Initial app code, based on Endre/SemanticSearch-HU

app.py

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+import streamlit as st
+from transformers import AutoTokenizer, AutoModel
+import transformers
+import torch
+from sentence_transformers import util
+
+# explicit no operation hash functions defined, because raw sentences, embedding, model and tokenizer are not going to change
+
+
+@st.cache(hash_funcs={list: lambda _: None})
+def load_raw_sentences(filename):
+    with open(filename) as f:
+        return f.readlines()
+
+@st.cache(hash_funcs={torch.Tensor: lambda _: None})
+def load_embeddings(filename):
+    with open(filename) as f:
+        return torch.load(filename,map_location=torch.device('cpu') )
+
+
+#Mean Pooling - Take attention mask into account for correct averaging
+def mean_pooling(model_output, attention_mask):
+    token_embeddings = model_output[0] #First element of model_output contains all token embeddings
+    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
+    sum_embeddings = torch.sum(token_embeddings * input_mask_expanded, 1)
+    sum_mask = torch.clamp(input_mask_expanded.sum(1), min=1e-9)
+    return sum_embeddings / sum_mask
+
+def findTopKMostSimilar(query_embedding, embeddings, all_sentences, k):
+    cosine_scores = util.pytorch_cos_sim(query_embedding, embeddings)
+    cosine_scores_list = cosine_scores.squeeze().tolist()
+    pairs = []
+    for idx,score in enumerate(cosine_scores_list):
+        if idx < len(all_sentences):
+            pairs.append({'score': '{:.4f}'.format(score), 'text': all_sentences[idx]})
+    pairs = sorted(pairs, key=lambda x: x['score'], reverse=True)
+    return pairs[0:k]
+
+def calculateEmbeddings(sentences,tokenizer,model):
+    tokenized_sentences = tokenizer(sentences, padding=True, truncation=True, max_length=128, return_tensors='pt')
+    with torch.no_grad():
+        model_output = model(**tokenized_sentences)
+    sentence_embeddings = mean_pooling(model_output, tokenized_sentences['attention_mask'])
+    return sentence_embeddings
+
+# explicit no operation hash function, because model and tokenizer are not going to change
+@st.cache(hash_funcs={transformers.models.bert.tokenization_bert_fast.BertTokenizerFast: lambda _: None, transformers.models.bert.modeling_bert.BertModel: lambda _: None})
+def load_model_and_tokenizer():
+    multilingual_checkpoint = 'sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2'
+    tokenizer = AutoTokenizer.from_pretrained(multilingual_checkpoint)
+    model = AutoModel.from_pretrained(multilingual_checkpoint)
+    print(type(tokenizer))
+    print(type(model))
+    return model, tokenizer
+
+
+model,tokenizer = load_model_and_tokenizer();
+raw_text_file = 'data/data/joint_text_filtered.md'
+all_sentences = load_raw_sentences(raw_text_file)
+
+embeddings_file = 'data/multibert_embedded.pt'
+all_embeddings = load_embeddings(embeddings_file)
+
+
+st.header('RF szöveg kereső')
+
+st.caption('[HU] Adjon meg egy tetszőleges kifejezést és a rendszer visszaadja az 5 hozzá legjobban hasonlító szöveget')
+
+
+
+text_area_input_query = st.text_area('[HU] Beviteli mező - [EN] Query input',value='Mikor van a leadási hataridő?')
+
+if text_area_input_query:
+    query_embedding = calculateEmbeddings([text_area_input_query],tokenizer,model)
+    top_pairs = findTopKMostSimilar(query_embedding, all_embeddings, all_sentences, 5)
+    st.json(top_pairs)
+
+
+