Update app.py

app.py

@@ -2,128 +2,54 @@ import os
 import random
 import uuid
 import base64
+import json
+import re
 import gradio as gr
 import numpy as np
-from PIL import Image
-import spaces
+import pandas as pd
 import torch
-import glob
+from PIL import Image
 from datetime import datetime
-import pandas as pd
-import json
-import re
-
 from diffusers import StableDiffusionXLPipeline, EulerAncestralDiscreteScheduler
+import anthropic
 
-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'])
+# ============================================================
+# === GLOBALS & DATA STORAGE FILES
+# ============================================================
 LIKES_CACHE_FILE = "likes_cache.json"
+LOG_CACHE_FILE = "log_cache.json"
+QUOTE_CACHE_FILE = "quotes_cache.json"
+
+STATIC_URL_PREFIX = "https://huggingface.co/spaces/awacke1/dalle-3-xl-lora-v2/file="
 
-def load_likes_cache():
-    if os.path.exists(LIKES_CACHE_FILE):
-        with open(LIKES_CACHE_FILE, 'r') as f:
+# Initialize caches / load from JSON
+def load_json(file):
+    if os.path.exists(file):
+        with open(file, 'r', encoding='utf-8') 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 save_json(file, data):
+    with open(file, 'w', encoding='utf-8') as f:
+        json.dump(data, f, indent=4)
 
-def get_random_style():
-    styles = [
-        "Impressionist", "Cubist", "Surrealist", "Abstract Expressionist",
-        "Pop Art", "Minimalist", "Baroque", "Art Nouveau", "Pointillist", "Fauvism"
-    ]
-    return random.choice(styles)
+likes_cache = load_json(LIKES_CACHE_FILE) or {}
+chat_logs = load_json(LOG_CACHE_FILE) if os.path.exists(LOG_CACHE_FILE) else []
+quotes = load_json(QUOTE_CACHE_FILE) if os.path.exists(QUOTE_CACHE_FILE) else []
 
-MAX_SEED = np.iinfo(np.int32).max
+# DataFrame for images
+image_metadata = pd.DataFrame(columns=['Filename','Prompt','Likes','Dislikes','Hearts','Created'])
 
-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
+# ============================================================
+# === ANTHROPIC CLIENT (Claude)
+# ============================================================
+anthropic_api_key = os.environ.get("ANTHROPIC_API_KEY", None)
+claude_client = anthropic.Anthropic(api_key=anthropic_api_key) if anthropic_api_key else None
 
+# ============================================================
+# === IMAGE PIPELINE
+# ============================================================
+pipe = None
 if torch.cuda.is_available():
     pipe = StableDiffusionXLPipeline.from_pretrained(
         "fluently/Fluently-XL-v4",
@@ -131,26 +57,60 @@ 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")
 
-@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),
+MAX_SEED = np.iinfo(np.int32).max
+
+# ============================================================
+# === HELPER FUNCTIONS
+# ============================================================
+def randomize_seed_fn(seed: int, randomize_seed: bool):
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+    return int(seed)
+
+def sanitize_prompt(prompt):
+    return re.sub(r'[^\w\s-]', '', prompt.lower())[:50]
+
+def save_image_locally(img, prompt):
+    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+    safe_prompt = sanitize_prompt(prompt)
+    filename = f"{timestamp}_{safe_prompt}.png"
+    img.save(filename)
+    if filename not in likes_cache:
+        likes_cache[filename] = {'likes': 0, 'dislikes': 0, 'hearts': 0}
+        save_json(LIKES_CACHE_FILE, likes_cache)
+    global image_metadata
+    new_row = {
+        'Filename': filename,
+        'Prompt': prompt,
+        'Likes': 0,
+        'Dislikes': 0,
+        'Hearts': 0,
+        'Created': str(datetime.now())
+    }
+    image_metadata = pd.concat([image_metadata, pd.DataFrame([new_row])], ignore_index=True)
+    return filename
+
+def log_input_output(user_input, model_output, link=""):
+    global chat_logs
+    chat_logs.append({
+        "timestamp": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
+        "input": user_input,
+        "output": model_output,
+        "file_link": link
+    })
+    save_json(LOG_CACHE_FILE, chat_logs)
+
+def generate_image(
+    prompt, negative_prompt, use_negative_prompt, seed, width, height, guidance_scale, randomize_seed
 ):
-    seed = int(randomize_seed_fn(seed, randomize_seed))
+    if pipe is None:
+        return ["No GPU available, cannot generate images."], 0, [], [], []
 
