Spaces:
Sleeping
Sleeping
File size: 2,779 Bytes
414c785 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 |
import gradio as gr
import requests
import os
import io
from PIL import Image
from typing import Optional
# Load Hugging Face Token from environment variables
def load_environment():
return os.getenv("HF_TOKEN")
# Enhance the text prompt for better photorealistic results
def craft_realistic_prompt(base_prompt: str) -> str:
realistic_modifiers = [
"photorealistic", "high resolution", "sharp focus",
"professional photography", "natural lighting", "detailed textures"
]
return f"{' '.join(realistic_modifiers)}, {base_prompt}, shot on professional camera, 8k resolution"
# Query Hugging Face API to generate image from text
def query_hf_api(prompt: str) -> Optional[bytes]:
model_url = "https://api-inference.huggingface.co/models/stabilityai/stable-diffusion-xl-base-1.0"
HF_TOKEN = load_environment()
if not HF_TOKEN:
raise ValueError("Hugging Face token not found. Set HF_TOKEN in environment variables.")
headers = {"Authorization": f"Bearer {HF_TOKEN}", "Content-Type": "application/json"}
payload = {
"inputs": craft_realistic_prompt(prompt),
"parameters": {
"negative_prompt": "cartoon, anime, low quality, bad anatomy, blurry, unrealistic, painting, drawing, sketch",
"num_inference_steps": 75,
"guidance_scale": 8.5,
}
}
response = requests.post(model_url, headers=headers, json=payload, timeout=120)
response.raise_for_status()
return response.content
# Generate image from text and return as PIL image
def generate_image(prompt: str):
try:
image_bytes = query_hf_api(prompt)
image = Image.open(io.BytesIO(image_bytes)).convert("RGB")
return image
except Exception as e:
return f"Error: {str(e)}"
# Modern Gradio UI using Blocks
with gr.Blocks() as demo:
gr.Markdown("# 🎨 Wangoes Text-to-Image Generator")
gr.Markdown("Generate stunning AI images from text using Hugging Face models.")
with gr.Row():
with gr.Column():
prompt_input = gr.Textbox(label="Enter your prompt",
placeholder="Describe the image you want to generate...")
generate_button = gr.Button("Generate Image", variant="primary")
with gr.Column():
image_output = gr.Image(label="Generated Image", type="pil")
gr.Examples(
examples=[
"A futuristic cityscape at sunset with flying cars",
"A serene mountain lake with crystal clear water",
"A cozy cabin in the woods during winter"
],
inputs=prompt_input
)
generate_button.click(fn=generate_image, inputs=prompt_input, outputs=image_output)
if __name__ == "__main__":
demo.launch() |