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import gradio as gr
import requests
import os
from PIL import Image
from io import BytesIO
from tqdm import tqdm
import time
# Defining the repository information and the trigger word
repo = "artificialguybr/TshirtDesignRedmond-V2"
# Function to generate image based on the prompt
def infer(color_prompt, dress_type_prompt, design_prompt, text):
# Build the full prompt
prompt = (
f"a single {color_prompt} colored {dress_type_prompt} with {design_prompt} designed on the {color_prompt} colored {dress_type_prompt}"
"hangs effortlessly on a plain wall, its simplicity transformed by bold,"
)
# Conditional parts
if text:
prompt += (
f" and {text} written on the {dress_type_prompt}"
"The contrast between the text and the calm background creates a striking visual"
)
# Add the hidden shadows part
prompt += f"soft light casts dynamic shadows, adding depth and emphasizing the crisp lines of the design, evoking a sense of modern sophistication."
full_prompt = f"{prompt}"
print("Generating image with prompt:", full_prompt)
api_url = f"https://api-inference.huggingface.co/models/{repo}"
#token = os.getenv("API_TOKEN") # Uncomment and use your Hugging Face API token
headers = {
#"Authorization": f"Bearer {token}"
}
payload = {
"inputs": full_prompt,
"parameters": {
"negative_prompt": "(worst quality, low quality, normal quality, lowres, low details, oversaturated, undersaturated, overexposed, underexposed, grayscale, bw, bad photo, bad photography, bad art:1.4), (watermark, signature, text font, username, error, logo, words, letters, digits, autograph, trademark, name:1.2), (blur, blurry, grainy), morbid, ugly, asymmetrical, mutated malformed, mutilated, poorly lit, bad shadow, draft, cropped, out of frame, cut off, censored, jpeg artifacts, out of focus, glitch, duplicate, (airbrushed, cartoon, anime, semi-realistic, cgi, render, blender, digital art, manga, amateur:1.3), (3D ,3D Game, 3D Game Scene, 3D Character:1.1), (bad hands, bad anatomy, bad body, bad face, bad teeth, bad arms, bad legs, deformities:1.3)",
"num_inference_steps": 30,
"scheduler": "DPMSolverMultistepScheduler"
},
}
error_count = 0
pbar = tqdm(total=None, desc="Loading model")
while True:
print("Sending request to API...")
response = requests.post(api_url, headers=headers, json=payload)
print("API response status code:", response.status_code)
if response.status_code == 200:
print("Image generation successful!")
return Image.open(BytesIO(response.content))
elif response.status_code == 503:
time.sleep(1)
pbar.update(1)
elif response.status_code == 500 and error_count < 5:
time.sleep(1)
error_count += 1
else:
print("API Error:", response.status_code)
raise Exception(f"API Error: {response.status_code}")
# Gradio Interface
iface = gr.Interface(
fn=infer,
inputs=[
gr.Textbox(lines=1, placeholder="Color Prompt"), # color_prompt
gr.Textbox(lines=1, placeholder="Dress Type Prompt"), # dress_type_prompt
gr.Textbox(lines=2, placeholder="Design Prompt"), # design_prompt
gr.Textbox(lines=1, placeholder="Text (Optional)"), # text
],
outputs="image",
title="Make your Brand",
description="Generation of clothes",
examples=[["Red", "T-shirt", "Simple design", "Stylish Text"]]
)
print("Launching Gradio interface...")
iface.launch()
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