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app.py
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import gradio as gr
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from transformers import pipeline
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import io, base64
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from PIL import Image
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import numpy as np
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import tensorflow as tf
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import mediapy
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import os
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import sys
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from huggingface_hub import snapshot_download
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import streamlit as st
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import firebase_admin
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from firebase_admin import credentials
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from firebase_admin import firestore
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import datetime
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import tempfile
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from typing import Optional
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import numpy as np
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from TTS.utils.manage import ModelManager
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from TTS.utils.synthesizer import Synthesizer
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# firestore singleton is a cached multiuser instance to persist shared crowdsource memory
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@st.experimental_singleton
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def get_db_firestore():
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cred = credentials.Certificate('test.json')
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firebase_admin.initialize_app(cred, {'projectId': u'clinical-nlp-b9117',})
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db = firestore.client()
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return db
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#start firestore singleton
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db = get_db_firestore()
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# create ASR ML pipeline
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asr = pipeline("automatic-speech-recognition", "facebook/wav2vec2-base-960h")
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# create Text Classification pipeline
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classifier = pipeline("text-classification")
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# create text generator pipeline
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story_gen = pipeline("text-generation", "pranavpsv/gpt2-genre-story-generator")
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# transcribe function
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def transcribe(audio):
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text = asr(audio)["text"]
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return text
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def speech_to_text(speech):
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text = asr(speech)["text"]
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return text
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def text_to_sentiment(text):
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sentiment = classifier(text)[0]["label"]
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return sentiment
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def upsert(text):
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date_time =str(datetime.datetime.today())
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doc_ref = db.collection('Text2SpeechSentimentSave').document(date_time)
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doc_ref.set({u'firefield': 'Recognize Speech', u'first': 'https://huggingface.co/spaces/awacke1/Text2SpeechSentimentSave', u'last': text, u'born': date_time,})
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saved = select('Text2SpeechSentimentSave', date_time)
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# check it here: https://console.firebase.google.com/u/0/project/clinical-nlp-b9117/firestore/data/~2FStreamlitSpaces
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return saved
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def select(collection, document):
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doc_ref = db.collection(collection).document(document)
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doc = doc_ref.get()
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docid = ("The id is: ", doc.id)
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contents = ("The contents are: ", doc.to_dict())
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return contents
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def selectall(text):
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docs = db.collection('Text2SpeechSentimentSave').stream()
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doclist=''
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for doc in docs:
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r=(f'{doc.id} => {doc.to_dict()}')
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doclist += r
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return doclist
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# story gen
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def generate_story(choice, input_text):
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query = "<BOS> <{0}> {1}".format(choice, input_text)
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generated_text = story_gen(query)
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generated_text = generated_text[0]['generated_text']
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generated_text = generated_text.split('> ')[2]
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return generated_text
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# images gen
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def generate_images(text):
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steps=50
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width=256
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height=256
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num_images=4
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diversity=6
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image_bytes = image_gen(text, steps, width, height, num_images, diversity)
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generated_images = []
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for image in image_bytes[1]:
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image_str = image[0]
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image_str = image_str.replace("data:image/png;base64,","")
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decoded_bytes = base64.decodebytes(bytes(image_str, "utf-8"))
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img = Image.open(io.BytesIO(decoded_bytes))
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generated_images.append(img)
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return generated_images
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# reductionism - interpolate 4 images - todo - unhardcode the pattern
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def generate_interpolation(gallery):
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times_to_interpolate = 4
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generated_images = []
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for image_str in gallery:
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image_str = image_str.replace("data:image/png;base64,","")
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decoded_bytes = base64.decodebytes(bytes(image_str, "utf-8"))
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img = Image.open(io.BytesIO(decoded_bytes))
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generated_images.append(img)
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generated_images[0].save('frame_0.png')
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generated_images[1].save('frame_1.png')
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generated_images[2].save('frame_2.png')
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generated_images[3].save('frame_3.png')
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input_frames = ["frame_0.png", "frame_1.png", "frame_2.png", "frame_3.png"]
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frames = list(util.interpolate_recursively_from_files(input_frames, times_to_interpolate, interpolator))
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mediapy.write_video("out.mp4", frames, fps=15)
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return "out.mp4"
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# image generator
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image_gen = gr.Interface.load("spaces/multimodalart/latentdiffusion")
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# video generator
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os.system("git clone https://github.com/google-research/frame-interpolation")
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sys.path.append("frame-interpolation")
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from eval import interpolator, util
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ffmpeg_path = util.get_ffmpeg_path()
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mediapy.set_ffmpeg(ffmpeg_path)
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model = snapshot_download(repo_id="akhaliq/frame-interpolation-film-style")
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interpolator = interpolator.Interpolator(model, None)
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demo = gr.Blocks()
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with demo:
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audio_file = gr.inputs.Audio(source="microphone", type="filepath")
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text = gr.Textbox()
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label = gr.Label()
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saved = gr.Textbox()
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savedAll = gr.Textbox()
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audio = gr.Audio(label="Output", interactive=False)
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b1 = gr.Button("Recognize Speech")
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b2 = gr.Button("Classify Sentiment")
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b3 = gr.Button("Save Speech to Text")
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b4 = gr.Button("Retrieve All")
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input_story_type = gr.Radio(choices=['superhero', 'action', 'drama', 'horror', 'thriller', 'sci_fi'], value='sci_fi', label="Genre")
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input_start_text = gr.Textbox(placeholder='A teddy bear outer space', label="Starting Text")
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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!")
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button_gen_story = gr.Button("Generate Story")
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gr.Markdown("2. After generating a story, hit the 'Generate Images' button to create some visuals for your story! (Can re-run multiple times!)")
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button_gen_images = gr.Button("Generate Images")
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gr.Markdown("3. After generating some images, hit the 'Generate Video' button to create a short video by interpolating the previously generated visuals!")
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button_gen_video = gr.Button("Generate Video")
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output_generated_story = gr.Textbox(label="Generated Story")
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output_gallery = gr.Gallery(label="Generated Story Images")
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output_interpolation = gr.Video(label="Generated Video")
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# Bind functions to buttons
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button_gen_story.click(fn=generate_story, inputs=[input_story_type , input_start_text], outputs=output_generated_story)
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button_gen_images.click(fn=generate_images, inputs=output_generated_story, outputs=output_gallery)
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button_gen_video.click(fn=generate_interpolation, inputs=output_gallery, outputs=output_interpolation)
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b1.click(speech_to_text, inputs=audio_file, outputs=input_start_text )
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b2.click(text_to_sentiment, inputs=text, outputs=label)
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b3.click(upsert, inputs=text, outputs=saved)
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b4.click(selectall, inputs=text, outputs=savedAll)
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demo.launch(debug=True, enable_queue=True)
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