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app.py

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+import gradio as gr
+from transformers import pipeline
+import io, base64
+from PIL import Image
+import numpy as np
+import tensorflow as tf
+import mediapy
+import os
+import sys
+from huggingface_hub import snapshot_download
+
+import streamlit as st
+import firebase_admin
+from firebase_admin import credentials
+from firebase_admin import firestore
+import datetime
+import tempfile
+from typing import Optional
+import numpy as np
+from TTS.utils.manage import ModelManager
+from TTS.utils.synthesizer import Synthesizer
+             
+
+# firestore singleton is a cached multiuser instance to persist shared crowdsource memory
+@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
+
+#start firestore singleton
+db = get_db_firestore()
+
+# create ASR ML pipeline
+asr = pipeline("automatic-speech-recognition", "facebook/wav2vec2-base-960h")
+
+# create Text Classification pipeline
+classifier = pipeline("text-classification")
+
+# create text generator pipeline
+story_gen = pipeline("text-generation", "pranavpsv/gpt2-genre-story-generator")
+
+# transcribe function
+def transcribe(audio):
+    text = asr(audio)["text"]
+    return text
+
+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:
+        r=(f'{doc.id} => {doc.to_dict()}')
+        doclist += r
+    return doclist 
+
+# story gen
+def generate_story(choice, input_text):
+    query = "<BOS> <{0}> {1}".format(choice, input_text)
+    generated_text = story_gen(query)
+    generated_text = generated_text[0]['generated_text']
+    generated_text = generated_text.split('> ')[2]
+    return generated_text
+    
+# images gen
+def generate_images(text):
+    steps=50
+    width=256
+    height=256
+    num_images=4
+    diversity=6
+    image_bytes = image_gen(text, steps, width, height, num_images, diversity)
+    generated_images = []
+    for image in image_bytes[1]:
+        image_str = image[0]
+        image_str = image_str.replace("data:image/png;base64,","")
+        decoded_bytes = base64.decodebytes(bytes(image_str, "utf-8"))
+        img = Image.open(io.BytesIO(decoded_bytes))
+        generated_images.append(img)
+    return generated_images
+    
+# reductionism - interpolate 4 images - todo - unhardcode the pattern
+def generate_interpolation(gallery):
+    times_to_interpolate = 4
+    generated_images = []
+    for image_str in gallery:
+        image_str = image_str.replace("data:image/png;base64,","")
+        decoded_bytes = base64.decodebytes(bytes(image_str, "utf-8"))
+        img = Image.open(io.BytesIO(decoded_bytes))
+        generated_images.append(img)
+    generated_images[0].save('frame_0.png')
+    generated_images[1].save('frame_1.png')
+    generated_images[2].save('frame_2.png')
+    generated_images[3].save('frame_3.png')
+    input_frames = ["frame_0.png", "frame_1.png", "frame_2.png", "frame_3.png"]
+    frames = list(util.interpolate_recursively_from_files(input_frames, times_to_interpolate, interpolator))
+    mediapy.write_video("out.mp4", frames, fps=15)
+    return "out.mp4"
+
+# image generator
+image_gen = gr.Interface.load("spaces/multimodalart/latentdiffusion")
+
+# video generator
+os.system("git clone https://github.com/google-research/frame-interpolation")
+sys.path.append("frame-interpolation")
+from eval import interpolator, util
+
+ffmpeg_path = util.get_ffmpeg_path()
+mediapy.set_ffmpeg(ffmpeg_path)
+model = snapshot_download(repo_id="akhaliq/frame-interpolation-film-style")
+interpolator = interpolator.Interpolator(model, None)
+
+demo = gr.Blocks()
+with demo:
+
+    audio_file = gr.inputs.Audio(source="microphone", type="filepath")
+    text = gr.Textbox()
+    label = gr.Label()
+    saved = gr.Textbox()
+    savedAll = gr.Textbox()
+    audio = gr.Audio(label="Output", interactive=False)
+    
+    b1 = gr.Button("Recognize Speech")
+    b2 = gr.Button("Classify Sentiment")
+    b3 = gr.Button("Save Speech to Text")
+    b4 = gr.Button("Retrieve All")
+
+    input_story_type = gr.Radio(choices=['superhero', 'action', 'drama', 'horror', 'thriller', 'sci_fi'], value='sci_fi', label="Genre")
+    input_start_text = gr.Textbox(placeholder='A teddy bear outer space', label="Starting Text")
+    
+    gr.Markdown("1. Select a type of story, then write some starting text! Then hit the 'Generate Story' button to generate a story! Feel free to edit the generated story afterwards!")
+    button_gen_story = gr.Button("Generate Story")
+    gr.Markdown("2. After generating a story, hit the 'Generate Images' button to create some visuals for your story! (Can re-run multiple times!)")
+    button_gen_images = gr.Button("Generate Images")
+    gr.Markdown("3. After generating some images, hit the 'Generate Video' button to create a short video by interpolating the previously generated visuals!")
+    button_gen_video = gr.Button("Generate Video")
+    output_generated_story = gr.Textbox(label="Generated Story")
+    output_gallery = gr.Gallery(label="Generated Story Images")
+    output_interpolation = gr.Video(label="Generated Video")
+            
+    # Bind functions to buttons
+    button_gen_story.click(fn=generate_story, inputs=[input_story_type , input_start_text], outputs=output_generated_story)
+    button_gen_images.click(fn=generate_images, inputs=output_generated_story, outputs=output_gallery)
+    button_gen_video.click(fn=generate_interpolation, inputs=output_gallery, outputs=output_interpolation)
+    
+    b1.click(speech_to_text, inputs=audio_file, outputs=input_start_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(debug=True, enable_queue=True)