import streamlit as st import numpy as np from pandas import DataFrame # from keybert import KeyBERT # For Flair (Keybert) # from flair.embeddings import TransformerDocumentEmbeddings import seaborn as sns # For download buttons from functionforDownloadButtons import download_button import os import json from kpe_ranker import KpeRanker st.set_page_config( page_title="استخراج عبارات کلیدی عهد", page_icon="🎈", ) def _max_width_(): max_width_str = f"max-width: 1400px;" st.markdown( f""" """, unsafe_allow_html=True, ) _max_width_() c30, c31, c32 = st.columns([2.5, 1, 3]) with c30: # st.image("logo.png", width=400) st.title("🔑 استخراج عبارات کلیدی") st.header("") with st.expander("ℹ️ - About this app", expanded=True): st.write( """ - استخراج عبارات کلیدی، محصولی نوین از شرکت عهد است که در ارزیابی‌های صورت‌گرفته، دقت بیشتری را نسبت به رقبا از خود نشان داده است. """ ) st.markdown("") st.markdown("") # st.markdown("## **...**") with st.form(key="my_form"): ce, c1, ce, c2, c3 = st.columns([0.07, 1, 0.07, 5, 0.07]) with c1: # if ModelType == "Default (DistilBERT)": # kw_model = KeyBERT(model=roberta) @st.cache(allow_output_mutation=True) def load_model(): return KpeRanker() kpe_ranker_extractor = load_model() # else: # @st.cache(allow_output_mutation=True) # def load_model(): # return KeyBERT("distilbert-base-nli-mean-tokens") # kw_model = load_model() top_N = st.slider( "# تعداد", min_value=1, max_value=30, value=10, help="You can choose the number of keywords/keyphrases to display. Between 1 and 30, default number is 10.", ) # min_Ngrams = st.number_input( # "Minimum Ngram", # min_value=1, # max_value=4, # help="""The minimum value for the ngram range. # *Keyphrase_ngram_range* sets the length of the resulting keywords/keyphrases. # To extract keyphrases, simply set *keyphrase_ngram_range* to (1, 2) or higher depending on the number of words you would like in the resulting keyphrases.""", # # help="Minimum value for the keyphrase_ngram_range. keyphrase_ngram_range sets the length of the resulting keywords/keyphrases. To extract keyphrases, simply set keyphrase_ngram_range to (1, # 2) or higher depending on the number of words you would like in the resulting keyphrases.", # ) # max_Ngrams = st.number_input( # "Maximum Ngram", # value=2, # min_value=1, # max_value=4, # help="""The maximum value for the keyphrase_ngram_range. # *Keyphrase_ngram_range* sets the length of the resulting keywords/keyphrases. # To extract keyphrases, simply set *keyphrase_ngram_range* to (1, 2) or higher depending on the number of words you would like in the resulting keyphrases.""", # ) # StopWordsCheckbox = st.checkbox( # "Remove stop words", # help="Tick this box to remove stop words from the document (currently English only)", # ) use_ner = st.checkbox( "NER", value=True, help="استفاده از شناسایی موجودیت‌های نام‌دار" ) with c2: doc = st.text_area( "متن خود را وارد کنید", height=510, ) MAX_WORDS = 500 import re res = len(re.findall(r"\w+", doc)) if res > MAX_WORDS: st.warning( "⚠️ Your text contains " + str(res) + " words." + " Only the first 500 words will be reviewed. Stay tuned as increased allowance is coming! 😊" ) doc = doc[:MAX_WORDS] submit_button = st.form_submit_button(label="✨ پردازش") if not submit_button: st.stop() #################################### get keyphrases ####################################################### keywords = kpe_ranker_extractor.extract(text=doc, count=top_N, using_ner=use_ner, return_sorted=True) # print(keywords) st.markdown("## **🎈 Check & download results **") st.header("") cs, c1, c2, c3, cLast = st.columns([2, 1.5, 1.5, 1.5, 2]) with c1: CSVButton2 = download_button(keywords, "Data.csv", "📥 Download (.csv)") with c2: CSVButton2 = download_button(keywords, "Data.txt", "📥 Download (.txt)") with c3: CSVButton2 = download_button(keywords, "Data.json", "📥 Download (.json)") st.header("") df = ( DataFrame(keywords, columns=["Keyword/Keyphrase", "Relevancy"]) .sort_values(by="Relevancy", ascending=False) .reset_index(drop=True) ) df.index += 1 # Add styling cmGreen = sns.light_palette("green", as_cmap=True) cmRed = sns.light_palette("red", as_cmap=True) df = df.style.background_gradient( cmap=cmGreen, subset=[ "Relevancy", ], ) c1, c2, c3 = st.columns([1, 3, 1]) format_dictionary = { "Relevancy": "{:.1%}", } df = df.format(format_dictionary) with c2: st.table(df)