Hugging Face's logo Hugging Face Search models, datasets, users... Models Datasets Spaces Posts Docs Solutions Pricing Spaces: fotographer / fai-lanthos like 0 Logs App Files Community Settings fai-lanthos / app.py comdoleger's picture comdoleger Update app.py d677fb1 VERIFIED about 1 hour ago raw Copy download link history blame edit delete No virus 7.87 kB import os import math import gradio as gr import numpy as np import requests import json import base64 from PIL import Image from io import BytesIO import runpod from enum import Enum api_key = os.getenv("FAI_API_KEY") api = os.getenv("FAI_API") def image_to_base64(image): # Open the image file with image: # Create a buffer to hold the binary data buffered = BytesIO() # Save the image in its original format to the buffer #print(image.format) image.save(buffered, format="PNG") # Get the byte data from the buffer binary_image_data = buffered.getvalue() # Encode the binary data to a base64 string base64_image = base64.b64encode(binary_image_data).decode("utf-8") return base64_image def process(data, api, api_key): runpod.api_key = api_key input_payload = {"input": data } try: endpoint = runpod.Endpoint(api) run_request = endpoint.run(input_payload) # Initial check without blocking, useful for quick tasks status = run_request.status() print(f"Initial job status: {status}") if status != "COMPLETED": # Polling with timeout for long-running tasks output = run_request.output(timeout=60) else: output = run_request.output() print(f"Job output: {output}") except Exception as e: print(f"An error occurred: {e}") image_data = output['image'] # Decode the Base64 string image_bytes = base64.b64decode(image_data) # Convert binary data to image image = Image.open(BytesIO(image_bytes)) return image def process_generate(fore, prompt, image_width, image_height, intensity, mode, refprompt): print(f"MODE: {mode}, INTENSITY: {intensity}, WIDTH: {image_width}, HEIGHT: {image_height}") forestr = image_to_base64(fore.convert("RGBA")) data = { "foreground_image64": forestr, "prompt" : prompt, "mode" : mode, "intensity" : float(intensity), "width" : int(image_width), "height" : int(image_height), "refprompt" : refprompt } image = process(data, api, api_key) return image class Stage(Enum): FIRST_STAGE = "first-stage" SECOND_STAGE = "refiner" FULL = "full" css="""#disp_image { text-align: center; /* Horizontally center the content */ } #share-btn-container {padding-left: 0.5rem !important; padding-right: 0.5rem !important; background-color: #000000; justify-content: center; align-items: center; border-radius: 9999px !important; max-width: 13rem; margin-left: auto;} div#share-btn-container > div {flex-direction: row;background: black;align-items: center} #share-btn-container:hover {background-color: #060606} #share-btn {all: initial; color: #ffffff;font-weight: 600; cursor:pointer; font-family: 'IBM Plex Sans', sans-serif; margin-left: 0.5rem !important; padding-top: 0.5rem !important; padding-bottom: 0.5rem !important;right:0;} #share-btn * {all: unset} #share-btn-container div:nth-child(-n+2){width: auto !important;min-height: 0px !important;} #share-btn-container .wrap {display: none !important} #share-btn-container.hidden {display: none!important} #duplicate-button { margin-left: auto; color: #fff; background: #1565c0; } """ block = gr.Blocks(css=css, title="## F.ai Lanthos").queue() with block: gr.HTML("""

Fotographer AI Lanthos

""") gr.HTML('''
Check out our AppFotographer.ai!
''') with gr.Row(): gr.Markdown("### F.ai Lanthos: Real Composite Photography in 2 minutes!") with gr.Row(): fore = gr.Image(source='upload', type="pil", label="Foreground Image", height=400) with gr.Column(): result_gallery = gr.Image(label='Output') #gr.Gallery(height=400, object_fit='contain', label='Outputs') with gr.Row(): prompt = gr.Textbox(label="Prompt") with gr.Column(): refprompt = gr.Textbox(label="Refiner Prompt") with gr.Row(): mode = gr.Radio(choices=[e.value for e in Stage], value=Stage.FULL.value, label="Generation Mode", type='value') with gr.Column(): image_width = gr.Slider(label="Image Width", minimum=256, maximum=1500, value=1024, step=64) image_height = gr.Slider(label="Image Height", minimum=256, maximum=1500, value=1024, step=64) with gr.Row(): intensity = gr.Slider(label="Refiner Strength", minimum=1, maximum=7, value=3, step=0.5) generate_button = gr.Button(value="Generate") ips = [fore, prompt, image_width, image_height, intensity, mode, refprompt] generate_button.click(fn=process_generate, inputs=ips, outputs=[result_gallery]) block.launch()