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,CausalLMModel,SGPTQnAModel
import torch
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"}
from transformers import BertTokenizer, BertForMaskedLM
view_count_file = "view_count.txt"
def get_views():
ret_val = 0
if ("view_count" not in st.session_state):
try:
data = int(open(view_count_file).read().strip("\n"))
except:
data = 0
data += 1
ret_val = data
st.session_state["view_count"] = data
with open(view_count_file,"w") as fp:
fp.write(str(data))
else:
ret_val = st.session_state["view_count"]
return "{:,}".format(ret_val)
def construct_model_info_for_display(model_names):
options_arr = []
markdown_str = f"
Models evaluated ({len(model_names)})
"
for node in model_names:
options_arr .append(node["name"])
if (node["mark"] == "True"):
markdown_str += f""
if ("Note" in node):
markdown_str += f""
markdown_str += "
"
markdown_str += "Note:
• Uploaded files are loaded into non-persistent memory for the duration of the computation. They are not cached
"
limit = "{:,}".format(MAX_INPUT)
markdown_str += f"• User uploaded file has a maximum limit of {limit} sentences.
"
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"{response_info}
"
score_text = "cosine_distance" if app_mode == "similarity" else "cosine_distance/score"
pivot_name = "main sentence" if app_mode == "similarity" else "query"
main_sent += f"Results sorted by {score_text}. Closest to furthest away from {pivot_name}
"
pivot_name = pivot_name[0].upper() + pivot_name[1:]
main_sent += f"{pivot_name}: {orig_sentences[main_index]}
"
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"{index}] {key} {results[key]:.2f}
")
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)
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("Compare popular/state-of-the-art models for tasks using sentence embeddings
", unsafe_allow_html=True)
st.markdown(f"Use cases for sentence embeddings
• {use_case['1']}
• {use_case['2']}
• {use_case['3']}
This app illustrates '{curr_use_case}' use case
", unsafe_allow_html=True)
st.markdown(f"views: {get_views()}
", unsafe_allow_html=True)
try:
with st.form('twc_form'):
uploaded_file = st.file_uploader("Step 1. Upload text file(one sentence in a line) or choose an example text file below", 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 == "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")