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Runtime error
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import time
import json
import numpy as np
import streamlit as st
from pathlib import Path
import sys
path_root = Path("./")
sys.path.append(str(path_root))
st.set_page_config(page_title="PSC Runtime",
page_icon='🌸', layout="centered")
# cola, colb, colc = st.columns([5, 4, 5])
# colaa, colbb, colcc = st.columns([1, 8, 1])
# with colbb:
# runtime = st.select_slider(
# 'Select a runtime type',
# options=['PyTorch', 'ONNX Runtime'])
# st.write('Now using: ', runtime)
# colaa, colbb, colcc = st.columns([1, 8, 1])
# with colbb:
# encoder = st.select_slider(
# 'Select a query encoder',
# options=['uniCOIL', 'SPLADE++ Ensemble Distil', 'SPLADE++ Self Distil'])
# st.write('Now Running Encoder: ', encoder)
# if runtime == 'PyTorch':
# runtime = 'pytorch'
# runtime_index = 1
# else:
# runtime = 'onnx'
# runtime_index = 0
col1, col2 = st.columns([9, 1])
with col1:
search_query = st.text_input(label="search query", placeholder="Search")
with col2:
st.write('#')
button_clicked = st.button("🔎")
import torch
fn = "dl19-gpt-3.5.pt"
object = torch.load(fn)
# for x for x in object:
# outputs = [x[2] for x in object]
outputs = object[2]
query2outputs = {}
for output in outputs:
all_queries = {x['query'] for x in output}
assert len(all_queries) == 1
query = list(all_queries)[0]
query2outputs[query] = [x['hits'] for x in output]
search_query = sorted(query2outputs)[0]
def preferences_from_hits(list_of_hits):
docid2id = {}
id2doc = {}
preferences = []
for result in list_of_hits:
for doc in result:
if doc["docid"] not in docid2id:
id = len(doc["docid"])
docid2id[doc["docid"]] = id
id2doc[id] = doc
preferences.append([docid2id[doc["docid"]] for doc in result])
# = {v: k for k, v in docid2id.items()}
return np.array(preferences), id2doc
def aggregate(list_of_hits):
import numpy as np
from permsc import KemenyOptimalAggregator, sum_kendall_tau, ranks_from_preferences
from permsc import BordaRankAggregator
preferences, id2doc = preferences_from_hits(list_of_hits)
y_optimal = KemenyOptimalAggregator().aggregate(preferences)
# y_optimal = BordaRankAggregator().aggregate(preferences)
print("preference:")
print(preferences)
print("preferences shape: ", preferences.shape)
print("y_optimal: ", y_optimal)
return [id2doc[id] for id in y_optimal]
aggregated_ranking = aggregate(query2outputs[search_query])
if search_query or button_clicked:
num_results = None
t_0 = time.time()
# search_results = query2outputs[search_query][0] # first from the 20
search_results = aggregated_ranking
st.write(
f'<p align=\"right\" style=\"color:grey;\"> Before aggregation for query [{search_query}] ms</p>', unsafe_allow_html=True)
for i, result in enumerate(search_results):
result_id = result["docid"]
contents = result["content"]
# output = f'<div class="row"> <b>Rank</b>: {i+1} | <b>Document ID</b>: {result_id} | <b>Score</b>:{result_score:.2f}</div>'
output = f'<div class="row"> <b>Rank</b>: {i+1} | <b>Document ID</b>: {result_id}'
try:
st.write(output, unsafe_allow_html=True)
st.write(
f'<div class="row">{contents}</div>', unsafe_allow_html=True)
except:
pass
st.write('---') |