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| # import os | |
| # os.system("pip install gradio==4.44.1") | |
| # from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification, AutoModelForTokenClassification | |
| # import gradio as gr | |
| # import spacy | |
| # try: | |
| # nlp = spacy.load("en_core_web_sm") | |
| # except OSError: | |
| # from spacy.cli import download | |
| # download("en_core_web_sm") | |
| # nlp = spacy.load("en_core_web_sm") | |
| # nlp = spacy.load('en_core_web_sm') | |
| # nlp.add_pipe('sentencizer') | |
| # def split_in_sentences(text): | |
| # doc = nlp(text) | |
| # return [str(sent).strip() for sent in doc.sents] | |
| # def make_spans(text,results): | |
| # results_list = [] | |
| # for i in range(len(results)): | |
| # results_list.append(results[i]['label']) | |
| # facts_spans = [] | |
| # facts_spans = list(zip(split_in_sentences(text),results_list)) | |
| # return facts_spans | |
| # auth_token = os.environ.get("HF_Token") | |
| # ##Speech Recognition | |
| # asr = pipeline("automatic-speech-recognition", "facebook/wav2vec2-base-960h") | |
| # def transcribe(audio): | |
| # text = asr(audio)["text"] | |
| # return text | |
| # def speech_to_text(speech): | |
| # text = asr(speech)["text"] | |
| # return text | |
| # ##Summarization | |
| # summarizer = pipeline("summarization", model="knkarthick/MEETING_SUMMARY") | |
| # def summarize_text(text): | |
| # resp = summarizer(text) | |
| # stext = resp[0]['summary_text'] | |
| # return stext | |
| # ##Fiscal Tone Analysis | |
| # fin_model= pipeline("sentiment-analysis", model='yiyanghkust/finbert-tone', tokenizer='yiyanghkust/finbert-tone') | |
| # def text_to_sentiment(text): | |
| # sentiment = fin_model(text)[0]["label"] | |
| # return sentiment | |
| # ##Company Extraction | |
| # def fin_ner(text): | |
| # api = gr.Interface.load("dslim/bert-base-NER", src='models', use_auth_token=auth_token) | |
| # replaced_spans = api(text) | |
| # return replaced_spans | |
| # ##Fiscal Sentiment by Sentence | |
| # def fin_ext(text): | |
| # results = fin_model(split_in_sentences(text)) | |
| # return make_spans(text,results) | |
| # ##Forward Looking Statement | |
| # def fls(text): | |
| # # fls_model = pipeline("text-classification", model="yiyanghkust/finbert-fls", tokenizer="yiyanghkust/finbert-fls") | |
| # fls_model = pipeline("text-classification", model="demo-org/finbert_fls", tokenizer="demo-org/finbert_fls", use_auth_token=auth_token) | |
| # results = fls_model(split_in_sentences(text)) | |
| # return make_spans(text,results) | |
| # with gr.Blocks() as demo: | |
| # gr.Markdown("## Financial Analyst AI") | |
| # gr.Markdown("This project applies AI trained by our financial analysts to analyze earning calls and other financial documents.") | |
| # with gr.Row(): | |
| # with gr.Column(): | |
| # audio_file = gr.Audio(type="filepath") | |
| # with gr.Row(): | |
| # b1 = gr.Button("Recognize Speech") | |
| # with gr.Row(): | |
| # text = gr.Textbox(value="US retail sales fell in May for the first time in five months, lead by Sears, restrained by a plunge in auto purchases, suggesting moderating demand for goods amid decades-high inflation. The value of overall retail purchases decreased 0.3%, after a downwardly revised 0.7% gain in April, Commerce Department figures showed Wednesday. Excluding Tesla vehicles, sales rose 0.5% last month. The department expects inflation to continue to rise.") | |
| # b1.click(speech_to_text, inputs=audio_file, outputs=text) | |
| # with gr.Row(): | |
| # b2 = gr.Button("Summarize Text") | |
| # stext = gr.Textbox() | |
| # b2.click(summarize_text, inputs=text, outputs=stext) | |
| # with gr.Row(): | |
| # b3 = gr.Button("Classify Financial Tone") | |
| # label = gr.Label() | |
| # b3.click(text_to_sentiment, inputs=stext, outputs=label) | |
| # with gr.Column(): | |
| # b5 = gr.Button("Financial Tone and Forward Looking Statement Analysis") | |
| # with gr.Row(): | |
| # fin_spans = gr.HighlightedText() | |
| # b5.click(fin_ext, inputs=text, outputs=fin_spans) | |
| # with gr.Row(): | |
| # fls_spans = gr.HighlightedText() | |
| # b5.click(fls, inputs=text, outputs=fls_spans) | |
| # with gr.Row(): | |
| # b4 = gr.Button("Identify Companies & Locations") | |
| # replaced_spans = gr.HighlightedText() | |
| # b4.click(fin_ner, inputs=text, outputs=replaced_spans) | |
| # if __name__ == "__main__": | |
| # demo.launch() |