app.py

import os
os.environ['KERAS_BACKEND'] = 'tensorflow'
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"

import tensorflow as tf
import keras
import numpy as np
from tokenizers import Tokenizer
from huggingface_hub import hf_hub_download
import json
from abc import ABC, abstractmethod

# ==============================================================================
# Model Architecture (Must match training code)
# ==============================================================================
@keras.saving.register_keras_serializable()
class RotaryEmbedding(keras.layers.Layer):
    def __init__(self, dim, max_len=2048, theta=10000, **kwargs):
        super().__init__(**kwargs)
        self.dim = dim
        self.max_len = max_len
        self.theta = theta
        self.built_cache = False

    def build(self, input_shape):
        if not self.built_cache:
            inv_freq = 1.0 / (self.theta ** (tf.range(0, self.dim, 2, dtype=tf.float32) / self.dim))
            t = tf.range(self.max_len, dtype=tf.float32)
            freqs = tf.einsum("i,j->ij", t, inv_freq)
            emb = tf.concat([freqs, freqs], axis=-1)

            self.cos_cached = tf.constant(tf.cos(emb), dtype=tf.float32)
            self.sin_cached = tf.constant(tf.sin(emb), dtype=tf.float32)
            self.built_cache = True
        super().build(input_shape)

    def rotate_half(self, x):
        x1, x2 = tf.split(x, 2, axis=-1)
        return tf.concat([-x2, x1], axis=-1)

    def call(self, q, k):
        seq_len = tf.shape(q)[2]
        dtype = q.dtype
        cos = tf.cast(self.cos_cached[:seq_len, :], dtype)[None, None, :, :]
        sin = tf.cast(self.sin_cached[:seq_len, :], dtype)[None, None, :, :]

        q_rotated = (q * cos) + (self.rotate_half(q) * sin)
        k_rotated = (k * cos) + (self.rotate_half(k) * sin)

        return q_rotated, k_rotated

    def get_config(self):
        config = super().get_config()
        config.update({"dim": self.dim, "max_len": self.max_len, "theta": self.theta})
        return config


@keras.saving.register_keras_serializable()
class RMSNorm(keras.layers.Layer):
    def __init__(self, epsilon=1e-5, **kwargs):
        super().__init__(**kwargs)
        self.epsilon = epsilon

    def build(self, input_shape):
        self.scale = self.add_weight(name="scale", shape=(input_shape[-1],), initializer="ones")

    def call(self, x):
        variance = tf.reduce_mean(tf.square(x), axis=-1, keepdims=True)
        return x * tf.math.rsqrt(variance + self.epsilon) * self.scale

    def get_config(self):
        config = super().get_config()
        config.update({"epsilon": self.epsilon})
        return config


@keras.saving.register_keras_serializable()
class TransformerBlock(keras.layers.Layer):
    def __init__(self, d_model, n_heads, ff_dim, dropout, max_len, rope_theta, layer_idx=0, **kwargs):
        super().__init__(**kwargs)
        self.d_model = d_model
        self.n_heads = n_heads
        self.ff_dim = ff_dim
        self.dropout_rate = dropout
        self.max_len = max_len
        self.rope_theta = rope_theta
        self.head_dim = d_model // n_heads
        self.layer_idx = layer_idx

        self.pre_attn_norm = RMSNorm()
        self.pre_ffn_norm = RMSNorm()

        self.q_proj = keras.layers.Dense(d_model, use_bias=False, name="q_proj")
        self.k_proj = keras.layers.Dense(d_model, use_bias=False, name="k_proj")
        self.v_proj = keras.layers.Dense(d_model, use_bias=False, name="v_proj")
        self.out_proj = keras.layers.Dense(d_model, use_bias=False, name="o_proj")

        self.rope = RotaryEmbedding(self.head_dim, max_len=max_len, theta=rope_theta)

        self.gate_proj = keras.layers.Dense(ff_dim, use_bias=False, name="gate_proj")
        self.up_proj = keras.layers.Dense(ff_dim, use_bias=False, name="up_proj")
        self.down_proj = keras.layers.Dense(d_model, use_bias=False, name="down_proj")

        self.dropout = keras.layers.Dropout(dropout)

    def call(self, x, training=None):
        B, T, D = tf.shape(x)[0], tf.shape(x)[1], self.d_model
        dtype = x.dtype

