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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 | |
| import tempfile | |
| from typing import Optional | |
| import numpy as np | |
| from TTS.utils.manage import ModelManager | |
| from TTS.utils.synthesizer import Synthesizer | |
| import io, base64 | |
| import numpy as np | |
| import tensorflow as tf | |
| import mediapy | |
| import os | |
| import sys | |
| import transformers | |
| from transformers import pipeline | |
| from PIL import Image | |
| from huggingface_hub import snapshot_download | |
| # firestore singleton is a cached multiuser instance to persist shared crowdsource memory | |
| 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") | |
| #asr = pipeline("automatic-speech-recognition", "snakers4/silero-models") | |
| # 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: | |
| #docid=doc.id | |
| #dict=doc.to_dict() | |
| #doclist+=doc.to_dict() | |
| r=(f'{doc.id} => {doc.to_dict()}') | |
| doclist += r | |
| return doclist | |
| # 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) | |
| # 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" | |
| 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() | |
| 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("") | |
| # 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) |