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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() |