        res = x
        y = self.pre_attn_norm(x)

        q = tf.transpose(tf.reshape(self.q_proj(y), [B, T, self.n_heads, self.head_dim]), [0, 2, 1, 3])
        k = tf.transpose(tf.reshape(self.k_proj(y), [B, T, self.n_heads, self.head_dim]), [0, 2, 1, 3])
        v = tf.transpose(tf.reshape(self.v_proj(y), [B, T, self.n_heads, self.head_dim]), [0, 2, 1, 3])

        q, k = self.rope(q, k)

        scores = tf.matmul(q, k, transpose_b=True) / tf.sqrt(tf.cast(self.head_dim, dtype))

        mask = tf.where(
            tf.linalg.band_part(tf.ones([T, T], dtype=dtype), -1, 0) == 0,
            tf.constant(-1e9, dtype=dtype),
            tf.constant(0.0, dtype=dtype)
        )
        scores += mask
        attn = tf.matmul(tf.nn.softmax(scores, axis=-1), v)

        attn = tf.reshape(tf.transpose(attn, [0, 2, 1, 3]), [B, T, D])
        x = res + self.dropout(self.out_proj(attn), training=training)

        res = x
        y = self.pre_ffn_norm(x)
        ffn = self.down_proj(keras.activations.silu(self.gate_proj(y)) * self.up_proj(y))

        return res + self.dropout(ffn, training=training)

    def get_config(self):
        config = super().get_config()
        config.update({
            "d_model": self.d_model,
            "n_heads": self.n_heads,
            "ff_dim": self.ff_dim,
            "dropout": self.dropout_rate,
            "max_len": self.max_len,
            "rope_theta": self.rope_theta,
            "layer_idx": self.layer_idx
        })
        return config


@keras.saving.register_keras_serializable()
class SAM1Model(keras.Model):
    def __init__(self, **kwargs):
        super().__init__()
        if 'config' in kwargs and isinstance(kwargs['config'], dict):
            self.cfg = kwargs['config']
        elif 'vocab_size' in kwargs:
            self.cfg = kwargs
        else:
            self.cfg = kwargs.get('cfg', kwargs)

        self.embed = keras.layers.Embedding(self.cfg['vocab_size'], self.cfg['d_model'], name="embed_tokens")

        ff_dim = int(self.cfg['d_model'] * self.cfg['ff_mult'])
        block_args = {
            'd_model': self.cfg['d_model'],
            'n_heads': self.cfg['n_heads'],
            'ff_dim': ff_dim,
            'dropout': self.cfg['dropout'],
            'max_len': self.cfg['max_len'],
            'rope_theta': self.cfg['rope_theta']
        }

        self.blocks = []
        for i in range(self.cfg['n_layers']):
            block = TransformerBlock(name=f"block_{i}", layer_idx=i, **block_args)
            self.blocks.append(block)

        self.norm = RMSNorm(name="final_norm")
        self.lm_head = keras.layers.Dense(self.cfg['vocab_size'], use_bias=False, name="lm_head")

    def call(self, input_ids, training=None):
        x = self.embed(input_ids)

        for block in self.blocks:
            x = block(x, training=training)

        return self.lm_head(self.norm(x))

    def get_config(self):
        base_config = super().get_config()
        base_config['config'] = self.cfg
        return base_config


# ==============================================================================
# Helper Functions
# ==============================================================================
def count_parameters(model):
    """Count total and non-zero parameters in model."""
    total_params = 0
    non_zero_params = 0
    
    for weight in model.weights:
        w = weight.numpy()
        total_params += w.size
        non_zero_params += np.count_nonzero(w)
    
    return total_params, non_zero_params


def format_param_count(count):
    """Format parameter count in human readable format."""
    if count >= 1e9:
        return f"{count/1e9:.2f}B"
    elif count >= 1e6:
        return f"{count/1e6:.2f}M"
    elif count >= 1e3:
        return f"{count/1e3:.2f}K"
    else:
        return str(count)


# ==============================================================================
# Model Backend Interface
# ==============================================================================
class ModelBackend(ABC):
    @abstractmethod
    def predict(self, input_ids):
        pass
    
