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| import streamlit as st | |
| from transformers import pipeline | |
| from transformers import AutoTokenizer,AutoModelForTokenClassification | |
| import math | |
| import nltk | |
| import torch | |
| from nltk.corpus import stopwords | |
| import spacy | |
| from spacy import displacy | |
| from word2number import w2n | |
| import en_core_web_md | |
| nltk.download('punkt') | |
| nltk.download('stopwords') | |
| nlp = en_core_web_md.load() | |
| sentiment_model = pipeline("text-classification", model="AhmedTaha012/managersFeedback-V1.0.7") | |
| increase_decrease_model = pipeline("text-classification", model="AhmedTaha012/nextQuarter-status-V1.1.9") | |
| tokenizer = AutoTokenizer.from_pretrained("AhmedTaha012/finance-ner-v0.0.9-finetuned-ner") | |
| model = AutoModelForTokenClassification.from_pretrained("AhmedTaha012/finance-ner-v0.0.9-finetuned-ner") | |
| # torch.compile(model) | |
| nlpPipe = pipeline("ner", model=model, tokenizer=tokenizer, grouped_entities=True) | |
| def getSpeakers(data): | |
| if "Speakers" in data: | |
| return "\n".join([x for x in data.split("Speakers")[-1].split("\n") if "--" in x]) | |
| elif "Call participants" in data: | |
| return "\n".join([x for x in data.split("Call participants")[-1].split("\n") if "--" in x]) | |
| elif "Call Participants" in data: | |
| return "\n".join([x for x in data.split("Call Participants")[-1].split("\n") if "--" in x]) | |
| def removeSpeakers(data): | |
| if "Speakers" in data: | |
| return data.split("Speakers")[0] | |
| elif "Call participants" in data: | |
| return data.split("Call participants")[0] | |
| elif "Call Participants" in data: | |
| return data.split("Call Participants")[0] | |
| def getQA(data): | |
| if "Questions and Answers" in data: | |
| return data.split("Questions and Answers")[-1] | |
| elif "Questions & Answers" in data: | |
| return data.split("Questions & Answers")[-1] | |
| elif "Q&A" in data: | |
| return data.split("Q&A")[-1] | |
| else: | |
| return "" | |
| def removeQA(data): | |
| if "Questions and Answers" in data: | |
| return data.split("Questions and Answers")[0] | |
| elif "Questions & Answers" in data: | |
| return data.split("Questions & Answers")[0] | |
| elif "Q&A" in data: | |
| return data.split("Q&A")[0] | |
| else: | |
| return "" | |
| def clean_and_preprocess(text): | |
| text=[x for x in text.split("\n") if len(x)>100] | |
| l=[] | |
| for t in text: | |
| # Convert to lowercase | |
| t = t.lower() | |
| # Tokenize text into words | |
| words = nltk.word_tokenize(t) | |
| # Remove stopwords | |
| stop_words = set(stopwords.words('english')) | |
| filtered_words = [word for word in words if word not in stop_words] | |
| # Join the words back into a cleaned text | |
| cleaned_text = ' '.join(filtered_words) | |
| l.append(cleaned_text) | |
| return "\n".join(l) | |
| def replace_abbreviations(text): | |
| replacements = { | |
| 'Q1': 'first quarter', | |
| 'Q2': 'second quarter', | |
| 'Q3': 'third quarter', | |
| 'Q4': 'fourth quarter', | |
| 'q1': 'first quarter', | |
| 'q2': 'second quarter', | |
| 'q3': 'third quarter', | |
| 'q4': 'fourth quarter', | |
| 'FY': 'fiscal year', | |
| 'YoY': 'year over year', | |
| 'MoM': 'month over month', | |
| 'EBITDA': 'earnings before interest, taxes, depreciation, and amortization', | |
| 'ROI': 'return on investment', | |
| 'EPS': 'earnings per share', | |
| 'P/E': 'price-to-earnings', | |
| 'DCF': 'discounted cash flow', | |
| 'CAGR': 'compound annual growth rate', | |
| 'GDP': 'gross domestic product', | |
| 'CFO': 'chief financial officer', | |
| 'GAAP': 'generally accepted accounting principles', | |
| 'SEC': 'U.S. Securities and Exchange Commission', | |
| 'IPO': 'initial public offering', | |
| 'M&A': 'mergers and acquisitions', | |
| 'EBIT': 'earnings before interest and taxes', | |
