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# Copyright 2022 Ken Kawamura
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import streamlit as st
import requests
from typing import Dict
import os
import sys
sys.path.append(os.path.join(os.path.dirname(__file__), "..", ".."))
from app.evaluation_scripts.run_eval import multi_inference_rank_eval
st.set_page_config(layout="wide")
st.markdown(f'<p style="text-align:center;background-image: linear-gradient(to right,#1aa3ff, #00ff00);color:#ffffff;font-size: 4em;;border-radius:2%;-webkit-background-clip: text;-webkit-text-fill-color: transparent;">Submit your question here</p>', unsafe_allow_html=True)
st.sidebar.markdown("# Evaluation 🤔")
st.markdown(
'<style>div[data-testid="stHorizontalBlock"] > div:first-of-type {border:10px;padding:30px;border-radius: 10px;;background-image: linear-gradient(45deg, #1aa3ff, #234599);}</style>', unsafe_allow_html=True)
st.markdown(
'<style>div[data-testid="stHorizontalBlock"] > div:nth-of-type(2) {border:10px;padding:30px;border-radius: 10px;background-image: linear-gradient(45deg, #f3ec78, #023611);}</style>', unsafe_allow_html=True)
INCLUDED_USERS = ['google', 'EleutherAI',
"bigscience", "facebook", "openai", "microsoft"]
PIPELINE_TAG_TO_TASKS = {
'text-generation': "CausalLM", 'text2text-generation': "Seq2SeqLM"}
@st.cache
def fetch_model_info_from_huggingface_api() -> Dict[str, Dict[str, str]]:
requests.get("https://huggingface.co")
response = requests.get("https://huggingface.co/api/models")
tags = response.json()
model_to_model_id = {}
model_to_pipeline_tag = {}
for model in tags:
model_name = model['modelId']
is_community_model = "/" in model_name
if is_community_model:
user = model_name.split("/")[0]
if user not in INCLUDED_USERS:
continue
if "pipeline_tag" in model and model["pipeline_tag"] in list(PIPELINE_TAG_TO_TASKS.keys()):
model_to_model_id[model['id']] = model['modelId']
model_to_pipeline_tag[model['id']
] = PIPELINE_TAG_TO_TASKS[model["pipeline_tag"]]
return model_to_pipeline_tag
model_to_auto_class = fetch_model_info_from_huggingface_api()
col1, col2 = st.columns([3, 2])
user_input = {}
with col1:
st.header("Question")
user_input['context'] = st.text_input(
label='Write your question. You may explicity mention the answer choices in the prompt.', value='Huggingface is awesome. True or False?')
user_input['answer_choices_texts'] = st.text_input(
label='Add answer choices in text spearated by a comma and a space.', value='True, False')
user_input['answer_choices_texts'] = user_input['answer_choices_texts'].split(
', ')
with col2:
st.header("Model Config")
user_input['model'] = st.selectbox(
"Which model?", list(model_to_auto_class.keys()))
user_input['auto_class'] = model_to_auto_class[user_input['model']]
col4, col5 = st.columns(2)
with col5:
#style taken from https://css-tricks.com/css-hover-effects-background-masks-3d/
st.markdown("""<style>
div.stButton > button:first-child {
width: 100%;
border: 4px solid;
border-image: repeating-linear-gradient(135deg,#F8CA00 0 10px,#E97F02 0 20px,#BD1550 0 30px) 8;
-webkit-mask:
conic-gradient(from 180deg at top 8px right 8px, #0000 90deg,#000 0)
var(--_i,200%) 0 /200% var(--_i,8px) border-box no-repeat,
conic-gradient(at bottom 8px left 8px, #0000 90deg,#000 0)
0 var(--_i,200%)/var(--_i,8px) 200% border-box no-repeat,
linear-gradient(#000 0 0) padding-box no-repeat;
transition: .3s, -webkit-mask-position .3s .3s;
}
button:hover {
--_i: 100%;
color: #CC333F;
transition: .3s, -webkit-mask-size .3s .3s;
}
</style>""", unsafe_allow_html=True)
st.header("Submit task")
submit = st.button('Submit')
with col4:
st.header("Result")
if submit:
with st.spinner('Wait for it...'):
prediction = multi_inference_rank_eval(
user_input['model'], user_input['auto_class'], user_input['answer_choices_texts'], user_input['context'])
# print(prediction)
st.markdown(f"### {user_input['answer_choices_texts'][prediction]}")
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