Update app.py

README.md

@@ -1,13 +1,13 @@
 ---
-title: TinkerSpace
-emoji: ⚡
+title: Granite Vision 3.1 2B
+emoji: 👀
 colorFrom: indigo
 colorTo: green
 sdk: gradio
+sdk_version: 5.15.0
 app_file: app.py
-pinned: true
-short_description: Demos for some my finetunes
-sdk_version: 6.2.0
+pinned: false
+license: apache-2.0
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -1,549 +1,122 @@
-import re
 import spaces
+import random
+import torch
 import gradio as gr
+from transformers import LlavaNextProcessor, LlavaNextForConditionalGeneration
+
+model_path = "ibm-granite/granite-vision-3.1-2b-preview"
+processor = LlavaNextProcessor.from_pretrained(model_path, use_fast=True)
+model = LlavaNextForConditionalGeneration.from_pretrained(model_path, torch_dtype="auto", device_map="auto")
+
+def get_text_from_content(content):
+    texts = []
+    for item in content:
+        if item["type"] == "text":
+            texts.append(item["text"])
+        elif item["type"] == "image":
+            texts.append("<image>")
+    return " ".join(texts)
+
+@spaces.GPU
+def chat_inference(image, text, temperature, top_p, top_k, max_tokens, conversation):
+    if conversation is None:
+        conversation = []
+        
+    user_content = []
+    if image is not None:
+        user_content.append({"type": "image", "image": image})
+    if text and text.strip():
+        user_content.append({"type": "text", "text": text.strip()})
+    if not user_content:
+        return conversation_display(conversation), conversation
+
+    conversation.append({
+        "role": "user",
+        "content": user_content
+    })
+
+    inputs = processor.apply_chat_template(
+        conversation,
+        add_generation_prompt=True,
+        tokenize=True,
+        return_dict=True,
+        return_tensors="pt"
+    ).to("cuda")
+
+    torch.manual_seed(random.randint(0, 10000))
 
-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
+        "max_new_tokens": max_tokens,
+        "temperature": temperature,
+        "top_p": top_p,
+        "top_k": top_k,
+        "do_sample": True,
     }
 
-    # 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
+    output = model.generate(**inputs, **generation_kwargs)
+    assistant_response = processor.decode(output[0], skip_special_tokens=True)
+
+    conversation.append({
+        "role": "assistant",
+        "content": [{"type": "text", "text": assistant_response.strip()}]
+    })
+    
+    return conversation_display(conversation), conversation
+
+def conversation_display(conversation):
+    chat_history = []
+    for msg in conversation:
+        if msg["role"] == "user":
+            user_text = get_text_from_content(msg["content"])
+        elif msg["role"] == "assistant":
+            assistant_text = msg["content"][0]["text"].split("<|assistant|>")[-1].strip()
+            chat_history.append({"role": "user", "content": user_text})
+            chat_history.append({"role": "assistant", "content": assistant_text})
+    return chat_history
+
+def clear_chat():
+    return [], [], "", None
+    
+with gr.Blocks(title="Granite Vision 3.1 2B", css="h1 { overflow: hidden; }") as demo:
+    gr.Markdown("# Granite Vision 3.1 2B")
+    
     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]
+        with gr.Column(scale=2):
+            image_input = gr.Image(type="pil", label="Upload Image (optional)")
+            with gr.Column():
+                temperature_input = gr.Slider(minimum=0.0, maximum=2.0, value=0.2, step=0.01, label="Temperature")
+                top_p_input = gr.Slider(minimum=0.0, maximum=1.0, value=0.95, step=0.01, label="Top p")
+                top_k_input = gr.Slider(minimum=0, maximum=100, value=50, step=1, label="Top k")
+                max_tokens_input = gr.Slider(minimum=10, maximum=300, value=128, step=1, label="Max Tokens")
+            
+        with gr.Column(scale=3):
+            chatbot = gr.Chatbot(label="Chat History", elem_id="chatbot", type='messages')
+            text_input = gr.Textbox(lines=2, placeholder="Enter your message here", label="Message")
+            with gr.Row():
+                send_button = gr.Button("Chat")
+                clear_button = gr.Button("Clear Chat")
+    
+
+    state = gr.State([])
+
+    send_button.click(
+        chat_inference,
+        inputs=[image_input, text_input, temperature_input, top_p_input, top_k_input, max_tokens_input, state],
+        outputs=[chatbot, state]
     )
 
-    clear_btn.click(
-        fn=lambda: (
-            "",
-            '<span class="placeholder-text">Your enhanced prompt will appear here</span>',
-            "",
-            gr.update(interactive=True),
-            gr.update(interactive=True)
-        ),
+    clear_button.click(
+        clear_chat,
         inputs=None,
-        outputs=[input_text, output_html, raw_output, enhance_btn, clear_btn]
+        outputs=[chatbot, state, text_input, image_input]
+    )
+
+    gr.Examples(
+        examples=[
+            ["https://raw.githubusercontent.com/gradio-app/gradio/main/test/test_files/bus.png", "What is this?"]
+        ],
+        inputs=[image_input, text_input]
     )
 
 if __name__ == "__main__":
-    demo.queue(max_size=20).launch(mcp_server=True)
+    demo.launch()

requirements.txt

@@ -1,2 +1,6 @@
-transformers
-nltk
+torch
+torchvision
+git+https://github.com/huggingface/transformers.git
+gradio
+accelerate
+bitsandbytes