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| import os | |
| import time | |
| import gradio as gr | |
| import numpy as np | |
| import requests | |
| from PIL import Image | |
| from io import BytesIO | |
| from azure.storage.blob import BlobServiceClient | |
| from huggingface_hub import InferenceClient | |
| from paintingface import generate | |
| from azure.storage.blob import BlobClient | |
| from pathlib import Path | |
| connect_str = os.getenv('AZURE_STORAGE_CONNECTION_STRING') | |
| container_name = "images" | |
| blob_service_client = BlobServiceClient.from_connection_string(conn_str=connect_str) # create a blob service client to interact with the storage account | |
| try: | |
| container_client = blob_service_client.get_container_client(container=container_name) # get container client to interact with the container in which images will be stored | |
| container_client.get_container_properties() # get properties of the container to force exception to be thrown if container does not exist | |
| except Exception as e: | |
| container_client = blob_service_client.create_container(container_name) # create a container in the storage account if it does not exista | |
| def load_image(url): | |
| response = requests.get(url) | |
| img = Image.open(BytesIO(response.content)) | |
| return img | |
| def image_loader(url): | |
| img = load_image(url) | |
| return img | |
| def txt2img(x): | |
| client = InferenceClient() | |
| image = client.text_to_image(x) | |
| timestr = time.strftime("%Y%m%d-%H%M%S") | |
| local_path = "./" | |
| # Upload the created file | |
| imgfile="txt2img%s.png"%timestr | |
| print("######################file name %s",imgfile) | |
| img_saved = image.save(imgfile) | |
| print("######################file is" ,os.path.abspath(imgfile)) | |
| blob_client = blob_service_client.get_blob_client(container=container_name, blob=imgfile) | |
| print("\nUploading to Azure Storage as blob:\n\t" + imgfile) | |
| upload_file_path = os.path.join(local_path, imgfile) | |
| with open(file=upload_file_path, mode="rb") as data: | |
| blob_client.upload_blob(data) | |
| blob_items = container_client.list_blobs() # list all the blobs in the container | |
| for blob in blob_items: | |
| blob_client = container_client.get_blob_client(blob=imgfile) | |
| url=blob_client.url | |
| print("i###################URL OF IMAGE = %s", url) | |
| return image_loader(url), generate(image_loader(url)) | |
| def img2img(x): | |
| timestr = time.strftime("%Y%m%d-%H%M%S") | |
| local_path = "./" | |
| # Upload the created file | |
| imgfile="img2img%s.png"%timestr | |
| print("######################file name %s",imgfile) | |
| img = Image.fromarray(x, "RGB") | |
| img_saved = img.save(imgfile) | |
| print("######################file is" ,os.path.abspath(imgfile)) | |
| blob_client = blob_service_client.get_blob_client(container=container_name, blob=imgfile) | |
| print("\nUploading to Azure Storage as blob:\n\t" + imgfile) | |
| upload_file_path = os.path.join(local_path, imgfile) | |
| with open(file=upload_file_path, mode="rb") as data: | |
| blob_client.upload_blob(data) | |
| blob_items = container_client.list_blobs() # list all the blobs in the container | |
| for blob in blob_items: | |
| blob_client = container_client.get_blob_client(blob=imgfile) | |
| url=blob_client.url | |
| print("i###################URL OF IMAGE = %s", url) | |
| return image_loader(url), generate(image_loader(url)) | |
| #return image_loader(x), generate(image_loader(x)) | |
| def imgproc(x): | |
| limg = load_image(x) | |
| return np.fliplr(limg) | |
| title = "Azure Huggingface" | |
| t2i_demo = gr.Interface(fn=txt2img, inputs="text", outputs=["image","image"]) | |
| i2i_demo = gr.Interface(fn=img2img, inputs=gr.inputs.Image(label="Image Input 2") , outputs=["image","image"]) | |
| demo = gr.TabbedInterface([t2i_demo,i2i_demo ], ["Text-to-Image", "Image-to-Image"]) | |
| #if __name__ == "__main__": | |
| demo.launch(share=True) | |