import gradio as gr import io, base64 import numpy as np import tensorflow as tf import mediapy import os import sys import streamlit as st import firebase_admin import datetime import transformers from transformers import pipeline from PIL import Image from huggingface_hub import snapshot_download from firebase_admin import credentials from firebase_admin import firestore #import os #os.system("pip install gradio==2.7.5.2") #import torch #import zipfile #import torchaudio #from glob import glob #import gradio as gr # 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)