    @abstractmethod
    def get_name(self):
        pass
    
    @abstractmethod
    def get_info(self):
        pass


class KerasBackend(ModelBackend):
    def __init__(self, model, name, display_name):
        self.model = model
        self.name = name
        self.display_name = display_name
        
        # Count parameters
        total, non_zero = count_parameters(model)
        self.total_params = total
        self.non_zero_params = non_zero
        self.sparsity = (1 - non_zero / total) * 100 if total > 0 else 0
        
        # Calculate actual model config for speed estimation
        self.n_heads = model.cfg.get('n_heads', 0)
        self.ff_dim = int(model.cfg.get('d_model', 0) * model.cfg.get('ff_mult', 0))
    
    def predict(self, input_ids):
        inputs = np.array([input_ids], dtype=np.int32)
        logits = self.model(inputs, training=False)
        return logits[0, -1, :].numpy()
    
    def get_name(self):
        return self.display_name
    
    def get_info(self):
        info = f"{self.display_name}\n"
        info += f"  Total params: {format_param_count(self.total_params)}\n"
        info += f"  Attention heads: {self.n_heads}\n"
        info += f"  FFN dimension: {self.ff_dim}\n"
        if self.sparsity > 1:
            info += f"  Sparsity: {self.sparsity:.1f}%\n"
        return info


# ==============================================================================
# EASY MODEL REGISTRY - ADD YOUR MODELS HERE!
# ==============================================================================
MODEL_REGISTRY = [
    # Format: (display_name, repo_id, weights_filename, config_filename)
    # Smaller models are ACTUALLY faster (fewer params = real speedup!)
    
    ("SAM-X-1-Large", "Smilyai-labs/Sam-1x-instruct", "ckpt.weights.h5", None),
    ("SAM-X-1-Fast ⚡ (BETA)", "Smilyai-labs/Sam-X-1-fast", "sam1_fast.weights.h5", "sam1_fast_config.json"),
    ("SAM-X-1-Mini 🚀 (BETA)", "Smilyai-labs/Sam-X-1-Mini", "sam1_mini.weights.h5", "sam1_mini_config.json"),
    ("SAM-X-1-Nano ⚡⚡ (BETA)", "Smilyai-labs/Sam-X-1-Nano", "sam1_nano.weights.h5", "sam1_nano_config.json"),
]

# To add a new model, just add a new line above! Format:
# ("Display Name", "repo_id", "weights.h5", "config.json")
# If config_filename is None, uses the default config


# ==============================================================================
# Load Models
# ==============================================================================
CONFIG_TOKENIZER_REPO_ID = "Smilyai-labs/Sam-1-large-it-0002"

print("="*80)
print("🤖 SAM-X-1 Multi-Model Chat Interface".center(80))
print("="*80)

# Download config and tokenizer
print(f"\n📦 Downloading config/tokenizer from: {CONFIG_TOKENIZER_REPO_ID}")
config_path = hf_hub_download(repo_id=CONFIG_TOKENIZER_REPO_ID, filename="config.json")
tokenizer_path = hf_hub_download(repo_id=CONFIG_TOKENIZER_REPO_ID, filename="tokenizer.json")

# Load config
with open(config_path, 'r') as f:
    base_config = json.load(f)

print(f"✅ Base config loaded")

# Build base model config
base_model_config = {
    'vocab_size': base_config['vocab_size'],
    'd_model': base_config['hidden_size'],
    'n_heads': base_config['num_attention_heads'],
    'ff_mult': base_config['intermediate_size'] / base_config['hidden_size'],
    'dropout': base_config.get('dropout', 0.0),
    'max_len': base_config['max_position_embeddings'],
    'rope_theta': base_config['rope_theta'],
    'n_layers': base_config['num_hidden_layers']
}

# Recreate tokenizer
print("\n🔤 Recreating tokenizer...")
tokenizer = Tokenizer.from_pretrained("gpt2")
eos_token = ""
eos_token_id = tokenizer.token_to_id(eos_token)

if eos_token_id is None:
    tokenizer.add_special_tokens([eos_token])
    eos_token_id = tokenizer.token_to_id(eos_token)

custom_tokens = ["<think>", "<think/>"]
for token in custom_tokens:
    if tokenizer.token_to_id(token) is None:
        tokenizer.add_special_tokens([token])

tokenizer.no_padding()
tokenizer.enable_truncation(max_length=base_config['max_position_embeddings'])
print(f"✅ Tokenizer ready (vocab size: {tokenizer.get_vocab_size()})")

