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from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification
import gradio as gr
import os
asr = pipeline("automatic-speech-recognition", "facebook/wav2vec2-base-960h")
summarizer = pipeline("summarization", model="knkarthick/MEETING_SUMMARY")
auth_token = os.environ.get("HF_Token")
tokenizer = AutoTokenizer.from_pretrained("demo-org/auditor_review_model",use_auth_token=auth_token)
audit_model = AutoModelForSequenceClassification.from_pretrained("demo-org/auditor_review_model",use_auth_token=auth_token)
nlp = pipeline("text-classification", model=audit_model, tokenizer=tokenizer)
def transcribe(audio):
text = asr(audio)["text"]
return text
def speech_to_text(speech):
text = asr(speech)["text"]
return text
def summarize_text(text):
stext = summarizer(text)
return stext
def text_to_sentiment(text):
sentiment = nlp(text)[0]["label"]
return sentiment
def ner(text):
api = gr.Interface.load("dslim/bert-base-NER", src='models')
print (api)
spans = api(text)
print (spans)
replaced_spans = [(key, None) if value=='No Disease' else (key, value) for (key, value) in spans]
return replaced_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 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(ner, inputs=text, outputs=replaced_spans)
demo.launch(share=True) |