app.py

import numpy as np
import pandas as pd
import streamlit as st
from streamlit_text_rating.st_text_rater import st_text_rater
from sentiment import classify_sentiment
from sentiment_onnx_classify import classify_sentiment_onnx, classify_sentiment_onnx_quant,create_onnx_model
from zeroshot_clf import zero_shot_classification
from transformers import AutoTokenizer,AutoModelForSequenceClassification
from onnxruntime.quantization import quantize_dynamic,QuantType
import transformers.convert_graph_to_onnx as onnx_convert
from pathlib import Path
import os
import time
import plotly.express as px
import plotly.graph_objects as go
import onnxruntime as ort
global _plotly_config
_plotly_config={'displayModeBar': False}

st.set_page_config(  # Alternate names: setup_page, page, layout
    layout="wide",  # Can be "centered" or "wide". In the future also "dashboard", etc.
    initial_sidebar_state="auto",  # Can be "auto", "expanded", "collapsed"
    page_title='None',  # String or None. Strings get appended with "• Streamlit".
)


padding_top = 0
st.markdown(f"""
    <style>
        .reportview-container .main .block-container{{
            padding-top: {padding_top}rem;
        }}
    </style>""",
    unsafe_allow_html=True,
)

def set_page_title(title):
    st.sidebar.markdown(unsafe_allow_html=True, body=f"""
        <iframe height=0 srcdoc="<script>
            const title = window.parent.document.querySelector('title') \

            const oldObserver = window.parent.titleObserver
            if (oldObserver) {{
                oldObserver.disconnect()
            }} \

            const newObserver = new MutationObserver(function(mutations) {{
                const target = mutations[0].target
                if (target.text !== '{title}') {{
                    target.text = '{title}'
                }}
            }}) \

            newObserver.observe(title, {{ childList: true }})
            window.parent.titleObserver = newObserver \

            title.text = '{title}'
        </script>" />
    """)


set_page_title('NLP use cases')

# Hide Menu Option
hide_streamlit_style = """
            <style>
            #MainMenu {visibility: hidden;}
            footer {visibility: hidden;}
            </style>
            """
st.markdown(hide_streamlit_style, unsafe_allow_html=True)


@st.cache(allow_output_mutation=True, suppress_st_warning=True, max_entries=None, ttl=None)
def create_model_dir(chkpt, model_dir):
    if not os.path.exists(chkpt):
        os.mkdir(model_dir)
        _model = AutoModelForSequenceClassification.from_pretrained(chkpt)
        _tokenizer = AutoTokenizer.from_pretrained(chkpt)
        _model.save_pretrained(model_dir)
        _tokenizer.save_pretrained(model_dir)
    else:
        pass


st.title("NLP use cases")

with st.sidebar:
    st.title("NLP tasks")
    select_task=st.selectbox(label="Select task from drop down menu",
                 options=['README',
                          'Detect Sentiment','Zero Shot Classification'])

if select_task=='README':
    st.header("NLP Summary")

############### Pre-Download & instantiate objects for sentiment analysis *********************** START **********************
sent_chkpt = 'distilbert-base-uncased-finetuned-sst-2-english'
sent_model_dir='sentiment_model_dir'
#create model/token dir
create_model_dir(chkpt=sent_chkpt, model_dir=sent_model_dir)

@st.cache(allow_output_mutation=True, suppress_st_warning=True, max_entries=None, ttl=None)
def task_selected(task,sent_model_dir=sent_model_dir):
    model_sentiment=AutoModelForSequenceClassification.from_pretrained(sent_model_dir)
    tokenizer_sentiment=AutoTokenizer.from_pretrained(sent_model_dir)

    create_onnx_model(_model=model_sentiment, _tokenizer=tokenizer_sentiment)

    #create inference session
    sentiment_session = ort.InferenceSession("sent_clf_onnx/sentiment_classifier_onnx.onnx")
    sentiment_session_int8 = ort.InferenceSession("sent_clf_onnx/sentiment_classifier_onnx_int8.onnx")

    return model_sentiment,tokenizer_sentiment,sentiment_session,sentiment_session_int8


