Update app.py

app.py

@@ -1,381 +1,227 @@
+import streamlit as st
+import anthropic
 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 torch
 import glob
-from datetime import datetime
-import pandas as pd
-import anthropic
+import json
 import pytz
-
-from diffusers import StableDiffusionXLPipeline, EulerAncestralDiscreteScheduler
-
-DESCRIPTION = """# 🎨 ArtForge + Claude Chat
-Create, curate, and compete with AI-generated art. Chat with Claude. Manage quotes. Track logs. Enjoy!
-"""
-
-# ------------------ Global Files & Constants ------------------ #
-LIKES_CACHE_FILE = "likes_cache.json"
-LOG_CACHE_FILE = "log_cache.json"
-QUOTE_CACHE_FILE = "quotes_cache.json"
-
-# For serving static content:
-URL_PREFIX = "https://huggingface.co/spaces/awacke1/dalle-3-xl-lora-v2/file="
-
-# ------------------ Load/Save Helpers ------------------ #
-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_json(file, data):
-    with open(file, 'w', encoding='utf-8') as f:
-        json.dump(data, f, indent=4)
-
-likes_cache = load_json(LIKES_CACHE_FILE) or {}
-all_logs = load_json(LOG_CACHE_FILE) if os.path.exists(LOG_CACHE_FILE) else []
-quotes_cache = load_json(QUOTE_CACHE_FILE) if os.path.exists(QUOTE_CACHE_FILE) else []
-
-# Image metadata
-image_metadata = pd.DataFrame(columns=['Filename', 'Prompt', 'Likes', 'Dislikes', 'Hearts', 'Created'])
-
-# ---------- Anthropic (Claude) Setup (optional) ---------- #
-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
-
-# ------------------ Logging of Inputs/Outputs ------------------ #
-def log_input_output(user_input, model_output, file_link=""):
-    global all_logs
-    timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
-    new_entry = {
-        "timestamp": timestamp,
-        "input": user_input,
-        "output": model_output,
-        "file_link": file_link
-    }
-    all_logs.append(new_entry)
-    save_json(LOG_CACHE_FILE, all_logs)
-
-# ------------------ Votes & Images ------------------ #
-def load_likes_cache():
-    return likes_cache
-
-def save_likes_cache():
-    save_json(LIKES_CACHE_FILE, likes_cache)
-
-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']
-        row = image_metadata.loc[image_metadata['Filename'] == filename]
-        if not row.empty:
-            prompt = row.iloc[0]['Prompt']
-            return f"{filename}\nPrompt: {prompt}\n👍 {likes} 👎 {dislikes} ❤️ {hearts}"
-    return filename
-
-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 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.clear()
-    save_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()
-    return get_image_gallery(), image_metadata.values.tolist()
-
-def vote(filename, vote_type):
-    global likes_cache, image_metadata
-    if filename in likes_cache:
-        likes_cache[filename][vote_type.lower()] += 1
-        save_likes_cache()
-        # Reflect in dataframe
-        row_idx = image_metadata[image_metadata['Filename'] == filename].index
-        if not row_idx.empty:
-            col = vote_type.capitalize()
-            curr_val = image_metadata.at[row_idx[0], col]
-            image_metadata.at[row_idx[0], col] = curr_val + 1
-    return get_image_gallery(), image_metadata.values.tolist()
-
-# ------------------ Image Generation (Original Code) ------------------ #
-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>"
-
-# Load pipeline
-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")
-else:
-    pipe = None
-
-def randomize_seed_fn(seed: int, randomize_seed: bool):
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-    return seed
-
-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]
-    safe_prompt = re.sub(r'[-\s]+', '-', safe_prompt).strip('-')
-    filename = f"{timestamp}_{safe_prompt}.png"
-    img.save(filename)
-    # Add metadata row
-    new_row = {
-        'Filename': filename,
-        'Prompt': prompt,
-        'Likes': 0,
-        'Dislikes': 0,
-        'Hearts': 0,
-        'Created': datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+from datetime import datetime
+from streamlit.components.v1 import html
+from PIL import Image
+import re
+from urllib.parse import quote
+
+# 1. Configuration
+Site_Name = '🤖🧠Ant🔬Claude📝'
+title="🤖🧠Ant🔬Claude📝"
+helpURL='https://huggingface.co/awacke1'
+bugURL='https://huggingface.co/spaces/awacke1'
+icons='🤖🧠🔬📝'
+st.set_page_config(
+    page_title=title,
+    page_icon=icons,
+    layout="wide",
+    #initial_sidebar_state="expanded",
+    initial_sidebar_state="auto",
+    menu_items={
+        'Get Help': helpURL,
+        'Report a bug': bugURL,
+        'About': title
     }
-    image_metadata.loc[len(image_metadata)] = new_row
-    likes_cache[filename] = {'likes': 0, 'dislikes': 0, 'hearts': 0}
-    save_likes_cache()
-    return filename
-
-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),
-):
-    if pipe is None:
-        return ["Error: No GPU or pipeline not loaded"], 0, [], get_image_gallery(), image_metadata.values.tolist()
-
-    seed = 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]
-    # Build static-content file links
-    file_links = [f"{URL_PREFIX}{p}" for p in image_paths]
-
-    # Log this generation
-    log_input_output(user_input=prompt, model_output="(image generated)", file_link=", ".join(file_links))
+)
 
