Create app.py

app.py

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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
+
+@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
+
+
+db = get_db_firestore()
+asr = pipeline("automatic-speech-recognition", "facebook/wav2vec2-base-960h")
+
+def transcribe(audio):
+    text = asr(audio)["text"]
+    return text
+
+classifier = pipeline("text-classification")
+
+def speech_to_text(speech):
+    text = asr(speech)["text"]
+    return text
+
+def text_to_sentiment(text):
+    sentiment = classifier(text)[0]["label"]
+    return sentiment 
+
+def upsert(text):
+    date_time =str(datetime.datetime.today())
+    doc_ref = db.collection('Text2SpeechSentimentSave').document(date_time)
+    doc_ref.set({u'firefield': 'Recognize Speech', u'first': 'https://huggingface.co/spaces/awacke1/Text2SpeechSentimentSave', u'last': text, u'born': date_time,})
+    saved = select('Text2SpeechSentimentSave', date_time)
+    # check it here:  https://console.firebase.google.com/u/0/project/clinical-nlp-b9117/firestore/data/~2FStreamlitSpaces
+    return saved
+      
+def select(collection, document):
+    doc_ref = db.collection(collection).document(document)
+    doc = doc_ref.get()
+    docid = ("The id is: ", doc.id)
+    contents = ("The contents are: ", doc.to_dict())
+    return contents
+          
+def selectall(text):
+    docs = db.collection('Text2SpeechSentimentSave').stream()
+    doclist=''
+    for doc in docs:
+        #docid=doc.id
+        #dict=doc.to_dict()
+        #doclist+=doc.to_dict()
+        r=(f'{doc.id} => {doc.to_dict()}')
+        doclist += r
+    return doclist 
+    
+demo = gr.Blocks()
+
+with demo:
+    #audio_file = gr.Audio(type="filepath")
+    audio_file = gr.inputs.Audio(source="microphone", type="filepath")
+    text = gr.Textbox()
+    label = gr.Label()
+    saved = gr.Textbox()
+    savedAll = gr.Textbox()
+    
+    b1 = gr.Button("Recognize Speech")
+    b2 = gr.Button("Classify Sentiment")
+    b3 = gr.Button("Save Speech to Text")
+    b4 = gr.Button("Retrieve All")
+
+    b1.click(speech_to_text, inputs=audio_file, outputs=text)
+    b2.click(text_to_sentiment, inputs=text, outputs=label)
+    b3.click(upsert, inputs=text, outputs=saved)
+    b4.click(selectall, inputs=text, outputs=savedAll)
+    
+demo.launch(share=True)