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| import streamlit as st | |
| import firebase_admin | |
| from firebase_admin import credentials | |
| from firebase_admin import firestore | |
| import datetime | |
| from transformers import pipeline | |
| import gradio as gr | |
| def get_db_firestore(): | |
| cred = credentials.Certificate('test.json') | |
| firebase_admin.initialize_app(cred, {'projectId': u'clinical-nlp-b9117',}) | |
| db = firestore.client() | |
| return db | |
| def upsertoftheminute(collection, document, firefield, first, last, born): | |
| date_time =str(datetime.datetime.today()).split()[0] | |
| doc_ref = db.collection(collection).document(document) | |
| doc_ref.set({u'firefield': firefield, u'first': first, u'last': last, u'born': date_time,}) | |
| def selectCollectionDocument(collection, document): | |
| doc_ref = db.collection(collection).document(document) | |
| doc = doc_ref.get() | |
| st.write("The id is: ", doc.id) | |
| st.write("The contents are: ", doc.to_dict()) | |
| db = get_db_firestore() | |
| upsertoftheminute(u'TimeSeries', u'DocumentofMinute', u'TestUser1', u'🧠🌳Yggdrasil🌳🧠', u'https://huggingface.co/spaces/awacke1/FirestorePersistence', 2022) | |
| selectCollectionDocument(u"TimeSeries", u"DocumentofMinute") | |
| asr = pipeline("automatic-speech-recognition", "facebook/wav2vec2-base-960h") | |
| classifier = pipeline("text-classification") | |
| def speech_to_text(speech): | |
| text = asr(speech)["text"] | |
| upsertoftheminute(u'TimeSeries', u'DocumentofMinuteText', u'TestUser1', u'🧠🌳Yggdrasil🌳🧠', text, 2022) | |
| return text | |
| def text_to_sentiment(text): | |
| sentiment = classifier(text)[0]["label"] | |
| upsertoftheminute(u'TimeSeries', u'DocumentofMinuteSentiment', u'TestUser1', u'🧠🌳Yggdrasil🌳🧠', sentiment, 2022) | |
| return sentiment | |
| demo = gr.Blocks() | |
| with demo: | |
| audio_file = gr.Audio(type="filepath") | |
| text = gr.Textbox() | |
| label = gr.Label() | |
| b1 = gr.Button("Recognize Speech") | |
| b2 = gr.Button("Classify Sentiment") | |
| b1.click(speech_to_text, inputs=audio_file, outputs=text) | |
| b2.click(text_to_sentiment, inputs=text, outputs=label) | |
| demo.launch() |