-    return image_paths, seed, file_links, get_image_gallery(), image_metadata.values.tolist()
+# Set up the Anthropic client
+client = anthropic.Anthropic(api_key=os.environ.get("ANTHROPIC_API_KEY"))
 
-# Some example prompts
-def get_random_style():
-    styles = [
-        "Impressionist", "Cubist", "Surrealist", "Abstract Expressionist",
-        "Pop Art", "Minimalist", "Baroque", "Art Nouveau", "Pointillist", "Fauvism"
-    ]
-    return random.choice(styles)
+# Initialize session state
+if "chat_history" not in st.session_state:
+    st.session_state.chat_history = []
 
-examples = [
-    f"{get_random_style()} painting of a majestic lighthouse...",
-    f"{get_random_style()} still life with a pair of vintage eyeglasses...",
-    f"{get_random_style()} depiction of a rustic wooden stool...",
-]
+# Function to get file download link
+def get_download_link(file_path):
+    with open(file_path, "rb") as file:
+        contents = file.read()
+    b64 = base64.b64encode(contents).decode()
+    file_name = os.path.basename(file_path)
+    return f'<a href="data:file/txt;base64,{b64}" download="{file_name}">Download {file_name}</a>'
 
-# ------------------ Claude Chat (Added from Streamlit) ------------------ #
-def chat_claude(user_input: str):
-    if not claude_client:
-        # If no API key or no client
-        return "Claude not available. Set ANTHROPIC_API_KEY."
-    if not user_input.strip():
-        return "No input provided."
-
-    response = claude_client.messages.create(
-        model="claude-3-sonnet-20240229",
-        max_tokens=1000,
-        messages=[{"role": "user", "content": user_input}]
-    )
-    text = response.content[0].text
-    log_input_output(user_input, text, "")
-    return text
-
-# ------------------ Quotes Feature (Short Version) ------------------ #
-def get_quotes_df():
-    return pd.DataFrame(quotes_cache) if quotes_cache else pd.DataFrame(columns=["text", "likes", "created"])
-
-def add_quote(quote_text):
-    if not quote_text.strip():
-        return get_quotes_df()
+# Function to generate filename
+def generate_filename(prompt, file_type):
     central = pytz.timezone('US/Central')
-    dt_str = datetime.now(central).strftime("%Y-%m-%d %H:%M:%S")
-    quotes_cache.append({"text": quote_text, "likes": 0, "created": dt_str})
-    save_json(QUOTE_CACHE_FILE, quotes_cache)
-    return get_quotes_df()
-
-def like_quote(index):
-    # index from DataFrame row index
-    if 0 <= index < len(quotes_cache):
-        quotes_cache[index]["likes"] += 1
-        save_json(QUOTE_CACHE_FILE, quotes_cache)
-    return get_quotes_df()
-
-# ------------------ Build Gradio Interface ------------------ #
-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), ...",
-                placeholder="Enter negative prompt"
-            )
-            seed = gr.Slider(
-                label="Seed",
-                minimum=0,
-                maximum=MAX_SEED,
-                step=1,
-                value=0
+    safe_date_time = datetime.now(central).strftime("%m%d_%H%M")
+    replaced_prompt = prompt.replace(" ", "_").replace("\n", "_")
+    safe_prompt = "".join(x for x in replaced_prompt if x.isalnum() or x == "_")[:90]
+    return f"{safe_date_time}_{safe_prompt}.{file_type}"
+
+# Function to create file
+def create_file(filename, prompt, response, should_save=True):
+    if not should_save:
+        return
+    with open(filename, 'w', encoding='utf-8') as file:
+        file.write(prompt + "\n\n" + response)
+
+# Function to load file content
+def load_file(file_name):
+    with open(file_name, "r", encoding='utf-8') as file:
+        content = file.read()
+    return content
+
+# Function to display glossary entity
+def display_glossary_entity(k):
+    search_urls = {
+        "🚀🌌ArXiv": lambda k: f"/?q={quote(k)}",
+        "📖": lambda k: f"https://en.wikipedia.org/wiki/{quote(k)}",
+        "🔍": lambda k: f"https://www.google.com/search?q={quote(k)}",
+        "🎥": lambda k: f"https://www.youtube.com/results?search_query={quote(k)}",
+    }
+    links_md = ' '.join([f"[{emoji}]({url(k)})" for emoji, url in search_urls.items()])
