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| 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() |