Create backup.v6.app.py

backup.v6.app.py

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+import os
+import random
+import uuid
+import base64
+import gradio as gr
+import numpy as np
+from PIL import Image
+import spaces
+import torch
+import glob
+from datetime import datetime
+import pandas as pd
+import json
+import re
+
+from diffusers import StableDiffusionXLPipeline, EulerAncestralDiscreteScheduler
+
+DESCRIPTION = """# 🎨 ArtForge: Community AI Gallery
+
+Create, curate, and compete with AI-generated art. Join our creative multiplayer experience! 🖼️🏆✨
+"""
+
+# Global variables
+image_metadata = pd.DataFrame(columns=['Filename', 'Prompt', 'Likes', 'Dislikes', 'Hearts', 'Created'])
+LIKES_CACHE_FILE = "likes_cache.json"
+
+def load_likes_cache():
+    if os.path.exists(LIKES_CACHE_FILE):
+        with open(LIKES_CACHE_FILE, 'r') as f:
+            return json.load(f)
+    return {}
+
+def save_likes_cache(cache):
+    with open(LIKES_CACHE_FILE, 'w') as f:
+        json.dump(cache, f)
+
+likes_cache = load_likes_cache()
+
+def create_download_link(filename):
+    with open(filename, "rb") as file:
+        encoded_string = base64.b64encode(file.read()).decode('utf-8')
+        download_link = f'<a href="data:image/png;base64,{encoded_string}" download="{filename}">Download Image</a>'
+        return download_link
+
+def save_image(img, prompt):
+    global image_metadata, likes_cache
+    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+    safe_prompt = re.sub(r'[^\w\s-]', '', prompt.lower())[:50]  # Limit to 50 characters
+    safe_prompt = re.sub(r'[-\s]+', '-', safe_prompt).strip('-')
+    filename = f"{timestamp}_{safe_prompt}.png"
+    img.save(filename)
+    new_row = pd.DataFrame({
+        'Filename': [filename],
+        'Prompt': [prompt],
+        'Likes': [0],
+        'Dislikes': [0],
+        'Hearts': [0],
+        'Created': [datetime.now()]
+    })
+    image_metadata = pd.concat([image_metadata, new_row], ignore_index=True)
+    likes_cache[filename] = {'likes': 0, 'dislikes': 0, 'hearts': 0}
+    save_likes_cache(likes_cache)
+    return filename
+
+def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+    return seed
+
+def get_image_gallery():
+    global image_metadata
+    image_files = image_metadata['Filename'].tolist()
+    return [(file, get_image_caption(file)) for file in image_files if os.path.exists(file)]
+
+def get_image_caption(filename):
+    global likes_cache, image_metadata
+    if filename in likes_cache:
+        likes = likes_cache[filename]['likes']
+        dislikes = likes_cache[filename]['dislikes']
+        hearts = likes_cache[filename]['hearts']
+        prompt = image_metadata[image_metadata['Filename'] == filename]['Prompt'].values[0]
+        return f"{filename}\nPrompt: {prompt}\n👍 {likes} 👎 {dislikes} ❤️ {hearts}"
+    return filename
+
+def delete_all_images():
+    global image_metadata, likes_cache
+    for file in image_metadata['Filename']:
+        if os.path.exists(file):
+            os.remove(file)
+    image_metadata = pd.DataFrame(columns=['Filename', 'Prompt', 'Likes', 'Dislikes', 'Hearts', 'Created'])
+    likes_cache = {}
+    save_likes_cache(likes_cache)
+    return get_image_gallery(), image_metadata.values.tolist()
+
+def delete_image(filename):
+    global image_metadata, likes_cache
+    if filename and os.path.exists(filename):
+        os.remove(filename)
+        image_metadata = image_metadata[image_metadata['Filename'] != filename]
+        if filename in likes_cache:
+            del likes_cache[filename]
+            save_likes_cache(likes_cache)
+    return get_image_gallery(), image_metadata.values.tolist()
+
+def vote(filename, vote_type):
+    global likes_cache
+    if filename in likes_cache:
+        likes_cache[filename][vote_type.lower()] += 1
+        save_likes_cache(likes_cache)
+    return get_image_gallery(), image_metadata.values.tolist()
+
+def get_random_style():
