Super-squash branch 'main' using huggingface_hub

Co-authored-by: notV3NOM <[email protected]>
Co-authored-by: aarbelle <[email protected]>

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
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+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
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+*.lfs.* filter=lfs diff=lfs merge=lfs -text
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+*.msgpack filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+title: TinkerSpace
+emoji: ⚡
+colorFrom: indigo
+colorTo: green
+sdk: gradio
+app_file: app.py
+pinned: true
+short_description: Demos for some my finetunes
+sdk_version: 6.2.0
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import re
+import spaces
+import gradio as gr
+
+from threading import Thread
+from transformers import AutoTokenizer, AutoModelForCausalLM, TextIteratorStreamer
+
+try:
+    import nltk
+    from nltk.tokenize import word_tokenize
+    from nltk.chunk import ne_chunk
+    from nltk.tag import pos_tag
+    NLTK_AVAILABLE = True
+except ImportError:
+    NLTK_AVAILABLE = False
+
+
+SYSTEM_PROMPT = """You are an expert creative director specializing in visual descriptions for image generation.
+
+Your task: Transform the user's concept into a rich, detailed image description while PRESERVING their core idea.
+
+IMPORTANT RULES:
+1. Keep ALL key elements (intents, entities) from the original concept
+2. Enhance with artistic details, NOT change the fundamental idea
+3. Maintain the user's intended subject, action, and setting
+
+You should elaborate on:
+• Visual composition and perspective (bird's eye, close-up, wide angle, etc.)
+• Artistic style (photorealistic, impressionist, specific artist like Van Gogh, etc.)
+• Color palette and color temperature
+• Lighting (golden hour, dramatic shadows, soft diffused, etc.)
+• Atmosphere and mood
+• Textures and materials (rough, smooth, metallic, organic, etc.)
+• Technical details (medium, brushwork, rendering style)
+• Environmental context (time of day, weather, season, era)
+• Level of detail and focus points
+
+Output format: A single, flowing paragraph that reads naturally as an image prompt."""
+
+CUDA_AVAILABLE = False
+
+models = {}
+tokenizers = {}
+
+models[False] = AutoModelForCausalLM.from_pretrained("shb777/PromptTuner-v0.1")
+tokenizers[False] = AutoTokenizer.from_pretrained("shb777/PromptTuner-v0.1")
+models[False].eval()
+
+if CUDA_AVAILABLE:
+    models[True] = AutoModelForCausalLM.from_pretrained("shb777/PromptTuner-v0.1").to('cuda')
+    tokenizers[True] = tokenizers[False]
+    models[True].eval()
+
+# Download NLTK data
+if NLTK_AVAILABLE:
+    try:
+        nltk.data.find('tokenizers/punkt')
+        nltk.data.find('taggers/averaged_perceptron_tagger')
+        nltk.data.find('chunkers/maxent_ne_chunker')
+        nltk.data.find('corpora/words')
+    except LookupError:
+        nltk.download('punkt', quiet=True)
+        nltk.download('averaged_perceptron_tagger', quiet=True)
+        nltk.download('maxent_ne_chunker', quiet=True)
+        nltk.download('words', quiet=True)
+
+
+def extract_key_phrases(text: str) -> list:
+    if not NLTK_AVAILABLE:
+        words = re.findall(r'\b[a-zA-Z]{3,}\b', text.lower())
+        return list(set(words))
+
+    phrases = []
+    try:
+        tokens = word_tokenize(text)
+        tagged = pos_tag(tokens)
+        chunks = ne_chunk(tagged)
+
+        current_phrase = []
+        for chunk in chunks:
+            if hasattr(chunk, 'label'):
+                phrase = ' '.join([token for token, _ in chunk.leaves()])
+                phrases.append(phrase.lower())
+            elif chunk[1].startswith('NN'):
+                current_phrase.append(chunk[0])
+            elif chunk[1].startswith('JJ') and current_phrase:
