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| import gradio as gr | |
| from gradio_client import Client | |
| import os | |
| from PIL import Image | |
| import io | |
| import base64 | |
| import requests | |
| def generate_image(prompt, negative_prompt="", guidance_scale=9): | |
| """ | |
| Generate an image using the Stable Diffusion API | |
| """ | |
| if not prompt.strip(): | |
| return None | |
| try: | |
| # Initialize the client | |
| client = Client("stabilityai/stable-diffusion") | |
| # Make the prediction | |
| result = client.predict( | |
| prompt=prompt, | |
| negative=negative_prompt, | |
| scale=guidance_scale, | |
| api_name="/infer" | |
| ) | |
| print(f"Debug - Result type: {type(result)}") | |
| print(f"Debug - Result: {result}") | |
| # Handle the specific format: list of dictionaries with 'image' keys | |
| if isinstance(result, list): | |
| for i, item in enumerate(result): | |
| try: | |
| if isinstance(item, dict) and 'image' in item: | |
| # Extract the image path from the dictionary | |
| image_path = item['image'] | |
| if os.path.exists(image_path): | |
| return Image.open(image_path) | |
| elif isinstance(item, str): | |
| # If it's a file path, load it as PIL Image | |
| if os.path.exists(item): | |
| return Image.open(item) | |
| # If it's a URL, download and return as PIL Image | |
| elif item.startswith(('http://', 'https://')): | |
| response = requests.get(item) | |
| return Image.open(io.BytesIO(response.content)) | |
| elif hasattr(item, 'save'): # PIL Image object | |
| return item | |
| except Exception as e: | |
| print(f"Debug - Error processing item {i}: {e}") | |
| continue | |
| # If no image found, try first item as fallback | |
| if len(result) > 0: | |
| first_item = result[0] | |
| if isinstance(first_item, dict) and 'image' in first_item: | |
| image_path = first_item['image'] | |
| if os.path.exists(image_path): | |
| return Image.open(image_path) | |
| elif isinstance(first_item, str) and os.path.exists(first_item): | |
| return Image.open(first_item) | |
| elif isinstance(result, dict) and 'image' in result: | |
| # Single dictionary result | |
| image_path = result['image'] | |
| if os.path.exists(image_path): | |
| return Image.open(image_path) | |
| elif isinstance(result, str): | |
| # Single string result - could be file path or URL | |
| if os.path.exists(result): | |
| return Image.open(result) | |
| elif result.startswith(('http://', 'https://')): | |
| response = requests.get(result) | |
| return Image.open(io.BytesIO(response.content)) | |
| elif hasattr(result, 'save'): | |
| # PIL Image object | |
| return result | |
| # If nothing worked, return None | |
| print("Debug - No valid image found in result") | |
| return None | |
| except Exception as e: | |
| print(f"Debug - Full error: {e}") | |
| return None | |
| def create_interface(): | |
| """ | |
| Create and configure the Gradio interface | |
| """ | |
| with gr.Blocks( | |
| title="🎨 VibeCode Image Generator", | |
| theme=gr.themes.Soft(), | |
| css=""" | |
| .main-header { | |
| text-align: center; | |
| margin-bottom: 2rem; | |
| } | |
| footer { | |
| visibility: hidden; | |
| } | |
| .generate-btn { | |
| background: linear-gradient(45deg, #667eea 0%, #764ba2 100%); | |
| border: none; | |
| border-radius: 8px; | |
| color: white; | |
| font-weight: bold; | |
| padding: 12px 24px; | |
| transition: transform 0.2s ease; | |
| } | |
| .generate-btn:hover { | |
| transform: translateY(-2px); | |
| } | |
| """ | |
| ) as demo: | |
| # Header | |
| gr.Markdown( | |
| """ | |
| <div class="main-header"> | |
| <h1>🎨 VibeCode Image Generator</h1> | |
| <p>Create stunning AI-generated images from text descriptions</p> | |
| </div> | |
| """, | |
| elem_classes=["main-header"] | |
| ) | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| # Input controls | |
| gr.Markdown("### 📝 Generation Settings") | |
| prompt_input = gr.Textbox( | |
| label="✨ Prompt", | |
| placeholder="Describe the image you want to generate... (e.g., 'A serene landscape with mountains and a lake at sunset')", | |
| lines=3, | |
| value="" | |
| ) | |
| negative_prompt_input = gr.Textbox( | |
| label="🚫 Negative Prompt", | |
| placeholder="What you DON'T want in the image... (e.g., 'blurry, low quality, distorted')", | |
| lines=2, | |
| value="blurry, low quality, distorted, ugly, duplicate" | |
| ) | |
| guidance_scale_input = gr.Slider( | |
| label="🎛️ Guidance Scale", | |
| minimum=1, | |
| maximum=20, | |
| value=9, | |
| step=0.5, | |
| info="How closely the model follows your prompt (higher = more strict)" | |
| ) | |
| generate_btn = gr.Button( | |
| "🎨 Generate Image", | |
| variant="primary", | |
| size="lg", | |
| elem_classes=["generate-btn"] | |
| ) | |
| # Examples | |
| gr.Markdown("### 💡 Example Prompts") | |
| gr.Examples( | |
| examples=[ | |
| ["A magical forest with glowing mushrooms and fireflies, fantasy art style", "blurry, low quality", 9], | |
| ["A futuristic cityscape at night with neon lights, cyberpunk style", "daylight, vintage", 12], | |
| ["A cute robot pet sitting in a garden, digital art", "scary, dark, realistic", 8], | |
| ["An astronaut riding a horse on Mars, cinematic lighting", "cartoon, low resolution", 10], | |
| ["A steampunk airship flying through clouds, detailed illustration", "modern, simple", 11] | |
| ], | |
| inputs=[prompt_input, negative_prompt_input, guidance_scale_input], | |
| label="Click an example to try it out!" | |
| ) | |
| with gr.Column(scale=1): | |
| # Output | |
| gr.Markdown("### 🖼️ Generated Image") | |
| output_image = gr.Image( | |
| label="Result", | |
| type="pil", | |
| height=400, | |
| show_label=False | |
| ) | |
| # Status/Info | |
| gr.Markdown( | |
| """ | |
| ### ℹ️ Tips for Better Results: | |
| - **Be specific**: Include details about style, lighting, composition | |
| - **Use negative prompts**: Exclude unwanted elements | |
| - **Adjust guidance**: Higher values follow prompts more strictly | |
| - **Try different scales**: 7-12 usually work well for most images | |
| """ | |
| ) | |
| # Event handlers | |
| generate_btn.click( | |
| fn=generate_image, | |
| inputs=[prompt_input, negative_prompt_input, guidance_scale_input], | |
| outputs=output_image, | |
| show_progress=True | |
| ) | |
| # Allow Enter key to generate | |
| prompt_input.submit( | |
| fn=generate_image, | |
| inputs=[prompt_input, negative_prompt_input, guidance_scale_input], | |
| outputs=output_image, | |
| show_progress=True | |
| ) | |
| # Footer | |
| gr.Markdown( | |
| """ | |
| --- | |
| <div style="text-align: center; color: #666; margin-top: 2rem;"> | |
| <p>Powered by Vibe Code Org. | Built with ❤️ </p> | |
| </div> | |
| """ | |
| ) | |
| return demo | |
| if __name__ == "__main__": | |
| # Create and launch the interface | |
| demo = create_interface() | |
| demo.launch( | |
| server_name="0.0.0.0", # Important for Hugging Face Spaces | |
| server_port=7860, # Default port for HF Spaces | |
| debug=False | |
| ) |