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| 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"} | |
| 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]}") | |