| 'IRR': 'internal rate of return', | |
| 'ROA': 'return on assets', | |
| 'ROE': 'return on equity', | |
| 'NAV': 'net asset value', | |
| 'PE ratio': 'price-to-earnings ratio', | |
| 'EPS growth': 'earnings per share growth', | |
| 'Fiscal Year': 'financial year', | |
| 'CAPEX': 'capital expenditure', | |
| 'APR': 'annual percentage rate', | |
| 'P&L': 'profit and loss', | |
| 'NPM': 'net profit margin', | |
| 'EBT': 'earnings before taxes', | |
| 'EBITDAR': 'earnings before interest, taxes, depreciation, amortization, and rent', | |
| 'PAT': 'profit after tax', | |
| 'COGS': 'cost of goods sold', | |
| 'EBTIDA': 'earnings before taxes, interest, depreciation, and amortization', | |
| 'E&Y': 'Ernst & Young', | |
| 'B2B': 'business to business', | |
| 'B2C': 'business to consumer', | |
| 'LIFO': 'last in, first out', | |
| 'FIFO': 'first in, first out', | |
| 'FCF': 'free cash flow', | |
| 'LTM': 'last twelve months', | |
| 'OPEX': 'operating expenses', | |
| 'TSR': 'total shareholder return', | |
| 'PP&E': 'property, plant, and equipment', | |
| 'PBT': 'profit before tax', | |
| 'EBITDAR margin': 'earnings before interest, taxes, depreciation, amortization, and rent margin', | |
| 'ROIC': 'return on invested capital', | |
| 'EPS': 'earnings per share', | |
| 'P/E': 'price-to-earnings', | |
| 'EBITDA': 'earnings before interest, taxes, depreciation, and amortization', | |
| 'YOY': 'year-over-year', | |
| 'MOM': 'month-over-month', | |
| 'CAGR': 'compound annual growth rate', | |
| 'GDP': 'gross domestic product', | |
| 'ROI': 'return on investment', | |
| 'ROE': 'return on equity', | |
| 'EBIT': 'earnings before interest and taxes', | |
| 'DCF': 'discounted cash flow', | |
| 'GAAP': 'Generally Accepted Accounting Principles', | |
| 'LTM': 'last twelve months', | |
| 'EBIT margin': 'earnings before interest and taxes margin', | |
| 'EBT': 'earnings before taxes', | |
| 'EBTA': 'earnings before taxes and amortization', | |
| 'FTE': 'full-time equivalent', | |
| 'EBIDTA': 'earnings before interest, depreciation, taxes, and amortization', | |
| 'EBTIDA': 'earnings before taxes, interest, depreciation, and amortization', | |
| 'EBITDAR': 'earnings before interest, taxes, depreciation, amortization, and rent', | |
| 'COGS': 'cost of goods sold', | |
| 'APR': 'annual percentage rate', | |
| 'PESTEL': 'Political, Economic, Social, Technological, Environmental, and Legal', | |
| 'KPI': 'key performance indicator', | |
| 'SWOT': 'Strengths, Weaknesses, Opportunities, Threats', | |
| 'CAPEX': 'capital expenditures', | |
| 'EBITDARM': 'earnings before interest, taxes, depreciation, amortization, rent, and management fees', | |
| 'EBITDAX': 'earnings before interest, taxes, depreciation, amortization, and exploration expenses', | |
| 'EBITDAS': 'earnings before interest, taxes, depreciation, amortization, and restructuring costs', | |
| 'EBITDAX-C': 'earnings before interest, taxes, depreciation, amortization, exploration expenses, and commodity derivatives', | |
| 'EBITDAX-R': 'earnings before interest, taxes, depreciation, amortization, exploration expenses, and asset retirement obligations', | |
| 'EBITDAX-E': 'earnings before interest, taxes, depreciation, amortization, exploration expenses, and environmental liabilities' | |
| # Add more abbreviations and replacements as needed | |
| } | |
| for abbreviation, full_form in replacements.items(): | |
| text = text.replace(abbreviation, full_form) | |
| return text | |
| def clean_and_preprocess(text): | |
| text=[x for x in text.split("\n") if len(x)>100] | |
| l=[] | |
| for t in text: | |
| # Convert to lowercase | |
| t = t.lower() | |
| # Tokenize text into words | |
| words = nltk.word_tokenize(t) | |
| # Remove stopwords | |
| stop_words = set(stopwords.words('english')) | |
| filtered_words = [word for word in words if word not in stop_words] | |
