Update app.py

app.py

@@ -1,27 +1,31 @@
 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
-
+import numpy as np
 import tempfile
+
+from firebase_admin import credentials
+from firebase_admin import firestore
+from transformers import pipeline
 from typing import Optional
-import numpy as np
 from TTS.utils.manage import ModelManager
 from TTS.utils.synthesizer import Synthesizer
+from gradio import inputs
+from gradio.inputs import Textbox
+from gradio import outputs
 
+#Persistence via Cloud Store
 @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")
 
+#STT Models
 MODEL_NAMES = [
     "en/ljspeech/tacotron2-DDC",
     "en/ljspeech/glow-tts",
@@ -47,20 +51,37 @@ for MODEL_NAME in MODEL_NAMES:
     )
     MODELS[MODEL_NAME] = synthesizer
 
+# Generators
+#generator2 = gr.Interface.load("huggingface/EleutherAI/gpt-neo-2.7B")
+#generator3 = gr.Interface.load("huggingface/EleutherAI/gpt-j-6B")
+#generator1 = gr.Interface.load("huggingface/gpt2-large")
+
+
+GEN_NAMES = [
+    "huggingface/EleutherAI/gpt-neo-2.7B",
+    "huggingface/EleutherAI/gpt-j-6B",
+    "huggingface/gpt2-large",
+]
+
+#ASR
 def transcribe(audio):
     text = asr(audio)["text"]
     return text
 
+#Sentiment Classifier
 classifier = pipeline("text-classification")
 
+#STT
 def speech_to_text(speech):
     text = asr(speech)["text"]
     return text
-
+    
+#TTSentiment
 def text_to_sentiment(text):
     sentiment = classifier(text)[0]["label"]
     return sentiment 
 
+#Save
 def upsert(text):
     date_time =str(datetime.datetime.today())
     doc_ref = db.collection('Text2SpeechSentimentSave').document(date_time)
@@ -69,13 +90,15 @@ def upsert(text):
     # check it here:  https://console.firebase.google.com/u/0/project/clinical-nlp-b9117/firestore/data/~2FStreamlitSpaces
     return saved
       
+#OpenLast
 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
-          
+
+#OpenAll   
 def selectall(text):
     docs = db.collection('Text2SpeechSentimentSave').stream()
     doclist=''
@@ -83,7 +106,8 @@ def selectall(text):
         r=(f'{doc.id} => {doc.to_dict()}')
         doclist += r
     return doclist 
-    
+
+#TTS
 def tts(text: str, model_name: str):
     print(text, model_name)
     synthesizer = MODELS.get(model_name, None)
@@ -94,26 +118,57 @@ def tts(text: str, model_name: str):
         synthesizer.save_wav(wavs, fp)
         return fp.name
 
+#Stories
+def Story(text: input):
+    return 
+
+#Blocks Rock It
 demo = gr.Blocks()
 with demo:
+    #UI
     audio_file = gr.inputs.Audio(source="microphone", type="filepath")
     text = gr.Textbox()
     label = gr.Label()
     saved = gr.Textbox()
     savedAll = gr.Textbox()
     TTSchoice = gr.inputs.Radio( label="Pick a TTS Model", choices=MODEL_NAMES,   )
+    Storychoice = gr.inputs.Radio( label="Pick a Story Generator", choices=GEN_NAMES,   )
     audio = gr.Audio(label="Output", interactive=False)
     
+    
+    #Buttons
     b1 = gr.Button("Recognize Speech")
     b2 = gr.Button("Classify Sentiment")
     b3 = gr.Button("Save Speech to Text")
     b4 = gr.Button("Retrieve All")
     b5 = gr.Button("Read It Back Aloud")
+    b6 = gr.Button("Generate a Story")
 
+    #Event Model Chains
     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)
     b5.click(tts,  inputs=[text,TTSchoice], outputs=audio)
-    
-demo.launch(share=True)
+    b6.click(tts,  inputs=[text,Storychoice], outputs=text)
+
+# Lets Do It
+demo.launch(share=True)
+
+title = "Story Generators"
+
+examples = [
+    ["At which point do we invent Love?"],
+    ["Love is a capacity more than consciousness is universal."],
+    ["See the grace of god in eachother."],
+    ["Love is a capacity more than consciousness is universal."],
+    ["Love is generativity when there is more energy than what they need for equilibrium."],
+    ["Collections of people have agency and mass having agency at the mesoscopic level"],
+    ["Having a deep human connection is an interface problem to solve."],
+    ["Having a collective creates agency since we build trust in eachother."]
+]
+
+#gr.Parallel(generator1, generator2, generator3, inputs=gr.inputs.Textbox(lines=5, label="Enter a sentence to get another sentence."),
+#            title=title, examples=examples).launch(share=False)
+            
+