+                current_phrase.append(chunk[0])
+            else:
+                if current_phrase:
+                    phrases.append(' '.join(current_phrase).lower())
+                    current_phrase = []
+
+        if current_phrase:
+            phrases.append(' '.join(current_phrase).lower())
+
+        for word, tag in tagged:
+            if tag.startswith('JJ') or tag in ('RB', 'RBR', 'RBS'):
+                phrases.append(word.lower())
+
+    except Exception:
+        words = re.findall(r'\b[a-zA-Z]{3,}\b', text.lower())
+        phrases = list(set(words))
+
+    # Also include original multi-word phrases
+    multi_word = re.findall(r'\b[a-zA-Z]{3,}(?:\s+[a-zA-Z]{3,}){1,3}\b', text)
+    phrases.extend([mw.lower() for mw in multi_word])
+
+    # Sort by length (longer first) and remove duplicates
+    phrases = list(set(phrases))
+    phrases.sort(key=len, reverse=True)
+
+    return phrases[:20]
+
+
+def highlight_matches(original_input: str, enhanced_output: str) -> str:
+    if not original_input.strip():
+        return f'<p class="output-text">{enhanced_output}</p>'
+
+    key_phrases = extract_key_phrases(original_input)
+    if not key_phrases:
+        return f'<p class="output-text">{enhanced_output}</p>'
+
+    # Sort by length (longer phrases first)
+    key_phrases.sort(key=len, reverse=True)
+
+    output = enhanced_output
+    highlighted_spans = []
+
+    for phrase in key_phrases:
+        pattern = re.compile(r'\b' + re.escape(phrase) + r'\b', re.IGNORECASE)
+
+        def replace_with_highlight(match):
+            matched_text = match.group(0)
+            start = match.start()
+            # Skip if already highlighted
+            for h_start, h_end in highlighted_spans:
+                if start >= h_start and start <= h_end:
+                    return matched_text
+            highlighted_spans.append((start, match.end()))
+            return f'<mark class="highlight-keyword">{matched_text}</mark>'
+
+        output = pattern.sub(replace_with_highlight, output)
+
+    return f'<p class="output-text">{output}</p>'
+
+
+@spaces.GPU(duration=30)
+def generate_gpu(inputs, generation_kwargs):
+    return models[True].generate(**inputs, **generation_kwargs)
+
+
+def enhance_prompt(user_prompt: str, use_gpu=CUDA_AVAILABLE):
+    """Enhance the user's prompt using the AI model."""
+    # Validate input
+    if not user_prompt or not user_prompt.strip():
+        yield (
+            '<span class="placeholder-text">Please enter a prompt to enhance.</span>',
+            "",
+            gr.update(interactive=True),
+            gr.update(interactive=True)
+        )
+        return
+
+    # Prepare messages
+    messages = [
+        {"role": "system", "content": SYSTEM_PROMPT},
+        {"role": "user", "content": user_prompt}
+    ]
+
+    use_gpu = use_gpu and CUDA_AVAILABLE
+    tokenizer = tokenizers[False]
+
+    # Tokenize input
+    prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+    inputs = tokenizer(prompt, return_tensors="pt")
+
+    if use_gpu:
+        inputs = {k: v.to('cuda') for k, v in inputs.items()}
+
+    # Set up streaming
+    streamer = TextIteratorStreamer(tokenizer, skip_special_tokens=True, skip_prompt=True)
+    generation_kwargs = {
+        'max_new_tokens': 512,
+        'streamer': streamer,
+        'do_sample': True,
+        'temperature': 1,
+        'top_p': 0.95,
+        'top_k': 64
+    }
+
+    # Show loading state
+    placeholder = '<span class="placeholder-text">Your enhanced prompt will appear here</span>'
+    yield placeholder, "", gr.update(interactive=False), gr.update(interactive=False)
+
+    try:
+        # Start generation in a separate thread
+        if use_gpu:
+            thread = Thread(target=generate_gpu, kwargs={'inputs': inputs, 'generation_kwargs': generation_kwargs})
