app.py

import time
import sys
import streamlit as st
import string
from io import StringIO 
import pdb
import json
from twc_embeddings import HFModel,SimCSEModel,SGPTModel
import torch
import requests


MAX_INPUT = 100

SEM_SIMILARITY="1"
DOC_RETRIEVAL="2"
CLUSTERING="3"


use_case = {"1":"Finding similar phrases/sentences","2":"Retrieving semantically matching information to a query. It may not be a factual match","3":"Clustering"}
use_case_url = {"1":"https://huggingface.co/spaces/taskswithcode/semantic_similarity","2":"https://huggingface.co/spaces/taskswithcode/semantic_search","3":""}


APP_NAME = "hf/semantic_similarity"
INFO_URL = "http://www.taskswithcode.com/stats/"


from transformers import BertTokenizer, BertForMaskedLM




def get_views(action):
    ret_val = 0
    if ("view_count" not in st.session_state):
        try:
           app_info = {'name': APP_NAME,"action":action}
           res = requests.post(INFO_URL, json = app_info).json()
           print(res)
           data = res["count"]
        except:
           data = 0
        ret_val = data
        st.session_state["view_count"] = data
    else:
        ret_val = st.session_state["view_count"]
        if (action != "init"):
           app_info = {'name': APP_NAME,"action":action}
           res = requests.post(INFO_URL, json = app_info).json()
    return "{:,}".format(ret_val)
        


def construct_model_info_for_display(model_names):
    options_arr  = []
    markdown_str = f"<div style=\"font-size:16px; color: #2f2f2f; text-align: left\"><br/><b>Models evaluated ({len(model_names)})</b></div>"
    for node in model_names:
        options_arr .append(node["name"])
        if (node["mark"] == "True"):
            markdown_str += f"<div style=\"font-size:16px; color: #5f5f5f; text-align: left\">&nbsp;•&nbsp;Model:&nbsp;<a href=\'{node['paper_url']}\' target='_blank'>{node['name']}</a><br/>&nbsp;&nbsp;&nbsp;&nbsp;Code released by:&nbsp;<a href=\'{node['orig_author_url']}\' target='_blank'>{node['orig_author']}</a><br/>&nbsp;&nbsp;&nbsp;&nbsp;Model info:&nbsp;<a href=\'{node['sota_info']['sota_link']}\' target='_blank'>{node['sota_info']['task']}</a></div>"
            if ("Note" in node):
                markdown_str += f"<div style=\"font-size:16px; color: #a91212; text-align: left\">&nbsp;&nbsp;&nbsp;&nbsp;{node['Note']}<a href=\'{node['alt_url']}\' target='_blank'>link</a></div>"
            markdown_str += "<div style=\"font-size:16px; color: #5f5f5f; text-align: left\"><br/></div>"
        
    markdown_str += "<div style=\"font-size:12px; color: #9f9f9f; text-align: left\"><b>Note:</b><br/>•&nbsp;Uploaded files are loaded into non-persistent memory for the duration of the computation. They are not cached</div>"
    limit = "{:,}".format(MAX_INPUT)
    markdown_str += f"<div style=\"font-size:12px; color: #9f9f9f; text-align: left\">•&nbsp;User uploaded file has a maximum limit of {limit} sentences.</div>"
    return options_arr,markdown_str


st.set_page_config(page_title='TWC - Compare popular/state-of-the-art models for tasks using sentence embeddings', page_icon="logo.jpg", layout='centered', initial_sidebar_state='auto',
            menu_items={
             'About': 'This app was created by taskswithcode. http://taskswithcode.com'
             
              })
col,pad = st.columns([85,15])

with col:
    st.image("long_form_logo_with_icon.png")


@st.experimental_memo
def load_model(model_name,model_names):
    try:
        ret_model = None
        for node in model_names:
            if (model_name.startswith(node["name"])):
                obj_class = globals()[node["class"]]
                ret_model = obj_class()
                ret_model.init_model(node["model"])
        assert(ret_model is not None)
    except Exception as e:
        st.error("Unable to load model:" + model_name + " " +  str(e))
        pass
    return ret_model

