update

app.py

@@ -0,0 +1,107 @@
+import faiss
+import numpy as np
+import pandas as pd
+import streamlit as st
+import torch
+from transformers import AutoModel, AutoTokenizer
+
+import os 
+
+os.environ['KMP_DUPLICATE_LIB_OK']='True'
+
+
+@st.cache(allow_output_mutation=True)
+def load_model_and_tokenizer():
+    tokenizer = AutoTokenizer.from_pretrained("kaisugi/scitoricsbert")
+    model = AutoModel.from_pretrained("kaisugi/scitoricsbert")
+    model.eval()
+    
+    return model, tokenizer
+
+
+@st.cache(allow_output_mutation=True)
+def load_sentence_data():
+    sentence_df = pd.read_csv("sentence_data_789k.csv.gz")
+    
+    return sentence_df
+
+
+@st.cache(allow_output_mutation=True)
+def load_sentence_embeddings():
+    npz_comp = np.load("sentence_embeddings_789k.npz")
+    sentence_embeddings = npz_comp["arr_0"]
+
+    return sentence_embeddings
+
+
+@st.cache(allow_output_mutation=True)
+def build_faiss_index(sentence_emeddings):
+    D = 768
+    N = 789188
+    Xt = sentence_emeddings[:39000]
+    X = sentence_emeddings
+
+    # Param of PQ
+    M = 16  # The number of sub-vector. Typically this is 8, 16, 32, etc.
+    nbits = 8 # bits per sub-vector. This is typically 8, so that each sub-vec is encoded by 1 byte
+    # Param of IVF
+    nlist = 1000  # The number of cells (space partition). Typical value is sqrt(N)
+    # Param of HNSW
+    hnsw_m = 32  # The number of neighbors for HNSW. This is typically 32
+
+    # Setup
+    quantizer = faiss.IndexHNSWFlat(D, hnsw_m)
+    index = faiss.IndexIVFPQ(quantizer, D, nlist, M, nbits)
+
+    # Train
+    index.train(Xt)
+
+    # Add
+    index.add(X)
+
+    # Search
+    index.nprobe = 8  # Runtime param. The number of cells that are visited for search.
+
+    return index
+
+
+@st.cache
+def get_retrieval_results(index, input_text, top_k, model, tokenizer, sentence_df):
+    with torch.no_grad():
+        inputs = tokenizer.encode_plus(
+            input_text,
+            padding=True,
+            truncation=True,
+            max_length=512,
+            return_tensors='pt'
+        )
+        outputs = model(**inputs)
+        query_embeddings = outputs.last_hidden_state[:, 0, :][0]
+        query_embeddings = query_embeddings.detach().cpu().numpy()
+        query_embeddings = query_embeddings / np.linalg.norm(query_embeddings, ord=2)
+
+    print(np.array([query_embeddings]))
+
+    dists, ids = index.search(x=np.array([query_embeddings]), k=top_k)
+    print(dists)
+    print(ids)
+
+
+def main(model, tokenizer, sentence_df, sentence_embeddings, index):
+    st.markdown("## AI-based Paraphrasing for Academic Writing")
+
+    input_text = st.text_area("text input", "Model have good results.", placeholder="Write something here...")
+    top_k = st.number_input('top_k', min_value=1, value=10, step=1)
+
+    get_retrieval_results(index, input_text, top_k, model, tokenizer, sentence_df)
+
+
+if __name__ == "__main__":
+    model, tokenizer = load_model_and_tokenizer()
+    sentence_df = load_sentence_data()
+    sentence_emeddings = load_sentence_embeddings()
+
+    faiss.normalize_L2(sentence_emeddings)
+    index = build_faiss_index(sentence_emeddings)
+
+    main(model, tokenizer, sentence_df, sentence_emeddings, index)