App moved to another Space

README.md

@@ -1,5 +1,5 @@
 ---
-title: Semantic Search
+title: Semantic Search (obsolete)
 emoji: 📖
 colorFrom: purple
 colorTo: red

app.py

@@ -1,147 +1,2 @@
-import time
-import re
-import pandas as pd
-import numpy as np
-import torch
-import torch.nn.functional as F
-from transformers import AutoTokenizer, AutoModel
-from tokenizers import Tokenizer, AddedToken
 import streamlit as st
-from st_click_detector import click_detector
-
-DEVICE = "cpu"
-MODEL_OPTIONS = ["msmarco-distilbert-base-tas-b", "all-mpnet-base-v2"]
-DESCRIPTION = """
-# Semantic search
-
-**Enter your query and hit enter**
-
-Built with 🤗 Hugging Face's [transformers](https://huggingface.co/transformers/) library, [SentenceBert](https://www.sbert.net/) models, [Streamlit](https://streamlit.io/) and 44k movie descriptions from the Kaggle [Movies Dataset](https://www.kaggle.com/rounakbanik/the-movies-dataset)
-"""
-
-
-@st.cache(
-    show_spinner=False,
-    hash_funcs={
-        AutoModel: lambda _: None,
-        AutoTokenizer: lambda _: None,
-        dict: lambda _: None,
-    },
-)
-def load():
-    models, tokenizers, embeddings = [], [], []
-    for model_option in MODEL_OPTIONS:
-        tokenizers.append(
-            AutoTokenizer.from_pretrained(f"sentence-transformers/{model_option}")
-        )
-        models.append(
-            AutoModel.from_pretrained(f"sentence-transformers/{model_option}").to(
-                DEVICE
-            )
-        )
-    embeddings.append(np.load("embeddings.npy"))
-    embeddings.append(np.load("embeddings2.npy"))
-    df = pd.read_csv("movies.csv")
-    return tokenizers, models, embeddings, df
-
-
-tokenizers, models, embeddings, df = load()
-
-
-def pooling(model_output):
-    return model_output.last_hidden_state[:, 0]
-
-
-def compute_embeddings(texts):
-    encoded_input = tokenizers[0](
-        texts, padding=True, truncation=True, return_tensors="pt"
-    ).to(DEVICE)
-
-    with torch.no_grad():
-        model_output = models[0](**encoded_input, return_dict=True)
-
-    embeddings = pooling(model_output)
-
-    return embeddings.cpu().numpy()
-
-
-def pooling2(model_output, attention_mask):
-    token_embeddings = model_output[0]
-    input_mask_expanded = (
-        attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
-    )
-    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(
-        input_mask_expanded.sum(1), min=1e-9
-    )
-
-
-def compute_embeddings2(list_of_strings):
-    encoded_input = tokenizers[1](
-        list_of_strings, padding=True, truncation=True, return_tensors="pt"
-    ).to(DEVICE)
-    with torch.no_grad():
-        model_output = models[1](**encoded_input)
-    sentence_embeddings = pooling2(model_output, encoded_input["attention_mask"])
-    return F.normalize(sentence_embeddings, p=2, dim=1).cpu().numpy()
-
-
-@st.cache(
-    show_spinner=False,
-    hash_funcs={Tokenizer: lambda _: None, AddedToken: lambda _: None},
-)
-def semantic_search(query, model_id):
-    start = time.time()
-    if len(query.strip()) == 0:
-        return ""
-    if "[Similar:" not in query:
-        if model_id == 0:
-            query_embedding = compute_embeddings([query])
-        else:
-            query_embedding = compute_embeddings2([query])
-    else:
-        match = re.match(r"\[Similar:(\d{1,5}).*", query)
-        if match:
-            idx = int(match.groups()[0])
-            query_embedding = embeddings[model_id][idx : idx + 1, :]
-            if query_embedding.shape[0] == 0:
-                return ""
-        else:
-            return ""
-    indices = np.argsort(embeddings[model_id] @ np.transpose(query_embedding)[:, 0])[
-        -1:-11:-1
-    ]
-    if len(indices) == 0:
-        return ""
-    result = "<ol>"
-    for i in indices:
-        result += f"<li style='padding-top: 10px'><b>{df.iloc[i].title}</b> ({df.iloc[i].release_date}). {df.iloc[i].overview} "
-        result += f"<a id='{i}' href='#'>Similar movies</a></li>"
-    delay = "%.3f" % (time.time() - start)
-    return f"<p><i>Computation time: {delay} seconds</i></p>{result}</ol>"
-
-
-st.sidebar.markdown(DESCRIPTION)
-
-model_choice = st.sidebar.selectbox("Similarity model", options=MODEL_OPTIONS)
-model_id = 0 if model_choice == MODEL_OPTIONS[0] else 1
-
-if "query" in st.session_state:
-    query = st.text_input("", value=st.session_state["query"])
-else:
-    query = st.text_input("", value="time travel")
-
-clicked = click_detector(semantic_search(query, model_id))
-
-if clicked != "":
-    st.markdown(clicked)
-    change_query = False
-    if "last_clicked" not in st.session_state:
-        st.session_state["last_clicked"] = clicked
-        change_query = True
-    else:
-        if clicked != st.session_state["last_clicked"]:
-            st.session_state["last_clicked"] = clicked
-            change_query = True
-    if change_query:
-        st.session_state["query"] = f"[Similar:{clicked}] {df.iloc[int(clicked)].title}"
-        st.experimental_rerun()
+st.markdown("Web app moved [here](https://huggingface.co/spaces/vivien/semantic-search2)")

embeddings.npy

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:64495712bf1903dd04604cd5641f5b521912d8938339e9e9e3071dad8952b34a
-size 134876288

embeddings2.npy

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:136aa7ffd5630d19dc88f1e779dbeb04011ef918ac3fba2148a8f5d58303d736
-size 134876288

movies.csv

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:1da4fb07829b3f57bce3fa663641c50b3d3e65cdf949f6e6f340960a5acc1005
-size 16293996

requirements.txt

@@ -1,5 +0,0 @@
-torch
-transformers
-numpy
-pandas
-st-click-detector