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| 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""" | |
| <style> | |
| .reportview-container .main .block-container{{ | |
| {max_width_str} | |
| }} | |
| </style> | |
| """, | |
| 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) | |
| 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) | |