  
@st.experimental_memo
def cached_compute_similarity(sentences,_model,model_name,main_index):
    texts,embeddings = _model.compute_embeddings(sentences,is_file=False)
    results = _model.output_results(None,texts,embeddings,main_index)
    return results


def uncached_compute_similarity(sentences,_model,model_name,main_index):
    with st.spinner('Computing vectors for sentences'):
        texts,embeddings = _model.compute_embeddings(sentences,is_file=False)
        results = _model.output_results(None,texts,embeddings,main_index)
    #st.success("Similarity computation complete")
    return results

def get_model_info(model_names,model_name):
    for node in model_names:
        if (model_name == node["name"]):
            return node

def run_test(model_names,model_name,sentences,display_area,main_index,user_uploaded):
    display_area.text("Loading model:" + model_name)
    model_info = get_model_info(model_names,model_name)
    if ("Note" in model_info):
        fail_link = f"{model_info['Note']} [link]({model_info['alt_url']})"
        display_area.write(fail_link)
    model = load_model(model_name,model_names)
    display_area.text("Model " + model_name  + " load complete")
    try:
            if (user_uploaded):
                results = uncached_compute_similarity(sentences,model,model_name,main_index)
            else:
                display_area.text("Computing vectors for sentences")
                results = cached_compute_similarity(sentences,model,model_name,main_index)
                display_area.text("Similarity computation complete")
            return results
            
    except Exception as e:
        st.error("Some error occurred during prediction" + str(e))
        st.stop()
    return {}



    

def display_results(orig_sentences,main_index,results,response_info,app_mode):
    main_sent = f"<div style=\"font-size:14px; color: #2f2f2f; text-align: left\">{response_info}<br/><br/></div>"
    score_text = "cosine_distance" if app_mode == SEM_SIMILARITY else "cosine_distance/score"
    pivot_name = "main sentence" if app_mode == SEM_SIMILARITY else "query"
    main_sent += f"<div style=\"font-size:14px; color: #6f6f6f; text-align: left\">Results sorted by {score_text}. Closest to furthest away from {pivot_name}</div>"
    pivot_name = pivot_name[0].upper() + pivot_name[1:]
    main_sent += f"<div style=\"font-size:16px; color: #2f2f2f; text-align: left\"><b>{pivot_name}:</b>&nbsp;&nbsp;{orig_sentences[main_index]}</div>"
    body_sent = []
    download_data = {}
    first = True
    for key in results:
        if (app_mode == DOC_RETRIEVAL and first):
            first = False
            continue
        index = orig_sentences.index(key) + 1
        body_sent.append(f"<div style=\"font-size:16px; color: #2f2f2f; text-align: left\">{index}]&nbsp;{key}&nbsp;&nbsp;&nbsp;<b>{results[key]:.2f}</b></div>")
        download_data[key] =  f"{results[key]:.2f}" 
    main_sent = main_sent + "\n" + '\n'.join(body_sent)
    st.markdown(main_sent,unsafe_allow_html=True)
    st.session_state["download_ready"] = json.dumps(download_data,indent=4)
    get_views("submit")
    


def init_session():
    st.session_state["download_ready"] = None    
    st.session_state["model_name"] = "ss_test"
    st.session_state["main_index"] = 1
    st.session_state["file_name"] = "default"
 
def app_main(app_mode,example_files,model_name_files):
  init_session()
  with open(example_files) as fp:
        example_file_names = json.load(fp) 
  with open(model_name_files) as fp:
        model_names = json.load(fp)
  curr_use_case = use_case[app_mode].split(".")[0]
  st.markdown("<h5 style='text-align: center;'>Compare popular/state-of-the-art models for tasks using sentence embeddings</h5>", unsafe_allow_html=True)
  st.markdown(f"<div style='color: #4f4f4f; text-align: left'>Use cases for sentence embeddings<br/>&nbsp;&nbsp;&nbsp;•&nbsp;&nbsp;{use_case['1']}<br/>&nbsp;&nbsp;&nbsp;•&nbsp;&nbsp;<a href=\'{use_case_url['2']}\' target='_blank'>{use_case['2']}</a><br/>&nbsp;&nbsp;&nbsp;•&nbsp;&nbsp;{use_case['3']}<br/><i>This app illustrates <b>'{curr_use_case}'</b> use case</i></div>", unsafe_allow_html=True)
  st.markdown(f"<div style='color: #9f9f9f; text-align: right'>views:&nbsp;{get_views('init')}</div>", unsafe_allow_html=True)