+        else:
+            thread = Thread(target=models[False].generate, kwargs={**inputs, **generation_kwargs})
+
+        thread.start()
+
+        # Stream output
+        output = ""
+        for text in streamer:
+            output += text
+            highlighted = highlight_matches(user_prompt, output)
+            yield highlighted, output, gr.update(), gr.update()
+
+    except gr.exceptions.Error as e:
+        if use_gpu:
+            gr.Warning(str(e))
+            gr.Info('Retrying with CPU')
+            inputs = {k: v.cpu() for k, v in inputs.items()}
+            thread = Thread(target=models[False].generate, kwargs={**inputs, **generation_kwargs})
+            thread.start()
+
+            output = ""
+            for text in streamer:
+                output += text
+                highlighted = highlight_matches(user_prompt, output)
+                yield highlighted, output, gr.update(), gr.update()
+        else:
+            raise gr.Error(e)
+
+    # Final output with interactive buttons restored
+    final_highlighted = highlight_matches(user_prompt, output)
+    yield final_highlighted, output, gr.update(interactive=True), gr.update(interactive=True)
+
+
+# =============================================================================
+# CSS - shadcn/ui inspired Zinc Dark Theme
+# =============================================================================
+custom_css = """
+/* ========== CSS VARIABLES ========== */
+:root {
+    --background: 240 10% 3.9%;
+    --foreground: 0 0% 98%;
+    --card: 240 10% 4.5%;
+    --card-border: 240 3.7% 18%;
+    --primary: 0 0% 98%;
+    --primary-foreground: 240 5.9% 10%;
+    --secondary: 240 3.7% 15.9%;
+    --secondary-foreground: 0 0% 98%;
+    --muted: 240 3.7% 15.9%;
+    --muted-foreground: 240 5% 64.9%;
+    --accent: 240 3.7% 15.9%;
+    --accent-foreground: 0 0% 98%;
+    --border: 240 3.7% 18%;
+    --input: 240 3.7% 18%;
+    --ring: 240 5.9% 85%;
+    --radius: 0.625rem;
+}
+
+/* ========== GLOBAL STYLES ========== */
+.gradio-container {
+    font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, "Helvetica Neue", Arial, sans-serif;
+    background: hsl(var(--background)) !important;
+    color: hsl(var(--foreground));
+}
+
+.gradio-container mark {
+    background: hsl(var(--accent) / 0.6);
+    color: hsl(var(--accent-foreground));
+    padding: 0.15em 0.35em;
+    border-radius: calc(var(--radius) - 2px);
+    font-weight: 500;
+    border: 1px solid hsl(var(--border) / 0.5);
+}
+
+footer { display: none !important; }
+
+/* ========== MARKDOWN ========== */
+.gradio-markdown {
+    color: hsl(var(--foreground)) !important;
+    font-size: 0.9375rem !important;
+    line-height: 1.6 !important;
+}
+
+.gradio-markdown:first-child {
+    margin-bottom: 2rem;
+    padding-bottom: 1.5rem;
+    border-bottom: 1px solid hsl(var(--border));
+}
+
+.gradio-markdown:last-child {
+    padding-top: 1.5rem;
+    border-top: 1px solid hsl(var(--border));
+    color: hsl(var(--muted-foreground)) !important;
+}
+
+.gradio-markdown a {
+    color: hsl(var(--foreground)) !important;
+    text-decoration: none;
+    border-bottom: 1px solid hsl(var(--border));
+    transition: border-color 0.2s ease;
+}
+
+.gradio-markdown a:hover {
+    border-color: hsl(var(--ring));
+}
+
+/* ========== LAYOUT ========== */
+.main-grid {
+    display: grid;
+    grid-template-columns: 1fr 1fr;
+    gap: 2rem;
+}
+
+@media (max-width: 768px) {
+    .main-grid { grid-template-columns: 1fr; }
+}
+
+/* ========== CARDS ========== */
+.card {
+    background: hsl(var(--card));
+    border: 1px solid hsl(var(--card-border));
+    border-radius: var(--radius);
+    padding: 1.5rem;
+    box-shadow: 0 1px 2px rgba(0, 0, 0, 0.3), 0 0 0 1px rgba(255, 255, 255, 0.02) inset;
+}
+
