File size: 7,997 Bytes
fbe0fa0
 
 
 
 
 
 
 
 
 
 
1873d2c
 
9916d6b
 
 
8b2cfa1
 
 
 
 
 
 
 
 
8f74413
613b336
 
ee19efa
221071c
a0a8ead
8f74413
613b336
 
ee19efa
613b336
 
 
 
 
 
9916d6b
 
 
 
 
 
 
 
 
 
8b2cfa1
 
9916d6b
5391edd
 
 
 
 
 
8b2cfa1
5391edd
 
 
8b2cfa1
9916d6b
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
f28647e
 
 
 
 
 
 
 
 
88cc794
905458c
 
 
 
 
 
 
 
 
 
 
 
fbe0fa0
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
bbfd9f4
4c2ebf4
fbe0fa0
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
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

# This lil dealio is my test of the new experiemntal primitives which promise to put cach in streamlit within striking distance of simulating cognitive episodic memory (personalized feelings about a moment through space time), and semantic memory (factual memories we are ready to share and communicate like your email address or physical address yo

# callback to update query param on selectbox change
def update_params():
    print("update1")
    try:
        st.experimental_set_query_params(option=st.session_state.query)
    except ValueError:
        pass
        
# RADIO BUTTON SET PERSIST
selected_option = st.radio(
    "Param", options, index=ix, key="query", on_change=update_params
)

# check if here for the first time then set the query
if 'query' not in st.session_state:
    #st.session_state['query'] = 'AI'
    query = st.text_input("", value="AI", key="query")
    #st.session_state.query = 'AI' 
    st.write(st.session_state.query)
else:
    query = st.text_input("", value=st.session_state["query"], key="query")

try:
    st.session_state.query = query  # if set already above.  this prevents two interface elements setting it first time once
except: # catch exception and set query param to predefined value
    print("Error cant set after init")
#if 'query' not in st.session_state:

# radio button persistance - plan is to hydrate when selected and change url along with textbox and search
options = ["ai", "nlp", "iot", "vr", "genomics", "graph", "cognitive"]
query_params = st.experimental_get_query_params()
ix = 0
if query_params:
    try:
        q0 = query_params['query'][0]
        ix = options.index(q0)
    except ValueError:
        pass
        


# Text Input, check the query params  set the text input to query value if in session
try:
    query_params = st.experimental_get_query_params()
    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))
except: # catch exception and set query param to predefined value
    #st.experimental_set_query_params(query="Genomics") # set default
    query_params = st.experimental_get_query_params()
    query_option = query_params['query'][0]

#st.experimental_set_query_params(option=selected_option)


# What impresses me about these two beautiful new streamlit persist prims is that one called the singleton can share memory across sessions (think all users yo)
#@st.experimental_singleton
#def get_sessionmaker(search_param):
#	url = "https://en.wikipedia.org/wiki/"
#	return url
#search_param = "Star_Trek:_Discovery"
#sm=  get_sessionmaker(search_param)

# What is supercool about the second prim the memo is it makes unwieldy data very wieldy.  Like the Lord of Rings in reverse re "you cannot wield it!  none of us can." ->  "You can wield it, now everyone can."
#@st.experimental_memo
#def factorial(n):
#	if n < 1:
#		return 1
#	return n * factorial(n - 1)
#em10 = factorial(10)
#em09 = factorial(9)  # Returns instantly!




DEVICE = "cpu"
MODEL_OPTIONS = ["msmarco-distilbert-base-tas-b", "all-mpnet-base-v2"]
DESCRIPTION = """
# Semantic search
**Enter your query and hit enter**
Built with πŸ€— 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)
"""

# Session state - search parms
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.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

@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




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()