app.py

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


# load cloud firestore client which establishes a connection to dataset where we persist data
@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 it up
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()


    
#demo.launch(share=True)


# 1. GPT-J: Story Generation Pipeline
story_gen = pipeline("text-generation", "pranavpsv/gpt2-genre-story-generator")

# 2. LatentDiffusion: Latent Diffusion Interface
image_gen = gr.Interface.load("spaces/multimodalart/latentdiffusion")

# 3. FILM: Frame Interpolation Model (code re-use from spaces/akhaliq/frame-interpolation/tree/main)
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)

def generate_story(choice, input_text):
    query = "<BOS> <{0}> {1}".format(choice, input_text)
    
    print(query)
    generated_text = story_gen(query)
    generated_text = generated_text[0]['generated_text']
    generated_text = generated_text.split('> ')[2]
    
    return generated_text
    
def generate_images(generated_text):
    steps=50
    width=256
    height=256
    num_images=4
    diversity=6
    image_bytes = image_gen(generated_text, steps, width, height, num_images, diversity)
    
    # Algo from spaces/Gradio-Blocks/latent_gpt2_story/blob/main/app.py
    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
    
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"
    
    

demo = gr.Blocks()

with demo:

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)
    
    with gr.Row():
    
        # Left column (inputs)
        with gr.Column():
            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("Be sure to run each of the buttons one at a time, they depend on each others' outputs!")
            
            # Rows of instructions & buttons
            with gr.Row():
                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")
            with gr.Row():
                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")
            with gr.Row():
                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")
                
            # Rows of references
            with gr.Row():
                gr.Markdown("--Models Used--")
            with gr.Row():
                gr.Markdown("Story Generation: [GPT-J](https://huggingface.co./pranavpsv/gpt2-genre-story-generator)")
            with gr.Row():
                gr.Markdown("Image Generation Conditioned on Text: [Latent Diffusion](https://huggingface.co./spaces/multimodalart/latentdiffusion) | [Github Repo](https://github.com/CompVis/latent-diffusion)")
            with gr.Row():
                gr.Markdown("Interpolations: [FILM](https://huggingface.co./spaces/akhaliq/frame-interpolation) | [Github Repo](https://github.com/google-research/frame-interpolation)")
            with gr.Row():
                gr.Markdown("![visitor badge](https://visitor-badge.glitch.me/badge?page_id=gradio-blocks_story_and_video_generation)")
                
        # Right column (outputs)
        with gr.Column():
            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)

demo.launch(debug=True, enable_queue=True)