import gradio as gr import google.generativeai as genai from PIL import Image import io import base64 # Set up Gemini API key GEMINI_API_KEY = "your-api-key-here" genai.configure(api_key=GEMINI_API_KEY) # Optimized function for fast & accurate image generation def generate_image(text, width=768, height=768): try: model = genai.GenerativeModel("gemini-pro-vision") # Fast inference configuration response = model.generate_content( text, generation_config={ "response_mime_type": "image/png", "width": width, "height": height } ) # Decode base64 image img_data = base64.b64decode(response.text) image = Image.open(io.BytesIO(img_data)) return image except Exception as e: return f"Error: {str(e)}" # Create Gradio UI with advanced options iface = gr.Interface( fn=generate_image, inputs=[ gr.Textbox(placeholder="Describe the image (e.g., Astronaut in a jungle)"), gr.Slider(minimum=256, maximum=1024, step=128, value=768, label="Width"), gr.Slider(minimum=256, maximum=1024, step=128, value=768, label="Height"), ], outputs="image", title="Fast Gemini AI Text-to-Image Generator", description="Enter a prompt and generate high-quality images with fast response times." ) iface.launch()