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| import os | |
| os.system("pip install gradio==3.0.18") | |
| from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification, AutoModelForTokenClassification | |
| import gradio as gr | |
| import spacy | |
| 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) | |
| demo = gr.Blocks() | |
| with 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.Column(): # Main Column | |
| with gr.Row(): | |
| with gr.Column(): # Left Column | |
| audio_file = gr.inputs.Audio(source="microphone", type="filepath") | |
| b1 = gr.Button("Recognize Speech") | |
| text = gr.Textbox(value="") # Move this outside the row | |
| b2 = gr.Button("Summarize Text") | |
| stext = gr.Textbox() | |
| b3 = gr.Button("Classify Financial Tone") | |
| label = gr.Label() | |
| # Reorganize the layout of the buttons and inputs | |
| b1.click(speech_to_text, inputs=audio_file, outputs=text) | |
| b2.click(summarize_text, inputs=text, outputs=stext) | |
| b3.click(text_to_sentiment, inputs=stext, outputs=label) | |
| with gr.Column(): # Right Column | |
| b5 = gr.Button("Financial Tone and Forward Looking Statement Analysis") | |
| fin_spans = gr.HighlightedText() | |
| fls_spans = gr.HighlightedText() | |
| b5.click(fin_ext, inputs=text, outputs=fin_spans) | |
| # b5.click(fls, inputs=text, outputs=fls_spans) | |
| b4 = gr.Button("Identify Companies & Locations") | |
| replaced_spans = gr.HighlightedText() | |
| b4.click(fin_ner, inputs=text, outputs=replaced_spans) | |
| # Place the textbox here, so it's separate from the buttons and inputs | |
| text = gr.Textbox(value="") | |
| demo.launch() | |