+/* ========== FORM ELEMENTS ========== */
+.form-label {
+    font-size: 0.875rem;
+    font-weight: 500;
+    margin-bottom: 0.5rem;
+    display: block;
+    color: hsl(var(--foreground));
+}
+
+.input-textarea {
+    width: 100%;
+    min-height: 140px;
+    padding: 0.875rem;
+    font-size: 0.9375rem;
+    line-height: 1.6;
+    background: hsl(var(--background));
+    border: 1px solid hsl(var(--input));
+    border-radius: var(--radius);
+    color: hsl(var(--foreground));
+    transition: all 0.2s ease;
+    resize: vertical;
+    box-shadow: 0 1px 2px rgba(0, 0, 0, 0.2);
+}
+
+.input-textarea::placeholder {
+    color: hsl(var(--muted-foreground) / 0.7);
+}
+
+.input-textarea:focus {
+    outline: none;
+    border-color: hsl(var(--ring));
+    box-shadow: 0 0 0 3px hsl(var(--ring) / 0.1), 0 1px 2px rgba(0, 0, 0, 0.2);
+    background: hsl(var(--background) / 0.8);
+}
+
+/* ========== BUTTONS ========== */
+.btn {
+    display: inline-flex;
+    align-items: center;
+    justify-content: center;
+    gap: 0.5rem;
+    font-size: 0.9375rem;
+    font-weight: 500;
+    padding: 0.625rem 1.25rem;
+    border-radius: var(--radius);
+    cursor: pointer;
+    transition: all 0.2s ease;
+    border: none;
+}
+
+.btn:focus-visible {
+    outline: none;
+    box-shadow: 0 0 0 2px hsl(var(--background)), 0 0 0 4px hsl(var(--ring));
+}
+
+.btn-primary {
+    background: hsl(var(--primary));
+    color: hsl(var(--primary-foreground));
+    box-shadow: 0 1px 2px rgba(0, 0, 0, 0.2), 0 0 0 1px rgba(255, 255, 255, 0.05) inset;
+}
+
+.btn-primary:hover {
+    opacity: 0.95;
+    box-shadow: 0 2px 4px rgba(0, 0, 0, 0.25), 0 0 0 1px rgba(255, 255, 255, 0.08) inset;
+}
+
+.btn-primary:active {
+    transform: translateY(1px);
+}
+
+.btn-primary:disabled {
+    opacity: 0.5;
+    cursor: not-allowed;
+}
+
+.btn-secondary {
+    background: hsl(var(--secondary));
+    color: hsl(var(--secondary-foreground));
+    border: 1px solid hsl(var(--border));
+    box-shadow: 0 1px 2px rgba(0, 0, 0, 0.2);
+}
+
+.btn-secondary:hover {
+    background: hsl(var(--secondary) / 0.8);
+    border-color: hsl(var(--muted-foreground) / 0.5);
+}
+
+.btn-secondary:active {
+    transform: translateY(1px);
+}
+
+/* ========== OUTPUT CONTAINER ========== */
+.output-container {
+    min-height: 140px;
+    padding: 0.875rem;
+    border: 1px solid hsl(var(--input));
+    border-radius: var(--radius);
+    background: hsl(var(--background));
+    box-shadow: 0 1px 2px rgba(0, 0, 0, 0.15), 0 0 0 1px rgba(255, 255, 255, 0.02) inset;
+}
+
+.output-text {
+    color: hsl(var(--foreground));
+    font-size: 0.9375rem;
+    line-height: 1.75;
+    margin: 0;
+}
+
+.placeholder-text {
+    color: hsl(var(--muted-foreground));
+}
+
+.highlight-keyword {
+    background: hsl(var(--accent) / 0.6);
+    color: hsl(var(--accent-foreground));
+    padding: 0.15em 0.35em;
+    border-radius: calc(var(--radius) - 2px);
+    font-weight: 500;
+    border: 1px solid hsl(var(--border) / 0.5);
+}
+
+/* ========== EXAMPLES ========== */
+.examples-section {
+    padding: 1.5rem;
+    background: hsl(var(--card));
+    border: 1px solid hsl(var(--card-border));
+    border-radius: var(--radius);
+    box-shadow: 0 1px 2px rgba(0, 0, 0, 0.2), 0 0 0 1px rgba(255, 255, 255, 0.02) inset;
+}
+
+/* ========== SPACING UTILITIES ========== */
+.mt-6 { margin-top: 1.5rem; }
+.flex { display: flex; }
+.gap-2 { gap: 0.5rem; }
+"""
+
+# =============================================================================
+# Gradio Interface
+# =============================================================================
+with gr.Blocks(css=custom_css, title="Prompt Enhancer") as demo:
+    # Header
+    with gr.Row():
+        gr.Markdown("Transform your creative ideas into detailed, vivid prompts for AI image generation.")
