Update app.py

app.py

@@ -1,16 +1,48 @@
+import streamlit as st
+import firebase_admin
+from firebase_admin import credentials
+from firebase_admin import firestore
+from datetime import datetime
 from transformers import pipeline
-
 import gradio as gr
 
+@st.experimental_singleton
+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 = now.strftime("%m/%d/%Y, %H:%M")
+    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):
-    return classifier(text)[0]["label"]
+    sentiment = classifier(text)[0]["label"]
+    upsertoftheminute(u'TimeSeries', u'DocumentofMinuteSentiment', u'TestUser1', u'🧠🌳Yggdrasil🌳🧠', sentiment, 2022)
+    return sentiment 
 
 demo = gr.Blocks()