  try:
      
      
      with st.form('twc_form'):

        step1_line = "Step 1. Upload text file(one sentence in a line) or choose an example text file below"
        if (app_mode ==  DOC_RETRIEVAL):
            step1_line += ". The first line is treated as the query"
        uploaded_file = st.file_uploader(step1_line, type=".txt")

        selected_file_index = st.selectbox(label=f'Example files ({len(example_file_names)})',  
                    options = list(dict.keys(example_file_names)), index=0,  key = "twc_file")
        st.write("")
        options_arr,markdown_str = construct_model_info_for_display(model_names)
        selection_label = 'Step 2. Select Model'
        selected_model = st.selectbox(label=selection_label,  
                    options = options_arr, index=0,  key = "twc_model")
        st.write("")
        if (app_mode == SEM_SIMILARITY):
            main_index = st.number_input('Step 3. Enter index of sentence in file to make it the main sentence',value=1,min_value = 1)
        else:
            main_index = 1
        st.write("")
        submit_button = st.form_submit_button('Run')

        
        input_status_area = st.empty()
        display_area = st.empty()
        if submit_button:
            start = time.time()
            if uploaded_file is not None:
                st.session_state["file_name"]  = uploaded_file.name
                sentences = StringIO(uploaded_file.getvalue().decode("utf-8")).read()
            else:
                st.session_state["file_name"]  = example_file_names[selected_file_index]["name"]
                sentences = open(example_file_names[selected_file_index]["name"]).read()
            sentences = sentences.split("\n")[:-1]
            if (len(sentences) < main_index):
                main_index = len(sentences)
                st.info("Selected sentence index is larger than number of sentences in file. Truncating to " + str(main_index)) 
            if (len(sentences) > MAX_INPUT):
                st.info(f"Input sentence count exceeds maximum sentence limit. First {MAX_INPUT} out of {len(sentences)} sentences chosen")
                sentences = sentences[:MAX_INPUT]
            st.session_state["model_name"] = selected_model
            st.session_state["main_index"] = main_index
            results = run_test(model_names,selected_model,sentences,display_area,main_index - 1,(uploaded_file is not None))
            display_area.empty()
            with display_area.container():
                device = 'GPU' if torch.cuda.is_available() else 'CPU'
                response_info = f"Computation time on {device}: {time.time() - start:.2f} secs for {len(sentences)} sentences"
                display_results(sentences,main_index - 1,results,response_info,app_mode)
                #st.json(results)
      st.download_button(
         label="Download results as json",
         data= st.session_state["download_ready"] if st.session_state["download_ready"] != None else "",
         disabled = False if st.session_state["download_ready"] != None else True,
         file_name= (st.session_state["model_name"] + "_" +  str(st.session_state["main_index"]) + "_" + '_'.join(st.session_state["file_name"].split(".")[:-1]) + ".json").replace("/","_"),
         mime='text/json',
         key ="download" 
        )
      
      

  except Exception as e:
    st.error("Some error occurred during loading" + str(e))
    st.stop()  
	
  st.markdown(markdown_str, unsafe_allow_html=True)
  
 

if __name__ == "__main__":
   #print("comand line input:",len(sys.argv),str(sys.argv))
   #app_main(sys.argv[1],sys.argv[2],sys.argv[3])
   app_main("1","sim_app_examples.json","sim_app_models.json")
   #app_main("2","doc_app_examples.json","doc_app_models.json")