Super-squash branch 'main' using huggingface_hub

Co-authored-by: sbera77 <sbera77@users.noreply.huggingface.co>
Co-authored-by: notv3nom <notv3nom@users.noreply.huggingface.co>
Co-authored-by: aarbelle <aarbelle@users.noreply.huggingface.co>

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
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+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar 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 nltk
+import torch
+import spaces
+
+import gradio as gr
+
+from threading import Thread
+
+from nltk.tag import pos_tag
+from nltk.chunk import ne_chunk
+from nltk.tokenize import word_tokenize
+
+from peft import PeftModel
+from transformers import CsmForConditionalGeneration
+from transformers import AutoTokenizer, AutoModelForCausalLM, TextIteratorStreamer, AutoProcessor
+
+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)
+
+
+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."""
+
+prompt_model = AutoModelForCausalLM.from_pretrained("shb777/PromptTuner-v0.1")
+tokenizer = AutoTokenizer.from_pretrained("shb777/PromptTuner-v0.1")
+prompt_model.eval()
+
+CSM_BASE_MODEL_ID = "sesame/csm-1b"
+CSM_ADAPTER_ID = "shb777/csm-maya-exp2"
+SPEAKER_ID = 4  # Was trained on this speaker ID
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+csm_processor = AutoProcessor.from_pretrained(CSM_BASE_MODEL_ID)
+csm_model = CsmForConditionalGeneration.from_pretrained(CSM_BASE_MODEL_ID, device_map=device)
+csm_model = PeftModel.from_pretrained(csm_model, CSM_ADAPTER_ID)
+csm_model.eval()
+
+
+def extract_key_phrases(text: str) -> list: # We will highlight key phrases in the enhanced prompt
+    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))
+
+    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])
+
+    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>'
+
+    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()
+            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>'
+
+
+def enhance_prompt(user_prompt: str):
+    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
+
+    messages = [
+        {"role": "system", "content": SYSTEM_PROMPT},
+        {"role": "user", "content": user_prompt}
+    ]
+
+    prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+    inputs = tokenizer(prompt, return_tensors="pt")
+
+    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
+    }
+
+    placeholder = '<span class="placeholder-text">Your enhanced prompt will appear here</span>'
+    yield placeholder, "", gr.update(interactive=False), gr.update(interactive=False)
+
+    thread = Thread(target=prompt_model.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()
+
+    final_highlighted = highlight_matches(user_prompt, output)
+    yield final_highlighted, output, gr.update(interactive=True), gr.update(interactive=True)
+
+
+@spaces.GPU
+def generate_tts_gpu(text):
+    conversation = [
+        {"role": str(SPEAKER_ID), "content": [{"type": "text", "text": text}]},
+    ]
+
+    enc = csm_processor.apply_chat_template(
+        conversation,
+        tokenize=True,
+        return_dict=True,
+        return_tensors="pt",
+    ).to(device)
+
+    gen_kwargs = {
+        "max_new_tokens": 375,
+        # "do_sample": True,
+        # "temperature": 0.7,
+        # "depth_decoder_do_sample": True,
+        # "depth_decoder_temperature": 0.7,
+        # "depth_decoder_top_k": 20,
+        # "depth_decoder_top_p": 0.95,
+    }
+
+    audio = csm_model.generate(
+        **enc,
+        **gen_kwargs,
+        output_audio=True
+    )
+    audio_array = audio[0].to(torch.float32).cpu().numpy()
+    return (24000, audio_array)
+
+
+def text_to_speech(text: str):
+    if not text or not text.strip():
+        raise gr.Error("Please enter text to convert to speech.")
+
+    if len(text) > 200:
+        raise gr.Error("Text too long. Please limit to 200 characters or split into sentences.")
+
+    # Preprocess text - remove characters that CSM struggles with
+    text = text.replace('(', '').replace(')', '')
+    text = text.replace('"', '').replace('"', '').replace('"', '')
+    text = text.replace(';', ',')
+    text = text.replace('!', ' ')
+    text = text.replace('[', '').replace(']', '')
+    text = text.replace('/', ' ')
+
+    try:
+        audio_result = generate_tts_gpu(text)
+        return audio_result
+    except Exception as e:
+        raise gr.Error(f"Failed to generate speech: {str(e)}")
+
+with open("style.css", "r") as f: # Load CSS for styling
+    custom_css = f.read()
+
+with gr.Blocks(css=custom_css, title="TinkerSpace") as demo:
+
+    with gr.Tabs():
+        with gr.Tab("CSM Maya TTS"):
+            with gr.Row(elem_classes=["main-grid"]):
+                with gr.Column(elem_classes=["card"]):
+                    gr.HTML('<label class="form-label">Text Input</label>')
+
+                    tts_input = gr.Textbox(
+                        placeholder="Enter text to convert to speech...",
+                        lines=5,
+                        show_label=False,
+                        container=False,
+                        elem_classes=["input-textarea"]
+                    )
+
+                    with gr.Row(elem_classes=["flex gap-2 mt-6"]):
+                        generate_tts_btn = gr.Button(
+                            "Generate Speech",
+                            variant="primary",
+                            scale=2,
+                            elem_classes=["btn", "btn-primary"]
+                        )
+                        clear_tts_btn = gr.Button(
+                            "Clear",
+                            scale=1,
+                            elem_classes=["btn", "btn-secondary"]
+                        )
+
+                with gr.Column(elem_classes=["card"]):
+                    gr.HTML('<label class="form-label">Generated Audio</label>')
+
+                    tts_output = gr.Audio(
+                        label=None,
+                        show_label=False,
+                        autoplay=False,
+                        interactive=False,
+                        elem_classes=["output-container"]
+                    )
+
+            with gr.Column(elem_classes=["examples-section"]):
+                gr.Examples(
+                    examples=[
+                        ['You went to the party, even though I explicitly told you not to?'],
+                        ["With a gentle touch and a loving smile, she reassured, 'Dont worry, my love. We'll get through this together, just like we always have. I love you.'"],
+                        ["After years of work, Heisenberg finally published a ground-breaking cutting-edge research paper on quantum physics."],
+                        ['"Uh, are you sure about this?" Tim asked nervously, looking at the steep slope before them. "Whoa, it\'s higher than I thought," he continued, his voice filled with trepidation.'],
+                        ['"Shh, Lucy, shh, we mustn\'t wake your baby brother," Tom whispered, as they tiptoed past the nursery.']
+                    ],
+                    inputs=tts_input,
+                    label="Examples"
+                )
+
+            with gr.Row():
+                gr.Markdown(
+                    "Powered by [CSM Maya](https://huggingface.co/shb777/csm-maya-exp2)"
+                )
+
+        with gr.Tab("Prompt Enhancer"):
+            with gr.Row(elem_classes=["main-grid"]):
+                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"]
+                        )
+
+                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)
+
+            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"
+                )
+
+            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."
+                )
+
+    enhance_btn.click(
+        fn=enhance_prompt,
+        inputs=[input_text],
+        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]
+    )
+
+    generate_tts_btn.click(
+        fn=text_to_speech,
+        inputs=[tts_input],
+        outputs=[tts_output]
+    )
+
+    clear_tts_btn.click(
+        fn=lambda: ("", None),
+        inputs=None,
+        outputs=[tts_input, tts_output]
+    )
+
+if __name__ == "__main__":
+    demo.queue(max_size=20).launch(mcp_server=True)

requirements.txt

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+nltk
+peft
+soundfile
+transformers

style.css

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+/* ========== 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; }