+    seed = randomize_seed_fn(seed, randomize_seed)
     if not use_negative_prompt:
         negative_prompt = ""
 
@@ -165,185 +125,183 @@ def generate(
         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()
+    filenames = []
+    for img in images:
+        fname = save_image_locally(img, prompt)
+        filenames.append(fname)
+
+    links = [f"{STATIC_URL_PREFIX}{f}" for f in filenames]
+    # Log the generation
+    log_input_output(user_input=prompt, model_output="(image generated)", link=", ".join(links))
+
+    # Return Gradio objects
+    return filenames, seed, links, get_image_gallery(), image_metadata.values.tolist()
+
+def get_image_gallery():
+    return [
+        (row["Filename"], f"{row['Filename']}\nPrompt: {row['Prompt']}\n👍 {row['Likes']} 👎 {row['Dislikes']} ❤️ {row['Hearts']}")
+        for _, row in image_metadata.iterrows()
+        if os.path.exists(row["Filename"])
+    ]
+
+def vote_image(filename, vote_type):
+    if filename and filename in likes_cache:
+        likes_cache[filename][vote_type] += 1
+        save_json(LIKES_CACHE_FILE, likes_cache)
+        idx = image_metadata.index[image_metadata['Filename'] == filename]
+        if not idx.empty:
+            image_metadata.at[idx, vote_type.capitalize()] = image_metadata.at[idx, vote_type.capitalize()] + 1
+    return get_image_gallery(), image_metadata.values.tolist()
+
+def delete_image(filename):
+    if filename and os.path.exists(filename):
+        os.remove(filename)
+    if filename in likes_cache:
+        del likes_cache[filename]
+        save_json(LIKES_CACHE_FILE, likes_cache)
+    global image_metadata
+    image_metadata = image_metadata[image_metadata['Filename'] != filename]
+    return get_image_gallery(), image_metadata.values.tolist()
+
+def delete_all_images():
+    global image_metadata, likes_cache
+    for f in image_metadata["Filename"].tolist():
+        if os.path.exists(f):
+            os.remove(f)
+    image_metadata = pd.DataFrame(columns=['Filename','Prompt','Likes','Dislikes','Hearts','Created'])
+    likes_cache.clear()
+    save_json(LIKES_CACHE_FILE, likes_cache)
+    return get_image_gallery(), image_metadata.values.tolist()
+
+# === QUOTES Demo (Optional) ===
+def add_quote(q):
+    if q.strip():
+        quotes.append({
+            "text": q,
+            "likes": 0,
+            "created": datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+        })
+        save_json(QUOTE_CACHE_FILE, quotes)
+    return [[idx, itm["text"], itm["likes"], itm["created"]] for idx, itm in enumerate(quotes)]
+
+def like_quote(idx):
+    if 0 <= idx < len(quotes):
+        quotes[idx]["likes"] += 1
+    save_json(QUOTE_CACHE_FILE, quotes)
+    return [[i, itm["text"], itm["likes"], itm["created"]] for i, itm in enumerate(quotes)]
+
+# === CLAUDE Chat ===
+def chat_claude(user_message):
+    if not claude_client:
+        return "No Anthropic API key configured."
+    if not user_message.strip():
+        return "Empty message."
+    resp = claude_client.messages.create(
+        model="claude-3-sonnet-20240229",
+        max_tokens=1000,
+        messages=[{"role": "user", "content": user_message}],
+    )
+    text = resp.content[0].text
+    log_input_output(user_input=user_message, model_output=text, link="")
+    return text
+
+# === Refresh gallery + DF
+def refresh_gallery_and_df():
+    return gr.update(value=get_image_gallery()), gr.update(value=image_metadata.values.tolist())
+
+# ============================================================
+# === BUILD GRADIO UI
+# ============================================================
+DESCRIPTION = """# 🎨 ArtForge & Claude Chat
+Generate AI art, chat with Claude, log everything, and vote on images.
+"""
 
