app.py

## **** below codelines are borrowed from multimodalart space
from pydoc import describe
import gradio as gr
import torch
from omegaconf import OmegaConf
import sys 
sys.path.append(".")
sys.path.append('./taming-transformers')
#sys.path.append('./latent-diffusion')
from taming.models import vqgan 
from util import instantiate_from_config
from huggingface_hub import hf_hub_download

model_path_e = hf_hub_download(repo_id="multimodalart/compvis-latent-diffusion-text2img-large", filename="txt2img-f8-large.ckpt")

#@title Import stuff
import argparse, os, sys, glob
import numpy as np
from PIL import Image
from einops import rearrange
from torchvision.utils import make_grid
import transformers
import gc
from util import instantiate_from_config
from ddim import DDIMSampler
from plms import PLMSSampler
from open_clip import tokenizer
import open_clip

def load_model_from_config(config, ckpt, verbose=False):
    print(f"Loading model from {ckpt}")
    pl_sd = torch.load(ckpt, map_location="cuda")
    #please use torch.load with map_location=torch.device('cpu') to map your storages to the CPU.
    pl_sd = torch.load(ckpt, map_location=torch.device('cpu'))
    sd = pl_sd["state_dict"]
    model = instantiate_from_config(config.model)
    m, u = model.load_state_dict(sd, strict=False)
    if len(m) > 0 and verbose:
        print("missing keys:")
        print(m)
    if len(u) > 0 and verbose:
        print("unexpected keys:")
        print(u)

    model = model.half().cuda()
    model.eval()
    return model

def load_safety_model(clip_model):
    """load the safety model"""
    import autokeras as ak  # pylint: disable=import-outside-toplevel
    from tensorflow.keras.models import load_model  # pylint: disable=import-outside-toplevel
    from os.path import expanduser  # pylint: disable=import-outside-toplevel

    home = expanduser("~")

    cache_folder = home + "/.cache/clip_retrieval/" + clip_model.replace("/", "_")
    if clip_model == "ViT-L/14":
        model_dir = cache_folder + "/clip_autokeras_binary_nsfw"
        dim = 768
    elif clip_model == "ViT-B/32":
        model_dir = cache_folder + "/clip_autokeras_nsfw_b32"
        dim = 512
    else:
        raise ValueError("Unknown clip model")
    if not os.path.exists(model_dir):
        os.makedirs(cache_folder, exist_ok=True)

        from urllib.request import urlretrieve  # pylint: disable=import-outside-toplevel

        path_to_zip_file = cache_folder + "/clip_autokeras_binary_nsfw.zip"
        if clip_model == "ViT-L/14":
            url_model = "https://raw.githubusercontent.com/LAION-AI/CLIP-based-NSFW-Detector/main/clip_autokeras_binary_nsfw.zip"
        elif clip_model == "ViT-B/32":
            url_model = (
                "https://raw.githubusercontent.com/LAION-AI/CLIP-based-NSFW-Detector/main/clip_autokeras_nsfw_b32.zip"
            )
        else:
            raise ValueError("Unknown model {}".format(clip_model))
        urlretrieve(url_model, path_to_zip_file)
        import zipfile  # pylint: disable=import-outside-toplevel

        with zipfile.ZipFile(path_to_zip_file, "r") as zip_ref:
            zip_ref.extractall(cache_folder)

    loaded_model = load_model(model_dir, custom_objects=ak.CUSTOM_OBJECTS)
    loaded_model.predict(np.random.rand(10 ** 3, dim).astype("float32"), batch_size=10 ** 3)

    return loaded_model
    
def is_unsafe(safety_model, embeddings, threshold=0.5):
    """find unsafe embeddings"""
    nsfw_values = safety_model.predict(embeddings, batch_size=embeddings.shape[0])
    x = np.array([e[0] for e in nsfw_values])
    return True if x > threshold else False

config = OmegaConf.load("./txt2img-1p4B-eval.yaml")
model = load_model_from_config(config,model_path_e)
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
model = model.to(device)

#NSFW CLIP Filter
safety_model = load_safety_model("ViT-B/32")
clip_model, _, preprocess = open_clip.create_model_and_transforms('ViT-B-32', pretrained='openai')


def run(prompt, steps, width, height, images, scale):
    opt = argparse.Namespace(
        prompt = prompt, 
        outdir='./outputs',
        ddim_steps = int(steps),
        ddim_eta = 0,
        n_iter = 1,
        W=int(width),
        H=int(height),
        n_samples=int(images),
        scale=scale,
        plms=True
    )

    if opt.plms:
        opt.ddim_eta = 0
        sampler = PLMSSampler(model)
    else:
        sampler = DDIMSampler(model)
    
    os.makedirs(opt.outdir, exist_ok=True)
    outpath = opt.outdir

    prompt = opt.prompt


    sample_path = os.path.join(outpath, "samples")
    os.makedirs(sample_path, exist_ok=True)
    base_count = len(os.listdir(sample_path))

