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| import gradio as gr | |
| import random | |
| import time | |
| import requests | |
| import io | |
| from PIL import Image | |
| import traceback | |
| from base64 import b64decode,b64encode | |
| from io import BytesIO | |
| import spacy | |
| from profanity_filter import ProfanityFilter | |
| with gr.Blocks(theme="darkdefault") as demo: | |
| def welcome(name): | |
| return f"Welcome to AIXRPL.com Minter, {name}!" | |
| def profanityCheck(prompt): | |
| prompt = prompt.replace('+',' ').replace('|',' ') | |
| nlp = spacy.load('en') | |
| profanity_filter = ProfanityFilter(nlps={'en': nlp}) # reuse spacy Language (optional) | |
| nlp.add_pipe(profanity_filter.spacy_component, last=True) | |
| pf = ProfanityFilter() | |
| pf.censor_whole_words = False | |
| doc = nlp(prompt) | |
| if doc._.is_profane: | |
| print(doc) | |
| return True | |
| for token in doc: | |
| print(token) | |
| if token._.is_profane: | |
| return True | |
| return False | |
| def inference(_prompt,_token): | |
| try: | |
| from PIL import Image | |
| import uuid | |
| import os | |
| print(_prompt,_token) | |
| if profanityCheck(_prompt): | |
| return '','unsafe','','','' | |
| r = requests.post(url='https://xcyborgart-app.herokuapp.com/create',data={"prompt":_prompt,"token":_token}) | |
| all_data = r.json() | |
| print(all_data.keys()) | |
| import base64 | |
| from io import BytesIO | |
| from PIL import Image | |
| im_bytes = base64.b64decode(all_data['img_data']) # im_bytes is a binary image | |
| im_file = BytesIO(im_bytes) # convert image to file-like object | |
| img = Image.open(im_file) # img is now PIL Image object | |
| return(img,all_data['description'],all_data['image_url'],all_data['keywords'],all_data['keywords_string']) | |
| except Exception as e: | |
| print('exception:',e) | |
| traceback.print_exc() | |
| return '','','','','' | |
| # img.save('/tmp/data.png') | |
| #return '/tmp/data.png' | |
| with gr.Group(): | |
| generate_progress = gr.StatusTracker(cover_container=True) | |
| with gr.Row(): | |
| with gr.Column(): | |
| with gr.Tab("Create"): | |
| gr.Markdown( | |
| """ | |
| Create AI generated artworks by using prompt engineering. | |
| """ | |
| ) | |
| text = gr.Textbox( | |
| label="Enter Prompt", show_label=True, max_lines=5 | |
| ).style( | |
| border=(True, False, True, True), | |
| rounded=(True, False, False, True), | |
| container=True, | |
| ) | |
| btn = gr.Button("Create").style( | |
| margin=True, | |
| rounded=(False, True, True, False), | |
| ) | |
| gr.Markdown( | |
| """ | |
| AI generated metadata. | |
| """ | |
| ) | |
| description = gr.Textbox( | |
| label="AI Generated Description", interactive=True, show_label=True, max_lines=1, elem_id="descData" | |
| ).style( | |
| border=(True, False, True, True), | |
| rounded=(True, False, False, True), | |
| container=True, | |
| ) | |
| traits = gr.HighlightedText(label="Auto Traits",interactive=True, show_label=True) | |
| # build_result = gr.Gallery()#gr.Image(interactive=False, shape=(320,320)) | |
| with gr.Column(): | |
| with gr.Tab("Artwork"): | |
| build_result = gr.Image(type="pil", shape=(512,None),show_label=True,label="Artwork Preview",interactive=False,) | |
| walletToken = gr.Textbox( | |
| visible=False, interactive=True, elem_id="walletToken", max_lines=1 | |
| ) | |
| imageData = gr.Textbox( | |
| visible=False, interactive=False, elem_id="imageData", max_lines=1 | |
| ) | |
| attribData = gr.Textbox( | |
| visible=False, interactive=False, elem_id="attribData", max_lines=1 | |
| ) | |
| btn.click( | |
| inference, | |
| inputs=[text,walletToken], | |
| outputs=[build_result,description,imageData, traits, attribData], | |
| status_tracker=generate_progress, | |
| api_name="generate" | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch(show_api=False, debug=True, enable_queue=True) |