 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."
+    "Futuristic cityscape in neon lighting",
+    "Cute cat wearing a wizard hat",
+    "Surreal landscape with floating islands",
 ]
 
-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:
+with gr.Blocks(css=".gradio-container {max-width: 1024px !important}") 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)
+            prompt = gr.Text(label="Prompt", max_lines=1)
+            run_button = gr.Button("Run")
+        result = gr.Gallery(label="Result", columns=1, preview=True)
+        use_negative_prompt = gr.Checkbox(label="Use negative prompt", value=True)
+        negative_prompt = gr.Text(
+            label="Negative prompt",
+            lines=3,
+            value="(deformed, distorted:1.3), poorly drawn, bad anatomy",
+        )
+        seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0)
+        randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+        width = gr.Slider(label="Width", minimum=512, maximum=2048, step=64, value=1024)
+        height = gr.Slider(label="Height", minimum=512, maximum=2048, step=64, value=1024)
+        guidance_scale = gr.Slider(label="Guidance Scale", minimum=1, maximum=20, step=0.5, value=7)
+        run_button.click(
+            fn=generate_image,
+            inputs=[prompt, negative_prompt, use_negative_prompt, seed, width, height, guidance_scale, randomize_seed],
+            outputs=[result, seed, gr.HTML(visible=False), gr.Gallery(), gr.Dataframe()],
+            api_name="run"
+        )
+        gr.Examples(examples=examples, inputs=prompt)
+
+    with gr.Tab("Chat with Claude"):
+        claude_input = gr.Textbox(label="Your Message")
+        claude_output = gr.Textbox(label="Claude's Reply", lines=4)
+        send_claude = gr.Button("Send to Claude")
+        send_claude.click(chat_claude, inputs=claude_input, outputs=claude_output)
+
+    with gr.Tab("Logs & Management"):
+        with gr.Accordion("All Logs", open=False):
+            logs_data = gr.Dataframe(
+                value=pd.DataFrame(chat_logs),
+                label="Input/Output Logs",
+                interactive=False,
+                wrap=True
+            )
 
-    with gr.Tab("Metadata and Management"):
+    with gr.Tab("Gallery & Voting"):
+        image_gallery = gr.Gallery(label="Generated Images", columns=4)
         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],
-    )
+        selected_image = gr.State()
+        with gr.Row():
+            like_button = gr.Button("👍 Like")
+            dislike_button = gr.Button("👎 Dislike")
+            heart_button = gr.Button("❤️ Heart")
+            delete_image_button = gr.Button("🗑️ Delete Image")
+            delete_all_button = gr.Button("🗑️ Delete All")
+        image_gallery.select(fn=lambda evt: evt, inputs=[], outputs=[selected_image])
+        like_button.click(fn=lambda x: vote_image(x, 'likes'), inputs=selected_image, outputs=[image_gallery, metadata_df])
+        dislike_button.click(fn=lambda x: vote_image(x, 'dislikes'), inputs=selected_image, outputs=[image_gallery, metadata_df])
+        heart_button.click(fn=lambda x: vote_image(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])
+        delete_all_button.click(fn=delete_all_images, outputs=[image_gallery, metadata_df])
+
+    with gr.Tab("Quotes (Optional)"):
+        quote_input = gr.Textbox(label="Enter a quote")
+        add_q_button = gr.Button("Add Quote")
+        quote_df = gr.Dataframe(value=[(idx, q['text'], q['likes'], q['created']) for idx,q in enumerate(quotes)],
+                                headers=["Index","Text","Likes","Created"], interactive=False)
+        selected_quote = gr.Number(label="Index to Like")
+        like_q_button = gr.Button("Like Quote")
+        add_q_button.click(fn=add_quote, inputs=quote_input, outputs=quote_df)
+        like_q_button.click(fn=like_quote, inputs=selected_quote, outputs=quote_df)
+
+    demo.load(fn=refresh_gallery_and_df, outputs=[image_gallery, metadata_df])
 
-    dislike_button.click(
-        fn=lambda x: vote(x, 'dislikes'),
-        inputs=[selected_image],
-        outputs=[image_gallery, metadata_df],
-    )
+if __name__ == "__main__":
+    demo.queue(max_size=20).launch()
 
-    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)