(bm25_scores, -5)[-5:]
+    bm25_hits = [{'corpus_id': idx, 'score': bm25_scores[idx]} for idx in top_n]
+    bm25_hits = sorted(bm25_hits, key=lambda x: x['score'], reverse=True)
+    
+    qe_string = []
+    for hit in bm25_hits[0:1000]:
+      if passages[hit['corpus_id']].replace("\n", " ") not in qe_string:
+        qe_string.append(passages[hit['corpus_id']].replace("\n", ""))
+
+    sub_string = []
+    for item in qe_string:
+      for sub_item in item.split(","):
+        sub_string.append(sub_item)
+    total_qe.append(sub_string)
+
+    ##### Sematic Search #####
+    # Encode the query using the bi-encoder and find potentially relevant passages
+    query_embedding = bi_encoder.encode(query, convert_to_tensor=True)
+    hits = util.semantic_search(query_embedding, corpus_embeddings, top_k=top_k)
+    hits = hits[0]  # Get the hits for the first query
+
+    ##### Re-Ranking #####
+    # Now, score all retrieved passages with the cross_encoder
+    cross_inp = [[query, passages[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]
+
+    # Output of top-10 hits from bi-encoder
+    hits = sorted(hits, key=lambda x: x['score'], reverse=True)
+    qe_string = []
+    for hit in hits[0:1000]:
+      if passages[hit['corpus_id']].replace("\n", " ") not in qe_string:
+        qe_string.append(passages[hit['corpus_id']].replace("\n", ""))
+    total_qe.append(qe_string)
+
+    # Output of top-10 hits from re-ranker
+    hits = sorted(hits, key=lambda x: x['cross-score'], reverse=True)
+    qe_string = []
+    for hit in hits[0:1000]:
+      if passages[hit['corpus_id']].replace("\n", " ") not in qe_string:
+        qe_string.append(passages[hit['corpus_id']].replace("\n", ""))
+    total_qe.append(qe_string)
+
+    # Total Results
+    total_qe.append(qe_string)
+
+    res = []
+    for sub_list in total_qe:
+      for i in sub_list:
+        rs = re.sub("([^\u0030-\u0039\u0041-\u007a])", ' ', i)
+        rs_final = re.sub("\x20\x20", "\n", rs)
+        res.append(rs_final.strip())
+
+    res_clean = []
+    for out in res:
+      if len(out) > 20:
+        keywords = custom_kw_extractor.extract_keywords(out)
+        for key in keywords:
+          res_clean.append(key[0])
+      else:
+        res_clean.append(out)
+
+    show_out = []
+    for i in res_clean:
+      num = word_len(i)
+      if num > 1:
+        show_out.append(i)
+
+    return show_out
+
+def reranking():
+    rerank_list = []
+    reres = []
+    rerank_list = search_nolog(query = user_query)
+    unique_list = list(set(rerank_list))
+    new_unique_list = [item for item in unique_list if item != user_query]
+    Lowercasing_list = [item.lower() for item in new_unique_list]
+
+    st.write("E-Commerce Query Expansion Results: \n")
+    st.write(Lowercasing_list[0:maxtags_sidebar])
+
+    for i in Lowercasing_list[0:maxtags_sidebar]:
+        reres.append(i)
+    np.random.seed(7)
+    np.random.shuffle(reres)
+    st.write("Reranking Results: \n")
+    st.write(reres)
+
+st.write("## Results:")
+if st.button('Generated Expansion'):
+    out_res = search(query = user_query)
+    #st.success(out_res)
+
+if st.button('Rerank'):
+    out_res = reranking()
+    #st.success(out_res)

etsy-embeddings-cpu.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0a8eb36f4ec40a7d1cb382376afc38cac7caed6104bbaf5a8b28f8a98ba18cb5
+size 456491627

requirements.txt

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+streamlit==0.82.0
+streamlit_tags
+pyarrow
+keytotext
+opencv-python-headless
+sentence-transformers
+rank_bm25
+yake
+altair==4.0