    all_samples=list()
    all_samples_images=list()
    with torch.no_grad():
        with torch.cuda.amp.autocast():
            with model.ema_scope():
                uc = None
                if opt.scale > 0:
                    uc = model.get_learned_conditioning(opt.n_samples * [""])
                for n in range(opt.n_iter):
                    c = model.get_learned_conditioning(opt.n_samples * [prompt])
                    shape = [4, opt.H//8, opt.W//8]
                    samples_ddim, _ = sampler.sample(S=opt.ddim_steps,
                                                    conditioning=c,
                                                    batch_size=opt.n_samples,
                                                    shape=shape,
                                                    verbose=False,
                                                    unconditional_guidance_scale=opt.scale,
                                                    unconditional_conditioning=uc,
                                                    eta=opt.ddim_eta)

                    x_samples_ddim = model.decode_first_stage(samples_ddim)
                    x_samples_ddim = torch.clamp((x_samples_ddim+1.0)/2.0, min=0.0, max=1.0)

                    for x_sample in x_samples_ddim:
                        x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
                        image_vector = Image.fromarray(x_sample.astype(np.uint8))
                        image_preprocess = preprocess(image_vector).unsqueeze(0)
                        with torch.no_grad():
                          image_features = clip_model.encode_image(image_preprocess)
                        image_features /= image_features.norm(dim=-1, keepdim=True)
                        query = image_features.cpu().detach().numpy().astype("float32")
                        unsafe = is_unsafe(safety_model,query,0.5)
                        if(not unsafe):
                            all_samples_images.append(image_vector)
                        else:
                            return(None,None,"Sorry, potential NSFW content was detected on your outputs by our NSFW detection model. Try again with different prompts. If you feel your prompt was not supposed to give NSFW outputs, this may be due to a bias in the model. Read more about biases in the Biases Acknowledgment section below.")
                        #Image.fromarray(x_sample.astype(np.uint8)).save(os.path.join(sample_path, f"{base_count:04}.png"))
                        base_count += 1
                    all_samples.append(x_samples_ddim)
                    
    
    # additionally, save as grid
    grid = torch.stack(all_samples, 0)
    grid = rearrange(grid, 'n b c h w -> (n b) c h w')
    grid = make_grid(grid, nrow=2)
    # to image
    grid = 255. * rearrange(grid, 'c h w -> h w c').cpu().numpy()
    
    Image.fromarray(grid.astype(np.uint8)).save(os.path.join(outpath, f'{prompt.replace(" ", "-")}.png'))
    #return(Image.fromarray(grid.astype(np.uint8)),all_samples_images,None)    
    return Image.fromarray(grid.astype(np.uint8))
        
## **** above codelines are borrowed from multimodalart space

import gradio as gr

fastspeech = gr.Interface.load("huggingface/facebook/fastspeech2-en-ljspeech")

def text2speech(text):
    return fastspeech(text)
    
def engine(text_input):
    ner = gr.Interface.load("huggingface/flair/ner-english-ontonotes-large")
    entities = ner(text_input)
    
    entities = [tupl for tupl in entities if None not in tupl]
    entities_num = len(entities)
    
    img = run(entities[0],'50','256','256','1','10')
    #img_intfc = gr.Interface.load("spaces/multimodalart/latentdiffusion")
    #img_intfc = gr.Interface.load("spaces/multimodalart/latentdiffusion", inputs=[gr.inputs.Textbox(lines=1, label="Input Text"), gr.inputs.Textbox(lines=1, label="Input Text"), gr.inputs.Textbox(lines=1, label="Input Text"), gr.inputs.Textbox(lines=1, label="Input Text"), gr.inputs.Textbox(lines=1, label="Input Text"), gr.inputs.Textbox(lines=1, label="Input Text")],
    #outputs=[gr.outputs.Image(type="pil", label="output image"),gr.outputs.Carousel(label="Individual images",components=["image"]),gr.outputs.Textbox(label="Error")], )
    #title="Convert text to image")
    #img = img_intfc[0]
    #img = img_intfc('George','50','256','256','1','10')
    #img = img[0]
    #inputs=['George',50,256,256,1,10]
    #run(prompt, steps, width, height, images, scale)
    
    speech = text2speech(text_input)
    return entities, speech, img
    
app = gr.Interface(fn=engine, 
                   inputs=gr.inputs.Textbox(lines=5, label="Input Text"),
                   #live=True,
                   description="Takes a text as input and reads it out to you.", 
                   outputs=[gr.outputs.Textbox(type="auto", label="Text"), gr.outputs.Audio(type="file", label="Speech Answer"), #],
                   gr.outputs.Image(type="file", label="output image")],
                   examples=["On April 17th Sunday George celebrated Easter. He is staying at Empire State building with his parents. He is a citizen of Canada and speaks English and French fluently. His role model is former president Obama. He got 1000 dollar from his mother to visit Disney World and to buy new iPhone mobile.  George likes watching Game of Thrones."]
                   ).launch(debug=True) 
                   
 
 #get_audio = gr.Button("generate audio")
 #get_audio.click(text2speech, inputs=text, outputs=speech)
 
#def greet(name):
#    return "Hello " + name + "!!"

#iface = gr.Interface(fn=greet, inputs="text", outputs="text")
#iface.launch()