| # Join the words back into a cleaned text | |
| cleaned_text = ' '.join(filtered_words) | |
| l.append(cleaned_text) | |
| return "\n".join(l) | |
| def convert_amount_to_number(amount_str): | |
| try: | |
| return w2n.word_to_num(amount_str) | |
| except ValueError: | |
| return 0 # Return 0 if the conversion fails | |
| st.header("Transcript Analysis", divider='rainbow') | |
| transcript = st.text_area("Enter the transcript:", height=100) | |
| if st.button("Analyze"): | |
| transcript=replace_abbreviations(transcript) | |
| transcript=replace_abbreviations(transcript) | |
| transcript=removeSpeakers(transcript) | |
| transcript=removeQA(transcript) | |
| transcript=clean_and_preprocess(transcript) | |
| tokens=transcript.split() | |
| splitSize=256 | |
| chunks=[tokens[r*splitSize:(r+1)*splitSize] for r in range(math.ceil(len(tokens)/splitSize))] | |
| chunks=[" ".join(chuk) for chuk in chunks] | |
| st.subheader("Management Sentiment", divider='rainbow') | |
| sentiment = [sentiment_model(x)[0]['label'] for x in chunks] | |
| sentiment=max(sentiment,key=sentiment.count) | |
| sentiment_color = "green" if sentiment == "postive" else "red" | |
| st.markdown(f'<span style="color:{sentiment_color}">{sentiment}</span>', unsafe_allow_html=True) | |
| st.subheader("Next Quarter Perdiction", divider='rainbow') | |
| increase_decrease = [increase_decrease_model(x)[0]['label'] for x in chunks] | |
| increase_decrease=max(increase_decrease,key=increase_decrease.count) | |
| increase_decrease_color = "green" if increase_decrease == "Increase" else "red" | |
| st.markdown(f'<span style="color:{increase_decrease_color}">{increase_decrease}</span>', unsafe_allow_html=True) | |
| st.subheader("Financial Metrics", divider='rainbow') | |
| ner_result=[] | |
| savedchunks=[] | |
| idx=0 | |
| while idx<len(chunks): | |
| ents=nlpPipe(chunks[idx]) | |
| if len(ents)>=1: | |
| idxx=0 | |
| savedchunks.append(idx) | |
| while idxx<len(ents): | |
| if len(ents[idxx]["word"].split())==2: | |
| ner_result.append({ents[idxx]["entity_group"]:ents[idxx]["word"]}) | |
| else: | |
| ner_result.append({ents[idxx]["entity_group"]:ents[idxx]["word"]+ents[idxx+1]["word"]+ents[idxx+2]["word"]}) | |
| idxx=idxx+2 | |
| idxx=idxx+1 | |
| idx=idx+1 | |
| profits=[x["profit"] for x in ner_result if "profit" in x] | |
| revenues=[x["revenue"] for x in ner_result if "revenue" in x] | |
| expences=[x["expense"] for x in ner_result if "expense" in x] | |
| for idx in range(len(revenues)): | |
| st.text_input(f'Revenue:{idx+1}', revenues[idx]) | |
| for idx in range(len(profits)): | |
| st.text_input(f'Profit:{idx+1}', profits[idx]) | |
| for idx in range(len(expences)): | |
| st.text_input(f'Expences:{idx+1}', expences[idx]) | |
| st.subheader("Parts from transcript that contais financial metrics", divider='rainbow') | |
| for idx in savedchunks: | |
| doc = nlp(chunks[idx]) | |
| entity_list=nlpPipe(chunks[idx]) | |
| entities = [] | |
| for entity in entity_list: | |
| span = doc.char_span(entity['start'], entity['end'], label=entity['entity_group']) | |
| entities.append(span) | |
| try: | |
| doc.ents = entities | |
| ent_html = displacy.render(doc, style="ent", jupyter=False) | |
| st.markdown(ent_html, unsafe_allow_html=True) | |
| except: | |
| pass | |
| st.subheader("Investment Recommendation", divider='rainbow') | |
| profitAmount=sum([convert_amount_to_number(x) for x in profits]) | |
| expencesAmount=sum([convert_amount_to_number(x) for x in expences]) | |
| if increase_decrease=="Increase" and sentiment=="postive" and profitAmount>expencesAmount: | |
| st.markdown(f'<span style="color:green">{"This is a great chance for investment. Do consider it."}</span>', unsafe_allow_html=True) | |
| else: | |
| st.markdown(f'<span style="color:red">{"Not the best chance for investment."}</span>', unsafe_allow_html=True) | |