Update app.py

app.py

@@ -1,48 +1,51 @@
 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
-import logging
-from diffusers import StableDiffusionXLPipeline, EulerAncestralDiscreteScheduler
 
-# Set up logging
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
+from diffusers import StableDiffusionXLPipeline, EulerAncestralDiscreteScheduler
 
 DESCRIPTION = """# 🎨 ArtForge: Community AI Gallery
-Create, curate, and compete with AI-generated art. Join our creative multiplayer experience! 🖼️🏆✨"""
 
-METADATA_FILE = "image_metadata.json"
-MAX_SEED = np.iinfo(np.int32).max
+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_metadata():
-    global image_metadata
-    if os.path.exists(METADATA_FILE):
-        with open(METADATA_FILE, 'r') as f:
-            image_metadata = pd.DataFrame(json.load(f))
-    else:
-        image_metadata = pd.DataFrame(columns=['Filename', 'Prompt', 'Likes', 'Dislikes', 'Hearts', 'Created'])
+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_metadata():
-    with open(METADATA_FILE, 'w') as f:
-        json.dump(image_metadata.to_dict('records'), f)
+def save_likes_cache(cache):
+    with open(LIKES_CACHE_FILE, 'w') as f:
+        json.dump(cache, f)
 
-load_metadata()
+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
+    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]
+    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)
@@ -52,51 +55,75 @@ def save_image(img, prompt):
         'Likes': [0],
         'Dislikes': [0],
         'Hearts': [0],
-        'Created': [str(datetime.now())]
+        'Created': [datetime.now()]
     })
-    image_metadata = pd.concat([image_metadata, new_row], ignore_index=True, sort=False)
-    save_metadata()
-    logger.info(f"Saved new image: {filename}")
+    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():
-    return [(file, get_image_caption(file)) for file in image_metadata['Filename'] if os.path.exists(file)]
+    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):
-    if filename in image_metadata['Filename'].values:
-        row = image_metadata[image_metadata['Filename'] == filename].iloc[0]
-        return f"{filename}\nPrompt: {row['Prompt']}\n👍 {row['Likes']} 👎 {row['Dislikes']} ❤️ {row['Hearts']}"
+    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
+    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'])
-    save_metadata()
-    logger.info("All images deleted")
+    likes_cache = {}
+    save_likes_cache(likes_cache)
     return get_image_gallery(), image_metadata.values.tolist()
 
 def delete_image(filename):
-    global image_metadata
+    global image_metadata, likes_cache
     if filename and os.path.exists(filename):
         os.remove(filename)
         image_metadata = image_metadata[image_metadata['Filename'] != filename]
-        save_metadata()
-        logger.info(f"Deleted image: {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 image_metadata
-    if filename in image_metadata['Filename'].values:
-        image_metadata.loc[image_metadata['Filename'] == filename, vote_type] += 1
-        save_metadata()
-        logger.info(f"Updated {vote_type} count for {filename}")
-    else:
-        logger.warning(f"File {filename} not found in metadata")
+    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",
@@ -104,22 +131,29 @@ if torch.cuda.is_available():
         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")
-else:
-    DESCRIPTION += "\n<p>Running on CPU 🥶 This demo may not work on CPU.</p>"
 
+@spaces.GPU(enable_queue=True)
 def generate(
-    prompt, negative_prompt="", use_negative_prompt=False,
-    seed=0, width=1024, height=1024, guidance_scale=3, randomize_seed=False,
-    progress=gr.Progress(track_tqdm=True)
+    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),
 ):
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
+    seed = int(randomize_seed_fn(seed, randomize_seed))
+
     if not use_negative_prompt:
         negative_prompt = ""
-    
+
     images = pipe(
         prompt=prompt,
         negative_prompt=negative_prompt,
@@ -132,12 +166,29 @@ def generate(
         output_type="pil",
     ).images
     image_paths = [save_image(img, prompt) for img in images]
-    return image_paths, seed, get_image_gallery(), image_metadata.values.tolist()
+    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}
+footer {
+visibility: hidden
+}
 '''
 
 with gr.Blocks(theme=gr.themes.Soft(), css=css) as demo:
@@ -145,53 +196,154 @@ with gr.Blocks(theme=gr.themes.Soft(), css=css) as demo:
 
     with gr.Tab("Generate Images"):
         with gr.Group():
-            prompt = gr.Text(label="Prompt", placeholder="Enter your prompt")
-            run_button = gr.Button("Generate")
-        result = gr.Gallery(label="Result", columns=1, preview=True)
+            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=False)
-            negative_prompt = gr.Text(label="Negative prompt", visible=False)
-            seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0)
+            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)
-            width = gr.Slider(label="Width", minimum=512, maximum=2048, step=8, value=1024)
-            height = gr.Slider(label="Height", minimum=512, maximum=2048, step=8, value=1024)
-            guidance_scale = gr.Slider(label="Guidance Scale", minimum=0.1, maximum=20.0, step=0.1, value=7.5)
-
-    with gr.Tab("Gallery"):
-        image_gallery = gr.Gallery(label="Generated Images", columns=4, height="auto")
-        delete_image_button = gr.Button("🗑️ Delete Selected Image")
-        selected_image = gr.State(None)
+            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("Metadata and Management"):
-        metadata_df = gr.Dataframe(label="Image Metadata", headers=["Filename", "Prompt", "Likes", "Dislikes", "Hearts", "Created"])
+    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(lambda x: gr.update(visible=x), inputs=use_negative_prompt, outputs=negative_prompt)
+    use_negative_prompt.change(
+        fn=lambda x: gr.update(visible=x),
+        inputs=use_negative_prompt,
+        outputs=negative_prompt,
+        api_name=False,
+    )
 
-    run_button.click(
-        fn=generate,
-        inputs=[prompt, negative_prompt, use_negative_prompt, seed, width, height, guidance_scale, randomize_seed],
-        outputs=[result, seed, image_gallery, metadata_df]
+    delete_all_button.click(
+        fn=delete_all_images,
+        inputs=[],
+        outputs=[image_gallery, metadata_df],
     )
 
-    delete_all_button.click(fn=delete_all_images, outputs=[image_gallery, metadata_df])
-    
-    image_gallery.select(fn=lambda evt, x=None: evt, inputs=None, outputs=selected_image)
-    
-    delete_image_button.click(fn=delete_image, inputs=[selected_image], outputs=[image_gallery, metadata_df])
+    image_gallery.select(
+        fn=lambda evt: evt,
+        inputs=[],
+        outputs=[selected_image],
+    )
 
-    for button, vote_type in [(like_button, 'Likes'), (dislike_button, 'Dislikes'), (heart_button, 'Hearts')]:
-        button.click(
-            fn=lambda x, vt=vote_type: vote(x, vt) if x else (get_image_gallery(), image_metadata.values.tolist()),
-            inputs=[selected_image],
-            outputs=[image_gallery, metadata_df]
-        )
+    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=lambda: (get_image_gallery(), image_metadata.values.tolist()), outputs=[image_gallery, metadata_df])
+    demo.load(fn=update_gallery_and_metadata, outputs=[image_gallery, metadata_df])
 
 if __name__ == "__main__":
-    demo.queue(max_size=20).launch(debug=False)
+    demo.queue(max_size=20).launch(share=True, debug=False)