# Load all models from registry
print("\n" + "="*80)
print("📦 LOADING MODELS".center(80))
print("="*80)

available_models = {}
dummy_input = tf.zeros((1, 1), dtype=tf.int32)

for display_name, repo_id, weights_filename, config_filename in MODEL_REGISTRY:
    try:
        print(f"\n⏳ Loading: {display_name}")
        print(f"   Repo: {repo_id}")
        print(f"   Weights: {weights_filename}")
        
        # Download weights
        weights_path = hf_hub_download(repo_id=repo_id, filename=weights_filename)
        
        # Load custom config if specified (for pruned models)
        if config_filename:
            print(f"   Config: {config_filename}")
            custom_config_path = hf_hub_download(repo_id=repo_id, filename=config_filename)
            with open(custom_config_path, 'r') as f:
                model_config = json.load(f)
            print(f"   📐 Custom architecture: {model_config['n_heads']} heads, {int(model_config['d_model'] * model_config['ff_mult'])} FFN dim")
        else:
            model_config = base_model_config.copy()
        
        # Create model with appropriate config
        model = SAM1Model(**model_config)
        model(dummy_input)
        model.load_weights(weights_path)
        model.trainable = False
        
        # Create backend
        backend = KerasBackend(model, display_name, display_name)
        available_models[display_name] = backend
        
        # Print stats
        print(f"   ✅ Loaded successfully!")
        print(f"   📊 Parameters: {format_param_count(backend.total_params)}")
        print(f"   📊 Attention heads: {backend.n_heads}")
        print(f"   📊 FFN dimension: {backend.ff_dim}")
        
    except Exception as e:
        print(f"   ⚠️  Failed to load: {e}")
        print(f"   Skipping {display_name}...")

if not available_models:
    raise RuntimeError("❌ No models loaded! Check your MODEL_REGISTRY configuration.")

print(f"\n✅ Successfully loaded {len(available_models)} model(s)")
print(f"   Device: {'GPU' if len(tf.config.list_physical_devices('GPU')) > 0 else 'CPU'}")

current_backend = list(available_models.values())[0]

# ==============================================================================
# Important Note About Pruning and Speed
# ==============================================================================
print("\n" + "="*80)
print("💡 ABOUT PRUNING & SPEED".center(80))
print("="*80)
print("""
📌 Does pruning reduce parameter count?
   YES and NO:
   • Total param count stays the same (architecture unchanged)
   • BUT pruned weights are set to ZERO (sparse weights)
   • Active/non-zero params are reduced significantly
   
📌 Does pruning speed up inference?
   IT DEPENDS:
   • Dense operations (regular matrix multiply): NO speedup by default
   • Need sparse kernels or hardware support for actual speedup
   • HOWEVER: Smaller active weights = better cache utilization
   • Less computation on zeros = potential speedup on some hardware
   
📌 What DOES speed things up reliably?
   ✅ Quantization (FP16, INT8) - smaller types = faster compute
   ✅ Fewer layers (layer pruning)
   ✅ Smaller hidden dimensions (width reduction)
   ✅ Knowledge distillation to smaller architecture
   
📌 Why use structured pruning then?
   ✅ Reduces memory footprint (especially with sparse storage)
   ✅ Can be combined with quantization for real speedups
   ✅ Preserves quality better than aggressive dimension reduction
   ✅ Foundation for converting to truly smaller architecture
""")

def generate_response_stream(prompt, temperature=0.7, backend=None):
    """Generate response and yield tokens one by one for streaming."""
    if backend is None:
        backend = current_backend
    
    encoded_prompt = tokenizer.encode(prompt)
    input_ids = [i for i in encoded_prompt.ids if i != eos_token_id]
    generated = input_ids.copy()

    current_text = ""
    in_thinking = False
    
    # Get max_len from the backend's model config
    max_len = backend.model.cfg['max_len']

    for _ in range(512):
        current_input = generated[-max_len:]
        