+    st.markdown(f"**{k}** <small>{links_md}</small>", unsafe_allow_html=True)
+
+# Function to create zip of files
+def create_zip_of_files(files):
+    import zipfile
+    zip_name = "all_files.zip"
+    with zipfile.ZipFile(zip_name, 'w') as zipf:
+        for file in files:
+            zipf.write(file)
+    return zip_name
+
+# Function to get zip download link
+def get_zip_download_link(zip_file):
+    with open(zip_file, 'rb') as f:
+        data = f.read()
+    b64 = base64.b64encode(data).decode()
+    href = f'<a href="data:application/zip;base64,{b64}" download="{zip_file}">Download All</a>'
+    return href
+
+# Function to create HTML for autoplaying and looping video
+def get_video_html(video_path, width="100%"):
+    video_url = f"data:video/mp4;base64,{base64.b64encode(open(video_path, 'rb').read()).decode()}"
+    return f'''
+    <video width="{width}" controls autoplay muted loop>
+        <source src="{video_url}" type="video/mp4">
+        Your browser does not support the video tag.
+    </video>
+    '''
+
+# Function to create HTML for audio player
+def get_audio_html(audio_path, width="100%"):
+    audio_url = f"data:audio/mpeg;base64,{base64.b64encode(open(audio_path, 'rb').read()).decode()}"
+    return f'''
+    <audio controls style="width: {width};">
+        <source src="{audio_url}" type="audio/mpeg">
+        Your browser does not support the audio element.
+    </audio>
+    '''
+
+
+
+# Streamlit app
+def main():
+    
+    # Sidebar
+    st.sidebar.title("🤖🧠Ant🔬Claude📝🔊 - Anthropic Claude 3.5 Sonnet")
+    
+    # File management
+    all_files = glob.glob("*.md")
+    all_files = [file for file in all_files if len(os.path.splitext(file)[0]) >= 10]
+    all_files.sort(key=lambda x: (os.path.splitext(x)[1], x), reverse=True)
+
+    if st.sidebar.button("🗑 Delete All"):
+        for file in all_files:
+            os.remove(file)
+        st.rerun()
+
+    if st.sidebar.button("⬇️ Download All"):
+        zip_file = create_zip_of_files(all_files)
+        st.sidebar.markdown(get_zip_download_link(zip_file), unsafe_allow_html=True)
+
+    for file in all_files:
+        col1, col2, col3, col4 = st.sidebar.columns([1,3,1,1])
+        with col1:
+            if st.button("🌐", key="view_"+file):
+                st.session_state.current_file = file
+                st.session_state.file_content = load_file(file)
+        with col2:
+            st.markdown(get_download_link(file), unsafe_allow_html=True)
+        with col3:
+            if st.button("📂", key="edit_"+file):
+                st.session_state.current_file = file
+                st.session_state.file_content = load_file(file)
+        with col4:
+            if st.button("🗑", key="delete_"+file):
+                os.remove(file)
+                st.rerun()
+
+    # Main content area
+    user_input = st.text_area("Enter your message:", height=100)
+    
+    if st.button("Send"):
+        if user_input:
+            response = client.messages.create(
+                model="claude-3-sonnet-20240229",
+                max_tokens=1000,
+                messages=[
+                    {"role": "user", "content": user_input}
+                ]
             )
-            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-            with gr.Row():
-                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=3)
-
-        # Button triggers generate function
-        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), gr.Gallery(), gr.Dataframe()],
-        )
-
-        gr.Examples(
-            examples=examples,
-            inputs=prompt,
-            outputs=[result, seed],
-            fn=generate,
-            cache_examples=False,
-        )
-
-    with gr.Tab("Gallery & 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)
-        metadata_df = gr.Dataframe(
-            label="Image Metadata",
-            headers=["Filename", "Prompt", "Likes", "Dislikes", "Hearts", "Created"],
-            interactive=False
-        )
-        delete_all_button = gr.Button("🗑️ Delete All Images")
-
-        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])