############## Pre-Download & instantiate objects for sentiment analysis ********************* END **********************************

if select_task == 'Detect Sentiment':

    t1=time.time()
    model_sentiment,tokenizer_sentiment,\
    sentiment_session,sentiment_session_int8 = task_selected(task=select_task)
    t2 = time.time()
    st.write(f"Total time to load Model is {(t2-t1)*1000:.1f} ms")

    st.header("You are now performing Sentiment Analysis")
    input_texts = st.text_input(label="Input texts separated by comma")
    c1,c2,c3,c4=st.columns(4)

    with c1:
        response1=st.button("Normal runtime")
    with c2:
        response2=st.button("ONNX runtime")
    with c3:
        response3=st.button("ONNX runtime with Quantization")
    with c4:
        response4 = st.button("Simulate 100 runs each runtime")

    if any([response1,response2,response3,response4]):
        if response1:
            start=time.time()
            sentiments = classify_sentiment(input_texts,
                                            model=model_sentiment,
                                            tokenizer=tokenizer_sentiment
                                            )
            end=time.time()
            st.write(f"Time taken for computation {(end-start)*1000:.1f} ms")
        elif response2:
            start = time.time()
            sentiments=classify_sentiment_onnx(input_texts,
                                               _session=sentiment_session,
                                               _tokenizer=tokenizer_sentiment)
            end = time.time()
            st.write(f"Time taken for computation {(end - start) * 1000:.1f} ms")
        elif response3:
            start = time.time()
            sentiments=classify_sentiment_onnx_quant(input_texts,
                                                     _session=sentiment_session_int8,
                                                     _tokenizer=tokenizer_sentiment)
            end = time.time()
            st.write(f"Time taken for computation {(end - start) * 1000:.1f} ms")
        elif response4:
            normal_runtime=[]
            for i in range(100):
                start=time.time()
                sentiments = classify_sentiment(input_texts,
                                                model=model_sentiment,
                                                tokenizer=tokenizer_sentiment)
                end=time.time()
                t = (end - start) * 1000
                normal_runtime.append(t)
            normal_runtime=np.clip(normal_runtime,10,60)

            onnx_runtime=[]
            for i in range(100):
                start=time.time()
                sentiments = classify_sentiment_onnx(input_texts,
                                               _session=sentiment_session,
                                               _tokenizer=tokenizer_sentiment)
                end=time.time()
                t=(end-start)*1000
                onnx_runtime.append(t)
            onnx_runtime = np.clip(onnx_runtime, 0, 20)

            onnx_runtime_quant=[]
            for i in range(100):
                start=time.time()
                sentiments = classify_sentiment_onnx_quant(input_texts,
                                               _session=sentiment_session,
                                               _tokenizer=tokenizer_sentiment)
                end=time.time()

                t=(end-start)*1000
                onnx_runtime_quant.append(t)
            onnx_runtime_quant = np.clip(onnx_runtime_quant, 0, 20)


            temp_df=pd.DataFrame({'Normal Runtime (ms)':normal_runtime,
                                  'ONNX Runtime (ms)':onnx_runtime,
                                  'ONNX Quant Runtime (ms)':onnx_runtime_quant})

            from plotly.subplots import make_subplots
            fig = make_subplots(rows=1, cols=3, start_cell="bottom-left",
                                subplot_titles=['Normal Runtime','ONNX Runtime','ONNX Runtime with Quantization'])

            fig.add_trace(go.Histogram(x=temp_df['Normal Runtime (ms)']),row=1,col=1)
            fig.add_trace(go.Histogram(x=temp_df['ONNX Runtime (ms)']),row=1,col=2)
            fig.add_trace(go.Histogram(x=temp_df['ONNX Quant Runtime (ms)']),row=1,col=3)
            fig.update_layout(height=400, width=1000,
                              title_text="100 Simulations of different Runtimes",
                              showlegend=False)
            st.plotly_chart(fig,config=_plotly_config )

        else:
            pass
        for i,t in enumerate(input_texts.split(',')):
            if sentiments[i]=='Positive':
                response=st_text_rater(t + f"--> This statement is {sentiments[i]}",
                                       color_background='rgb(154,205,50)',key=t)
            else:
                response = st_text_rater(t + f"--> This statement is {sentiments[i]}",
                                         color_background='rgb(233, 116, 81)',key=t)

if select_task=='Zero Shot Classification':
    st.header("You are now performing Zero Shot Classification")
    input_texts = st.text_input(label="Input text to classify into topics")
    input_lables = st.text_input(label="Enter labels separated by commas")

    c1,c2,c3,c4=st.columns(4)

    with c1:
        response1=st.button("Normal runtime")
    with c2:
        response2=st.button("ONNX runtime")
    with c3:
        response3=st.button("ONNX runtime with Quantization")
    with c4:
        response4 = st.button("Simulate 100 runs each runtime")

    if any([response1,response2,response3,response4]):
        if response1:
            start=time.time()
            output = zero_shot_classification(input_texts, input_lables)
            end=time.time()
            st.write("")
            st.write(f"Time taken for computation {(end-start)*1000:.1f} ms")
            st.plotly_chart(output, config=_plotly_config)