        # Get logits from selected backend
        next_token_logits = backend.predict(current_input)

        if temperature > 0:
            next_token_logits = next_token_logits / temperature
            top_k_indices = np.argpartition(next_token_logits, -50)[-50:]
            top_k_logits = next_token_logits[top_k_indices]
            top_k_probs = np.exp(top_k_logits - np.max(top_k_logits))
            top_k_probs /= top_k_probs.sum()
            next_token = top_k_indices[np.random.choice(len(top_k_indices), p=top_k_probs)]
        else:
            next_token = np.argmax(next_token_logits)

        if next_token == eos_token_id:
            break

        generated.append(int(next_token))

        new_text = tokenizer.decode(generated[len(input_ids):])
        if len(new_text) > len(current_text):
            new_chunk = new_text[len(current_text):]
            current_text = new_text
            
            if "<think>" in new_chunk:
                in_thinking = True
            elif "</think>" in new_chunk or "<think/>" in new_chunk:
                in_thinking = False
                
            yield new_chunk, in_thinking

# ==============================================================================
# Gradio Interface
# ==============================================================================
if __name__ == "__main__":
    import gradio as gr

    custom_css = """
    .chat-container {
        height: 600px;
        overflow-y: auto;
        padding: 20px;
        background: #ffffff;
    }
    
    .user-message {
        background: #f7f7f8;
        padding: 16px;
        margin: 12px 0;
        border-radius: 8px;
    }
    
    .assistant-message {
        background: #ffffff;
        padding: 16px;
        margin: 12px 0;
        border-radius: 8px;
        border-left: 3px solid #10a37f;
    }
    
    .message-content {
        color: #353740;
        line-height: 1.6;
        font-size: 15px;
    }
    
    .message-header {
        font-weight: 600;
        margin-bottom: 8px;
        color: #353740;
        font-size: 14px;
    }
    
    .thinking-content {
        color: #6b7280;
        font-style: italic;
        border-left: 3px solid #d1d5db;
        padding-left: 12px;
        margin: 8px 0;
        background: #f9fafb;
        padding: 8px 12px;
        border-radius: 4px;
    }
    
    .input-row {
        background: #ffffff;
        padding: 12px;
        border-radius: 8px;
        margin-top: 12px;
        border: 1px solid #e5e7eb;
    }
    
    .gradio-container {
        max-width: 900px !important;
        margin: auto !important;
    }
    
    .announcement-banner {
        background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
        color: white;
        padding: 16px 24px;
        border-radius: 12px;
        margin-bottom: 20px;
        box-shadow: 0 4px 6px rgba(0,0,0,0.1);
        text-align: center;
        font-size: 16px;
        font-weight: 500;
        animation: slideIn 0.5s ease-out;
    }
    
    @keyframes slideIn {
        from {
            opacity: 0;
            transform: translateY(-20px);
        }
        to {
            opacity: 1;
            transform: translateY(0);
        }
    }
    
    .announcement-banner strong {
        font-weight: 700;
        font-size: 18px;
    }
    
    .settings-panel {
        background: #f9fafb;
        padding: 16px;
        border-radius: 8px;
        margin-bottom: 12px;
        border: 1px solid #e5e7eb;
    }
    
    .model-info {
        background: #f0f9ff;
        border: 1px solid #bae6fd;
        padding: 12px;
        border-radius: 8px;
        margin-top: 8px;
        font-size: 13px;
        font-family: monospace;
        white-space: pre-line;
    }
    """

    def format_message_html(role, content, show_thinking=True):
        """Format a single message as HTML."""
        role_class = "user-message" if role == "user" else "assistant-message"
        role_name = "You" if role == "user" else "SAM-X-1"
        
        thinking = ""
        answer = ""
        
        if "<think>" in content:
            parts = content.split("<think>", 1)
            before_think = parts[0].strip()
            
            if len(parts) > 1:
                after_think = parts[1]
                
                if "</think>" in after_think:
                    think_parts = after_think.split("</think>", 1)
                    thinking = think_parts[0].strip()
                    answer = (before_think + " " + think_parts[1]).strip()
                elif "<think/>" in after_think:
                    think_parts = after_think.split("<think/>", 1)
                    thinking = think_parts[0].strip()
                    answer = (before_think + " " + think_parts[1]).strip()
                else:
                    thinking = after_think.strip()
                    answer = before_think
            else:
                answer = before_think
        else:
            answer = content
        
        html = f'<div class="{role_class}">'
        html += f'<div class="message-header">{role_name}</div>'
        html += f'<div class="message-content">'
        
        if thinking and show_thinking:
            html += f'<div class="thinking-content">💭 {thinking}</div>'
        
        if answer:
            html += f'<div>{answer}</div>'
        
        html += '</div></div>'
        return html

    def render_history(history, show_thinking):
        """Render chat history as HTML."""
        html = ""
        for msg in history:
            html += format_message_html(msg["role"], msg["content"], show_thinking)
        return html

    def send_message(message, history, show_thinking, temperature, model_choice):
        if not message.strip():
            yield history, "", render_history(history, show_thinking), ""
            return
        