+
+    # Main content - two column layout
+    with gr.Row(elem_classes=["main-grid"]):
+        # Input column
+        with gr.Column(elem_classes=["card"]):
+            gr.HTML('<label class="form-label">Input Prompt</label>')
+
+            input_text = gr.Textbox(
+                placeholder="Describe your image concept... e.g., fox, red tail, blue moon, clouds",
+                lines=5,
+                show_label=False,
+                autofocus=True,
+                container=False,
+                elem_classes=["input-textarea"]
+            )
+
+            with gr.Row(elem_classes=["flex gap-2 mt-6"]):
+                enhance_btn = gr.Button(
+                    "Enhance Prompt",
+                    variant="primary",
+                    scale=2,
+                    elem_classes=["btn", "btn-primary"]
+                )
+                clear_btn = gr.Button(
+                    "Clear",
+                    scale=1,
+                    elem_classes=["btn", "btn-secondary"]
+                )
+
+        # Output column
+        with gr.Column(elem_classes=["card"]):
+            gr.HTML('<label class="form-label">Enhanced Prompt</label>')
+
+            output_html = gr.HTML(
+                value='<span class="placeholder-text">Your enhanced prompt will appear here</span>',
+                elem_classes=["output-container"]
+            )
+
+            raw_output = gr.Textbox(visible=False)
+
+    # Examples section
+    with gr.Column(elem_classes=["examples-section"]):
+        gr.Examples(
+            examples=[
+                ["fox, red tail, blue moon, clouds"],
+                ["room with french window, cozy morning vibes, minimal"],
+                ["anime style, sunset, japan"]
+            ],
+            inputs=input_text,
+            label="Examples"
+        )
+
+    # Footer
+    with gr.Row():
+        gr.Markdown(
+            "Powered by [PromptTuner](https://huggingface.co/shb777/PromptTuner-v0.1), "
+            "a finetuned gemma3-270M model specifically designed to enhance text prompts "
+            "for text-to-image generation."
+        )
+
+    # =============================================================================
+    # Event Handlers
+    # =============================================================================
+    enhance_btn.click(
+        fn=enhance_prompt,
+        inputs=[input_text, gr.State(False)],
+        outputs=[output_html, raw_output, enhance_btn, clear_btn]
+    )
+
+    clear_btn.click(
+        fn=lambda: (
+            "",
+            '<span class="placeholder-text">Your enhanced prompt will appear here</span>',
+            "",
+            gr.update(interactive=True),
+            gr.update(interactive=True)
+        ),
+        inputs=None,
+        outputs=[input_text, output_html, raw_output, enhance_btn, clear_btn]
+    )
+
+if __name__ == "__main__":
+    demo.queue(max_size=20).launch(mcp_server=True)

requirements.txt

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+transformers
+nltk