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import streamlit as st |
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import firebase_admin |
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from firebase_admin import credentials |
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from firebase_admin import firestore |
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import datetime |
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from transformers import pipeline |
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import gradio as gr |
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@st.experimental_singleton |
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def get_db_firestore(): |
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cred = credentials.Certificate('test.json') |
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firebase_admin.initialize_app(cred, {'projectId': u'clinical-nlp-b9117',}) |
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db = firestore.client() |
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return db |
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db = get_db_firestore() |
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asr = pipeline("automatic-speech-recognition", "facebook/wav2vec2-base-960h") |
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def transcribe(audio): |
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text = asr(audio)["text"] |
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return text |
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classifier = pipeline("text-classification") |
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def speech_to_text(speech): |
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text = asr(speech)["text"] |
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return text |
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def text_to_sentiment(text): |
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sentiment = classifier(text)[0]["label"] |
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return sentiment |
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def upsert(text): |
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date_time =str(datetime.datetime.today()) |
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doc_ref = db.collection('Text2SpeechSentimentSave').document(date_time) |
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doc_ref.set({u'firefield': 'Recognize Speech', u'first': 'https://huggingface.co/spaces/awacke1/Text2SpeechSentimentSave', u'last': text, u'born': date_time,}) |
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saved = select('Text2SpeechSentimentSave', date_time) |
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return saved |
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def select(collection, document): |
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doc_ref = db.collection(collection).document(document) |
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doc = doc_ref.get() |
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docid = ("The id is: ", doc.id) |
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contents = ("The contents are: ", doc.to_dict()) |
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return contents |
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def selectall(text): |
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docs = db.collection('Text2SpeechSentimentSave').stream() |
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doclist='' |
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for doc in docs: |
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r=(f'{doc.id} => {doc.to_dict()}') |
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doclist += r |
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return doclist |
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demo = gr.Blocks() |
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with demo: |
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audio_file = gr.inputs.Audio(source="microphone", type="filepath") |
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text = gr.Textbox() |
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label = gr.Label() |
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saved = gr.Textbox() |
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savedAll = gr.Textbox() |
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b1 = gr.Button("Recognize Speech") |
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b2 = gr.Button("Classify Sentiment") |
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b3 = gr.Button("Save Speech to Text") |
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b4 = gr.Button("Retrieve All") |
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b1.click(speech_to_text, inputs=audio_file, outputs=text) |
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b2.click(text_to_sentiment, inputs=text, outputs=label) |
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b3.click(upsert, inputs=text, outputs=saved) |
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b4.click(selectall, inputs=text, outputs=savedAll) |
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demo.launch(share=True) |
