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| from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification, AutoModelForTokenClassification | |
| import gradio as gr | |
| import os | |
| import spacy | |
| nlp = spacy.load('en_core_web_sm') | |
| 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): | |
| stext = summarizer(text) | |
| return stext | |
| ##Fiscal Sentiment | |
| fin_model = pipeline("text-classification", model="demo-org/auditor_review_model", \ | |
| tokenizer="demo-org/auditor_review_model",use_auth_token=auth_token) | |
| def text_to_sentiment(text): | |
| sentiment = fin_model(text)[0]["label"] | |
| return sentiment | |
| ##Company Extraction | |
| def fin_ner(text): | |
| print ("ner") | |
| #ner_pipeline = pipeline("ner", model="dslim/bert-base-NER", tokenizer="dslim/bert-base-NER") | |
| api = gr.Interface.load("dslim/bert-base-NER", src='models') | |
| replaced_spans = api(text) | |
| print (replaced_spans) | |
| print ("spans2") | |
| #replaced_spans = [(key, None) if value=='No Disease' else (key, value) for (key, value) in spans] | |
| return replaced_spans | |
| ##Fiscal Sentiment by Sentence | |
| def fin_ext(text): | |
| doc = nlp(text) | |
| doc_sents = [sent for sent in doc.sents] | |
| sents_list = [] | |
| for sent in doc.sents: | |
| sents_list.append(sent.text) | |
| results = fin_model(sents_list) | |
| results_list = [] | |
| for i in range(len(results)): | |
| results_list.append(results[i]['label']) | |
| fin_spans = [] | |
| fin_spans = list(zip(sents_list,results_list)) | |
| return fin_spans | |
| demo = gr.Blocks() | |
| with demo: | |
| audio_file = gr.inputs.Audio(source="microphone", type="filepath") | |
| b1 = gr.Button("Recognize Speech") | |
| text = gr.Textbox() | |
| b1.click(speech_to_text, inputs=audio_file, outputs=text) | |
| b2 = gr.Button("Summarize Text") | |
| stext = gr.Textbox() | |
| b2.click(summarize_text, inputs=text, outputs=stext) | |
| b3 = gr.Button("Classify Overall Financial Sentiment") | |
| label = gr.Label() | |
| b3.click(text_to_sentiment, inputs=stext, outputs=label) | |
| b4 = gr.Button("Extract Companies & Segments") | |
| replaced_spans = gr.HighlightedText() | |
| b4.click(fin_ner, inputs=text, outputs=replaced_spans) | |
| b5 = gr.Button("Extract Financial Sentiment") | |
| fin_spans = gr.HighlightedText() | |
| b5.click(fin_ext, inputs=text, outputs=fin_spans) | |
| demo.launch() |