app.py

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)
"""

if 'query' not in st.session_state:
    query = st.text_input("", value="artificial intelligence", key="query")
    st.session_state['query'] = 'value'
else:
    query = st.text_input("", value=st.session_state["query"], key="query")
    st.session_state.query = query 
if 'query' not in st.session_state:
    st.session_state.query = 'value'    
st.write(st.session_state.query)

@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


# Session state
if 'key' not in st.session_state:
    st.session_state['key'] = 'value'
if 'key' not in st.session_state:
    st.session_state.key = 'value'
st.write(st.session_state.key)

#st.session_state.key = 'value2'     # Attribute API
#st.session_state['key'] = 'value2'  # Dictionary like API

st.write(st.session_state)

st.session_state
for key in st.session_state.keys():
    del st.session_state[key]
st.text_input("Your name", key="name")
st.session_state.name

try:
    query_params = st.experimental_get_query_params()
    #query_option = query_params['option'][0] #throws an exception when visiting http://host:port
    query_option = query_params['query'][0] #throws an exception when visiting http://host:port
    option_selected = st.sidebar.selectbox('Pick option', options, index=options.index(query_option))
    #if option_selected:
    #    st.experimental_set_query_params(option=option_selected)
except: # catch exception and set query param to predefined value
    st.experimental_set_query_params(query="Genomics") # defaults to dog
    query_params = st.experimental_get_query_params()
    #query_option = query_params['option'][0]
    query_option = query_params['query'][0]
    #if option_selected:
    #    st.experimental_set_query_params(option=option_selected)
    



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()