+    styles = [
+        "Impressionist", "Cubist", "Surrealist", "Abstract Expressionist",
+        "Pop Art", "Minimalist", "Baroque", "Art Nouveau", "Pointillist", "Fauvism"
+    ]
+    return random.choice(styles)
+
+MAX_SEED = np.iinfo(np.int32).max
+
+if not torch.cuda.is_available():
+    DESCRIPTION += "\n<p>Running on CPU 🥶 This demo may not work on CPU.</p>"
+
+USE_TORCH_COMPILE = 0
+ENABLE_CPU_OFFLOAD = 0
+
+if torch.cuda.is_available():
+    pipe = StableDiffusionXLPipeline.from_pretrained(
+        "fluently/Fluently-XL-v4",
+        torch_dtype=torch.float16,
+        use_safetensors=True,
+    )
+    pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
+    
+    pipe.load_lora_weights("ehristoforu/dalle-3-xl-v2", weight_name="dalle-3-xl-lora-v2.safetensors", adapter_name="dalle")
+    pipe.set_adapters("dalle")
+
+    pipe.to("cuda")
+
+@spaces.GPU(enable_queue=True)
+def generate(
+    prompt: str,
+    negative_prompt: str = "",
+    use_negative_prompt: bool = False,
+    seed: int = 0,
+    width: int = 1024,
+    height: int = 1024,
+    guidance_scale: float = 3,
+    randomize_seed: bool = False,
+    progress=gr.Progress(track_tqdm=True),
+):
+    seed = int(randomize_seed_fn(seed, randomize_seed))
+
+    if not use_negative_prompt:
+        negative_prompt = ""
+
+    images = pipe(
+        prompt=prompt,
+        negative_prompt=negative_prompt,
+        width=width,
+        height=height,
+        guidance_scale=guidance_scale,
+        num_inference_steps=20,
+        num_images_per_prompt=1,
+        cross_attention_kwargs={"scale": 0.65},
+        output_type="pil",
+    ).images
+    image_paths = [save_image(img, prompt) for img in images]
+    download_links = [create_download_link(path) for path in image_paths]
+
+    return image_paths, seed, download_links, get_image_gallery(), image_metadata.values.tolist()
+
+examples = [
+    f"{get_random_style()} painting of a majestic lighthouse on a rocky coast. Use bold brushstrokes and a vibrant color palette to capture the interplay of light and shadow as the lighthouse beam cuts through a stormy night sky.",
+    f"{get_random_style()} still life featuring a pair of vintage eyeglasses. Focus on the intricate details of the frames and lenses, using a warm color scheme to evoke a sense of nostalgia and wisdom.",
+    f"{get_random_style()} depiction of a rustic wooden stool in a sunlit artist's studio. Emphasize the texture of the wood and the interplay of light and shadow, using a mix of earthy tones and highlights.",
+    f"{get_random_style()} scene viewed through an ornate window frame. Contrast the intricate details of the window with a dreamy, soft-focus landscape beyond, using a palette that transitions from cool interior tones to warm exterior hues.",
+    f"{get_random_style()} close-up study of interlaced fingers. Use a monochromatic color scheme to emphasize the form and texture of the hands, with dramatic lighting to create depth and emotion.",
+    f"{get_random_style()} composition featuring a set of dice in motion. Capture the energy and randomness of the throw, using a dynamic color palette and blurred lines to convey movement.",
+    f"{get_random_style()} interpretation of heaven. Create an ethereal atmosphere with soft, billowing clouds and radiant light, using a palette of celestial blues, golds, and whites.",
+    f"{get_random_style()} portrayal of an ancient, mystical gate. Combine architectural details with elements of fantasy, using a rich, jewel-toned palette to create an air of mystery and magic.",
+    f"{get_random_style()} portrait of a curious cat. Focus on capturing the feline's expressive eyes and sleek form, using a mix of bold and subtle colors to bring out the cat's personality.",
+    f"{get_random_style()} abstract representation of toes in sand. Use textured brushstrokes to convey the feeling of warm sand, with a palette inspired by a sun-drenched beach."
