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 gradio as gr 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 @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") #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 = " <{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("![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)