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import os |
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os.system("pip install gradio==3.0.18") |
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from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification, AutoModelForTokenClassification |
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import gradio as gr |
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import spacy |
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nlp = spacy.load('en_core_web_sm') |
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nlp.add_pipe('sentencizer') |
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def split_in_sentences(text): |
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doc = nlp(text) |
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return [str(sent).strip() for sent in doc.sents] |
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def make_spans(text,results): |
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results_list = [] |
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for i in range(len(results)): |
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results_list.append(results[i]['label']) |
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facts_spans = [] |
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facts_spans = list(zip(split_in_sentences(text),results_list)) |
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return facts_spans |
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fin_model= pipeline("sentiment-analysis", model='FinanceInc/auditor_sentiment_finetuned', tokenizer='FinanceInc/auditor_sentiment_finetuned') |
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def fin_ext(text): |
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results = fin_model(split_in_sentences(text)) |
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return make_spans(text,results) |
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def fls(text): |
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fls_model = pipeline("text-classification", model="FinanceInc/finbert_fls", tokenizer="FinanceInc/finbert_fls") |
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results = fls_model(split_in_sentences(text)) |
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return make_spans(text,results) |
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demo = gr.Blocks() |
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with demo: |
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gr.Markdown("## Financial Analyst AI") |
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gr.Markdown("This project applies AI trained by us to analyze earning calls and other financial documents.") |
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with gr.Row(): |
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with gr.Column(): |
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with gr.Row(): |
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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.") |
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with gr.Row(): |
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b5 = gr.Button("Run Sentiment Analysis and Forward Looking Statement Analysis") |
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with gr.Column(): |
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with gr.Row(): |
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fin_spans = gr.HighlightedText() |
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with gr.Row(): |
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fls_spans = gr.HighlightedText() |
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b5.click(fin_ext, inputs=text, outputs=fin_spans) |
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b5.click(fls, inputs=text, outputs=fls_spans) |
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demo.launch() |