-
-    with gr.Tab("Chat Claude"):
-        claude_input = gr.Textbox(label="Your message:")
-        claude_output = gr.Textbox(label="Claude's reply:", lines=4)
-        claude_button = gr.Button("Send to Claude")
-        claude_button.click(fn=chat_claude, inputs=claude_input, outputs=claude_output)
-
-    with gr.Tab("Quotes"):
-        with gr.Row():
-            quote_text = gr.Textbox(label="Add a quote:")
-            add_quote_btn = gr.Button("Add Quote")
-        quote_data = gr.Dataframe(headers=["text","likes","created"], interactive=False)
-        with gr.Row():
-            row_index = gr.Number(label="Index to like:")
-            like_btn = gr.Button("Like Quote")
-
-        add_quote_btn.click(fn=add_quote, inputs=quote_text, outputs=quote_data)
-        like_btn.click(fn=like_quote, inputs=row_index, outputs=quote_data)
-
-    with gr.Tab("Logs"):
-        gr.Markdown("#### All logged inputs & outputs")
-        logs_data = gr.Dataframe(
-            label="Log Data",
-            value=pd.DataFrame(all_logs),
-            interactive=False
-        )
-
-    def update_gallery_and_metadata():
-        return gr.update(value=get_image_gallery()), gr.update(value=image_metadata.values.tolist())
-
-    demo.load(fn=update_gallery_and_metadata, outputs=[image_gallery, metadata_df])
+            
+            st.write("Claude's response:")
+            st.write(response.content[0].text)
+
+            # Save response to file
+            filename = generate_filename(user_input, "md")
+            create_file(filename, user_input, response.content[0].text)
+
+            # Add to chat history
+            st.session_state.chat_history.append({"user": user_input, "claude": response.content[0].text})
+
+    # Display chat history
+    st.subheader("Chat History")
+    for chat in st.session_state.chat_history:
+        st.text_area("You:", chat["user"], height=100, disabled=True)
+        st.text_area("Claude:", chat["claude"], height=200, disabled=True)
+        st.markdown("---")
+
+    # File content viewer/editor
+    if hasattr(st.session_state, 'current_file'):
+        st.subheader(f"Editing: {st.session_state.current_file}")
+        new_content = st.text_area("File Content:", st.session_state.file_content, height=300)
+        if st.button("Save Changes"):
+            with open(st.session_state.current_file, 'w', encoding='utf-8') as file:
+                file.write(new_content)
+            st.success("File updated successfully!")
+
+    # Image Gallery
+    st.subheader("Image Gallery")
+    image_files = glob.glob("*.png") + glob.glob("*.jpg") + glob.glob("*.jpeg")
+    image_cols = st.slider("Image Gallery Columns", min_value=1, max_value=5, value=3)
+    cols = st.columns(image_cols)
+    for idx, image_file in enumerate(image_files):
+        with cols[idx % image_cols]:
+            img = Image.open(image_file)
+            st.image(img, caption=image_file, use_column_width=True)
+            display_glossary_entity(os.path.splitext(image_file)[0])
+
+    # Video Gallery
+    st.subheader("Video Gallery")
+    video_files = glob.glob("*.mp4")
+    video_cols = st.slider("Video Gallery Columns", min_value=1, max_value=5, value=3)
+    cols = st.columns(video_cols)
+    for idx, video_file in enumerate(video_files):
+        with cols[idx % video_cols]:
+            st.markdown(get_video_html(video_file, width="100%"), unsafe_allow_html=True)
+            display_glossary_entity(os.path.splitext(video_file)[0])
+
+    # Audio Gallery
+    st.subheader("Audio Gallery")
+    audio_files = glob.glob("*.mp3") + glob.glob("*.wav")
+    audio_cols = st.slider("Audio Gallery Columns", min_value=1, max_value=5, value=3)
+    cols = st.columns(audio_cols)
+    for idx, audio_file in enumerate(audio_files):
+        with cols[idx % audio_cols]:
+            st.markdown(get_audio_html(audio_file, width="100%"), unsafe_allow_html=True)
+            display_glossary_entity(os.path.splitext(audio_file)[0])
 
 if __name__ == "__main__":
-    demo.queue(max_size=20).launch(share=True, debug=False)
+    main()