+]
+
+css = '''
+.gradio-container{max-width: 1024px !important}
+h1{text-align:center}
+footer {
+visibility: hidden
+}
+'''
+
+with gr.Blocks(theme=gr.themes.Soft(), css=css) as demo:
+    gr.Markdown(DESCRIPTION)
+
+    with gr.Tab("Generate Images"):
+        with gr.Group():
+            with gr.Row():
+                prompt = gr.Text(
+                    label="Prompt",
+                    show_label=False,
+                    max_lines=1,
+                    placeholder="Enter your prompt",
+                    container=False,
+                )
+                run_button = gr.Button("Run", scale=0)
+            result = gr.Gallery(label="Result", columns=1, preview=True, show_label=False)
+        with gr.Accordion("Advanced options", open=False):
+            use_negative_prompt = gr.Checkbox(label="Use negative prompt", value=True)
+            negative_prompt = gr.Text(
+                label="Negative prompt",
+                lines=4,
+                max_lines=6,
+                value="""(deformed, distorted, disfigured:1.3), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, (mutated hands and fingers:1.4), disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation, (NSFW:1.25)""",
+                placeholder="Enter a negative prompt",
+                visible=True,
+            )
+            seed = gr.Slider(
+                label="Seed",
+                minimum=0,
+                maximum=MAX_SEED,
+                step=1,
+                value=0,
+                visible=True
+            )
+            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+            with gr.Row(visible=True):
+                width = gr.Slider(
+                    label="Width",
+                    minimum=512,
+                    maximum=2048,
+                    step=8,
+                    value=1920,
+                )
+                height = gr.Slider(
+                    label="Height",
+                    minimum=512,
+                    maximum=2048,
+                    step=8,
+                    value=1080,
+                )
+            with gr.Row():
+                guidance_scale = gr.Slider(
+                    label="Guidance Scale",
+                    minimum=0.1,
+                    maximum=20.0,
+                    step=0.1,
+                    value=20.0,
+                )
+        
+        gr.Examples(
+            examples=examples,
+            inputs=prompt,
+            outputs=[result, seed],
+            fn=generate,
+            cache_examples=False,
+        )
+
+    with gr.Tab("Gallery and Voting"):
+        image_gallery = gr.Gallery(label="Generated Images", show_label=True, columns=4, height="auto")
+        
+        with gr.Row():
+            like_button = gr.Button("👍 Like")
+            dislike_button = gr.Button("👎 Dislike")
+            heart_button = gr.Button("❤️ Heart")
+            delete_image_button = gr.Button("🗑️ Delete Selected Image")
+        
+        selected_image = gr.State(None)
+
+    with gr.Tab("Metadata and Management"):
+        metadata_df = gr.Dataframe(
+            label="Image Metadata",
+            headers=["Filename", "Prompt", "Likes", "Dislikes", "Hearts", "Created"],
+            interactive=False
+        )
+        delete_all_button = gr.Button("🗑️ Delete All Images")
+
+    use_negative_prompt.change(
+        fn=lambda x: gr.update(visible=x),
+        inputs=use_negative_prompt,
+        outputs=negative_prompt,
+        api_name=False,
+    )
+
+    delete_all_button.click(
+        fn=delete_all_images,
+        inputs=[],
+        outputs=[image_gallery, metadata_df],
+    )
+
+    image_gallery.select(
+        fn=lambda evt: evt,
+        inputs=[],
+        outputs=[selected_image],
+    )
+
+    like_button.click(
+        fn=lambda x: vote(x, 'likes'),
+        inputs=[selected_image],
+        outputs=[image_gallery, metadata_df],
+    )
+
+    dislike_button.click(
+        fn=lambda x: vote(x, 'dislikes'),
+        inputs=[selected_image],
+        outputs=[image_gallery, metadata_df],
+    )
+
+    heart_button.click(
+        fn=lambda x: vote(x, 'hearts'),
+        inputs=[selected_image],
+        outputs=[image_gallery, metadata_df],
+    )
+
+    delete_image_button.click(
+        fn=delete_image,
+        inputs=[selected_image],
+        outputs=[image_gallery, metadata_df],
+    )
+
+    def update_gallery_and_metadata():
+        return gr.update(value=get_image_gallery()), gr.update(value=image_metadata.values.tolist())
+
+    gr.on(
+        triggers=[
+            prompt.submit,
+            negative_prompt.submit,
+            run_button.click,
+        ],
+        fn=generate,
+        inputs=[
+            prompt,
+            negative_prompt,
+            use_negative_prompt,
+            seed,
+            width,
+            height,
+            guidance_scale,
+            randomize_seed,
+        ],
+        outputs=[result, seed, gr.HTML(visible=False), image_gallery, metadata_df],
+        api_name="run",
+    )
+
+    demo.load(fn=update_gallery_and_metadata, outputs=[image_gallery, metadata_df])
+
+if __name__ == "__main__":
+    demo.queue(max_size=20).launch(share=True, debug=False)