hfmrbean/Multitab_genai

1import os2import time3import gradio as gr4import numpy as np5import requests6from PIL import Image7from io import BytesIO8from azure.storage.blob import BlobServiceClient9from huggingface_hub import InferenceClient10from paintingface import generate11from azure.storage.blob import BlobClient12from pathlib import Path13 14os.environ['AZURE_STORAGE_CONNECTION_STRING'] = 'DefaultEndpointsProtocol=https;AccountName=myblobstorage6397;AccountKey=nITHnSxOMtUk9jEyq4FeDqJnrPOChIN210p9iOxvo2bkBP9l3jKwwINBbGiEw5kdpuDRwpo4lXUK+ASt0D9zLQ==;EndpointSuffix=core.windows.net'15connect_str = os.getenv('AZURE_STORAGE_CONNECTION_STRING')16container_name = "images"17blob_service_client = BlobServiceClient.from_connection_string(conn_str=connect_str) # create a blob service client to interact with the storage account18try:19    container_client = blob_service_client.get_container_client(container=container_name) # get container client to interact with the container in which images will be stored20    container_client.get_container_properties() # get properties of the container to force exception to be thrown if container does not exist21except Exception as e:22    container_client = blob_service_client.create_container(container_name) # create a container in the storage account if it does not exista23 24def load_image(url):25    response = requests.get(url)26    img = Image.open(BytesIO(response.content))27    return img28 29def image_loader(url):30    img = load_image(url)31    return img32 33def txt2img(x):34    client = InferenceClient()35    image = client.text_to_image(x)36    timestr = time.strftime("%Y%m%d-%H%M%S")37    local_path = "./"38 39    # Upload the created file40    imgfile="txt2img%s.png"%timestr41    print("######################file name %s",imgfile)42    img_saved = image.save(imgfile)43    print("######################file is" ,os.path.abspath(imgfile))44    blob_client = blob_service_client.get_blob_client(container=container_name, blob=imgfile)45    print("\nUploading to Azure Storage as blob:\n\t" + imgfile)46    upload_file_path = os.path.join(local_path, imgfile)47 48    with open(file=upload_file_path, mode="rb") as data:49            blob_client.upload_blob(data)50    blob_items = container_client.list_blobs() # list all the blobs in the container51    for blob in blob_items:52        blob_client = container_client.get_blob_client(blob=imgfile)53        url=blob_client.url54    print("i###################URL OF IMAGE  = %s", url)55    return  image_loader(url), generate(image_loader(url))56 57 58def img2img(x):59    timestr = time.strftime("%Y%m%d-%H%M%S")60    local_path = "./"61 62    # Upload the created file63    imgfile="img2img%s.png"%timestr64    print("######################file name %s",imgfile)65    img = Image.fromarray(x, "RGB")66 67    img_saved = img.save(imgfile)68    print("######################file is" ,os.path.abspath(imgfile))69    blob_client = blob_service_client.get_blob_client(container=container_name, blob=imgfile)70    print("\nUploading to Azure Storage as blob:\n\t" + imgfile)71    upload_file_path = os.path.join(local_path, imgfile)72 73    with open(file=upload_file_path, mode="rb") as data:74            blob_client.upload_blob(data)75    blob_items = container_client.list_blobs() # list all the blobs in the container76    for blob in blob_items:77        blob_client = container_client.get_blob_client(blob=imgfile)78        url=blob_client.url79    print("i###################URL OF IMAGE  = %s", url)80    return  image_loader(url), generate(image_loader(url))81    #return  image_loader(x), generate(image_loader(x))82 83def imgproc(x):84    limg = load_image(x)85    return np.fliplr(limg)86 87 88title = "Azure Huggingface"89t2i_demo = gr.Interface(fn=txt2img, inputs="text", outputs=["image","image"])90i2i_demo = gr.Interface(fn=img2img, inputs=gr.inputs.Image(label="Image Input 2") , outputs=["image","image"])91 92demo = gr.TabbedInterface([t2i_demo,i2i_demo ], ["Text-to-Image", "Image-to-Image"])93 94#if __name__ == "__main__":95demo.launch()96