        # Switch backend based on selection
        backend = available_models[model_choice]
        
        # Add user message
        history.append({"role": "user", "content": message})
        yield history, "", render_history(history, show_thinking), backend.get_info()
        
        # Generate prompt
        prompt = f"User: {message}\nSam:   <think>"
        
        # Start assistant message
        history.append({"role": "assistant", "content": "<think>"})
        
        # Stream response
        for new_chunk, in_thinking in generate_response_stream(prompt, temperature, backend):
            history[-1]["content"] += new_chunk
            yield history, "", render_history(history, show_thinking), backend.get_info()

    # Create Gradio interface
    with gr.Blocks(css=custom_css, theme=gr.themes.Soft(primary_hue="slate")) as demo:
        # Announcement Banner
        gr.HTML("""
        <div class="announcement-banner">
            🎉 <strong>NEW UPDATE:</strong> Multiple model variants now available! 
            Choose Fast/Mini/Nano for <strong>30-250% speed boost</strong>! ⚡
            The models marked with (BETA) are not useful yet. <strong>They are still in development!</strong>
        </div>
        """)
        
        gr.Markdown("# 🤖 SAM-X-1 Multi-Model Chat")
        
        # Settings panel
        with gr.Accordion("⚙️ Settings", open=False):
            with gr.Row():
                model_selector = gr.Dropdown(
                    choices=list(available_models.keys()),
                    value=list(available_models.keys())[0],
                    label="Model Selection",
                    info="Choose your speed/quality tradeoff"
                )
            
            model_info_box = gr.Textbox(
                label="Selected Model Info",
                value=list(available_models.values())[0].get_info(),
                interactive=False,
                lines=4,
                elem_classes=["model-info"]
            )
            
            with gr.Row():
                temperature_slider = gr.Slider(
                    minimum=0.0,
                    maximum=2.0,
                    value=0.7,
                    step=0.1,
                    label="Temperature",
                    info="Higher = more creative, Lower = more focused"
                )
                show_thinking_checkbox = gr.Checkbox(
                    label="Show Thinking Process",
                    value=True,
                    info="Display model's reasoning"
                )
        
        # Chat state and display
        chatbot_state = gr.State([])
        chat_html = gr.HTML(value="", elem_classes=["chat-container"])
        
        # Input area
        with gr.Row(elem_classes=["input-row"]):
            msg_input = gr.Textbox(
                placeholder="Ask me anything...",
                show_label=False,
                container=False,
                scale=9
            )
            send_btn = gr.Button("Send", variant="primary", scale=1)
        
        with gr.Row():
            clear_btn = gr.Button("🗑️ Clear Chat", size="sm")
        
        # Event handlers
        msg_input.submit(
            send_message,
            inputs=[msg_input, chatbot_state, show_thinking_checkbox, temperature_slider, model_selector],
            outputs=[chatbot_state, msg_input, chat_html, model_info_box]
        )
        
        send_btn.click(
            send_message,
            inputs=[msg_input, chatbot_state, show_thinking_checkbox, temperature_slider, model_selector],
            outputs=[chatbot_state, msg_input, chat_html, model_info_box]
        )
        
        clear_btn.click(
            lambda: ([], ""),
            outputs=[chatbot_state, chat_html]
        )
        
        show_thinking_checkbox.change(
            lambda h, st: render_history(h, st),
            inputs=[chatbot_state, show_thinking_checkbox],
            outputs=[chat_html]
        )
        
        # Update model info when selection changes
        model_selector.change(
            lambda choice: available_models[choice].get_info(),
            inputs=[model_selector],
            outputs=[model_info_box]
        )

    demo.launch(debug=True, share=True)