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| import streamlit as st | |
| import pandas as pd | |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline | |
| from annotated_text import annotated_text | |
| st.set_page_config( | |
| page_title="Requirement Identifier", layout="wide" | |
| #page_icon="🎈", | |
| ) | |
| st.cache_resource | |
| def get_pipleine(): | |
| tokenizer = AutoTokenizer.from_pretrained("SarmadBashir/dronology_bert_uncased", model_max_length = 128) | |
| model = AutoModelForSequenceClassification.from_pretrained("SarmadBashir/dronology_bert_uncased") | |
| pipe = pipeline("text-classification", model=model, tokenizer=tokenizer) | |
| return pipe | |
| pipe = get_pipleine() | |
| def get_prediction(input_text, pipe): | |
| map_labels = {'LABEL_0': 'Information', 'LABEL_1': 'Requirement'} | |
| output = pipe(input_text) | |
| label = map_labels.get(output[0]['label']) | |
| score = int(round(output[0]['score'], 2) * 100) | |
| return label, score | |
| def _max_width_(): | |
| max_width_str = f"max-width: 1400px;" | |
| st.markdown( | |
| f""" | |
| <style> | |
| .reportview-container .main .block-container{{ | |
| {max_width_str} | |
| }} | |
| </style> | |
| """, | |
| unsafe_allow_html=True, | |
| ) | |
| #_max_width_() | |
| def show_updated_list(test_data): | |
| show_list = [] | |
| text = test_data['STR.REQ'].tolist() | |
| labels = test_data['class'].tolist() | |
| for info in zip(text, labels): | |
| if info[1] == 0: | |
| updated_text = info[0] + ' (GT: Information)' | |
| else: | |
| updated_text = info[0] + ' (GT: Requirement)' | |
| show_list.append(updated_text) | |
| return show_list | |
| c30, c31, c32 = st.columns([6, 1, 3]) | |
| with c30: | |
| # st.image("logo.png", width=400) | |
| st.title("Requirement or not, that is the question!") | |
| #st.header("") | |
| with st.expander("ℹ️ - About this app", expanded=True): | |
| st.write( | |
| """ | |
| - This app is a working demo for the paper: "Requirement or not, that is the question: A case from the railway industry". | |
| - The app relies on BERT model (trained on dronology dataset) for classification of given text as Requirement or Information. | |
| - The replication package [ReqORNot](https://github.com/a66as/REFSQ2023-ReqORNot) contains all the relevant code and information for replication of experiements. | |
| """ | |
| ) | |
| st.write(""" | |
| - This work is partially funded by [AIDOaRt (KDT)](https://sites.mdu.se/aidoart) and [SmartDelta (ITEA)](https://itea4.org/project/smartdelta.html) projects. | |
| """) | |
| images= ['./pictures/smart_delta.jpeg', './pictures/aidoart.jpeg' ] | |
| #st.image(images, caption=None, width=130, use_column_width=False, clamp=False, channels="RGB", output_format="auto") | |
| #st.image('./pictures/smart_delta.jpeg', caption=None, width=130, use_column_width=None, clamp=False, channels="RGB", output_format="auto") | |
| col1,col2 = st.columns([0.7,7]) | |
| with col1: | |
| st.image(images[0],width=100,use_column_width='never') | |
| with col2: | |
| st.image(images[1],width=100,use_column_width='never') | |
| st.markdown("") | |
| st.markdown("") | |
| st.markdown("") | |
| st.markdown("") | |
| with st.form(key="my_form"): | |
| ce, c1, c2, ce = st.columns([0.04, 0.01, 1, 0.04]) | |
| with c2: | |
| doc = st.text_area( | |
| "✍️Write the text below", | |
| height=50, | |
| ) | |
| st.write("***OR***") | |
| test_data = pd.read_csv('./data/test.csv') | |
| #test_data = test_data.sample(frac=1) | |
| text = show_updated_list(test_data) | |
| text.insert(0, '-') | |
| option = st.selectbox( | |
| 'Select the row from test data', | |
| text) | |
| #print(option) | |
| st.markdown("") | |
| submit_button = st.form_submit_button(label="✨ Get the Prediction!") | |
| if not submit_button: | |
| st.stop() | |
| if option == '-' and len(doc.split()) <= 2: | |
| st.warning('Please Provide Valid Input!') | |
| st.stop() | |
| st.markdown("") | |
| st.markdown("") | |
| st.markdown("### **Output**") | |
| if option != '-' and len(doc.split())<=3: | |
| option_text = '' | |
| if '(GT: Requirement)' in option: | |
| option_text = option.replace('(GT: Requirement)', '').strip() | |
| else: | |
| option_text = option.replace('(GT: Information)', '').strip() | |
| predicted_label, probability = get_prediction(option_text, pipe) | |
| annotated_text( | |
| 'The model classified', | |
| ('selected', "", "#8ef"), 'text as: ', | |
| (predicted_label,"", "#afa"),'with a probability of', | |
| (str(probability),"", "#afa"),'%' | |
| ) | |
| else: | |
| predicted_label, probability = get_prediction(doc, pipe) | |
| annotated_text( | |
| 'The model classified', | |
| ('written', "", "#8ef"), 'text as: ', | |
| (predicted_label,"", "#afa"),'with a probability of', | |
| (str(probability),"", "#afa"),'%' | |
| ) |