✨ Professional Gradio UI with comparison table and clean design

app.py

@@ -3,24 +3,46 @@ from transformers import AutoModelForCausalLM, AutoTokenizer
 import torch
 
 # ============================================================================
-# 🌸 YUUKI - Mobile-Trained Code Generator
+# YUUKI - Mobile-Trained Code Generator
+# First LLM Trained Entirely on a Smartphone
 # ============================================================================
 
-print("🌸 Loading Yuuki model...")
-print("This may take a minute on first load...")
+MODEL_ID = "OpceanAI/Yuuki-best"
+MODEL_LOADED = False
+model = None
+tokenizer = None
+
+
+def load_model():
+    """Load the Yuuki model with proper error handling."""
+    global model, tokenizer, MODEL_LOADED
+    
+    if MODEL_LOADED:
+        return True
+    
+    try:
+        print(f"Loading Yuuki model from {MODEL_ID}...")
+        
+        tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
+        model = AutoModelForCausalLM.from_pretrained(
+            MODEL_ID,
+            torch_dtype=torch.float32,
+            low_cpu_mem_usage=True,
+            trust_remote_code=True
+        )
+        
+        # Ensure pad token is set
+        if tokenizer.pad_token is None:
+            tokenizer.pad_token = tokenizer.eos_token
+        
+        MODEL_LOADED = True
+        print("Model loaded successfully!")
+        return True
+        
+    except Exception as e:
+        print(f"Error loading model: {e}")
+        return False
 
-try:
-    model = AutoModelForCausalLM.from_pretrained(
-        "OpceanAI/Yuuki-best",
-        torch_dtype=torch.float32,
-        low_cpu_mem_usage=True
-    )
-    tokenizer = AutoTokenizer.from_pretrained("OpceanAI/Yuuki-best")
-    print("✅ Model loaded successfully!")
-except Exception as e:
-    print(f"❌ Error loading model: {e}")
-    model = None
-    tokenizer = None
 
 # ============================================================================
 # Generation Function
@@ -28,186 +50,331 @@ except Exception as e:
 
 def generate_code(
     prompt: str,
-    max_length: int = 100,
+    max_new_tokens: int = 100,
     temperature: float = 0.7,
-    top_p: float = 0.9
+    top_p: float = 0.9,
+    top_k: int = 50,
+    repetition_penalty: float = 1.1
 ) -> str:
     """Generate code completion using Yuuki."""
     
-    if model is None or tokenizer is None:
-        return "❌ Model failed to load. Please refresh the page."
+    if not MODEL_LOADED:
+        if not load_model():
+            return "Error: Model failed to load. Please try refreshing the page."
     
-    if not prompt.strip():
-        return "⚠️ Please enter a code prompt."
+    if not prompt or not prompt.strip():
+        return "Please enter a code prompt to generate."
     
     try:
-        inputs = tokenizer(prompt, return_tensors="pt")
+        inputs = tokenizer(
+            prompt, 
+            return_tensors="pt",
+            truncation=True,
+            max_length=512
+        )
         
         with torch.no_grad():
             outputs = model.generate(
                 **inputs,
-                max_length=max_length,
+                max_new_tokens=max_new_tokens,
                 temperature=temperature,
                 top_p=top_p,
+                top_k=top_k,
+                repetition_penalty=repetition_penalty,
                 do_sample=True,
-                pad_token_id=tokenizer.eos_token_id,
-                eos_token_id=tokenizer.eos_token_id
+                pad_token_id=tokenizer.pad_token_id,
+                eos_token_id=tokenizer.eos_token_id,
+                num_return_sequences=1
             )
         
-        generated = tokenizer.decode(outputs[0], skip_special_tokens=True)
-        return generated
+        generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+        return generated_text
     
     except Exception as e:
-        return f"❌ Generation error: {str(e)}"
+        return f"Generation error: {str(e)}"
+
 
 # ============================================================================
-# Examples
+# Examples by Language Quality
 # ============================================================================
 
-examples = [
-    # Agda (best language)
-    ["module Main where", 100, 0.7, 0.9],
-    ["open import Data.Nat", 80, 0.7, 0.9],
-    ["data Bool : Set where", 80, 0.7, 0.9],
+EXAMPLES = [
+    # Agda - Best performance (55/100)
+    ["module Main where", 120, 0.7, 0.9, 50, 1.1],
+    ["open import Data.Nat", 100, 0.7, 0.9, 50, 1.1],
+    ["data Bool : Set where", 100, 0.7, 0.9, 50, 1.1],
     
-    # C (limited but improving)
-    ["int main() {", 80, 0.7, 0.9],
-    ["#include <stdio.h>", 60, 0.7, 0.9],
+    # C - Limited (20/100)
+    ["int main() {", 100, 0.7, 0.9, 50, 1.1],
+    ["#include <stdio.h>", 80, 0.7, 0.9, 50, 1.1],
     
-    # Python (weak due to dataset ordering)
-    ["def hello():", 60, 0.8, 0.9],
-    ["import numpy as np", 60, 0.7, 0.9],
+    # Assembly - Basic (15/100)
+    ["mov eax,", 60, 0.8, 0.9, 50, 1.1],
+    
+    # Python - Weak due to dataset order (8/100)
+    ["def hello():", 80, 0.8, 0.9, 50, 1.2],
+    ["import numpy as np", 60, 0.7, 0.9, 50, 1.1],
 ]
 
+
 # ============================================================================
-# Custom CSS
+# Custom CSS - Clean Modern Design
 # ============================================================================
 
-custom_css = """
-#title {
+CUSTOM_CSS = """
+/* Main container */
+.gradio-container {
+    max-width: 1200px !important;
+    margin: auto !important;
+}
+
+/* Header styling */
+.header-title {
     text-align: center;
-    background: linear-gradient(90deg, #667eea 0%, #764ba2 100%);
-    -webkit-background-clip: text;
-    -webkit-text-fill-color: transparent;
-    font-size: 3em;
-    font-weight: bold;
-    margin-bottom: 0.5em;
+    font-size: 2.5rem;
+    font-weight: 700;
+    color: #1a1a2e;
+    margin-bottom: 0.25rem;
+    letter-spacing: -0.02em;
 }
 
-#subtitle {
+.header-subtitle {
     text-align: center;
-    font-size: 1.3em;
-    color: #666;
-    margin-bottom: 1em;
+    font-size: 1.1rem;
+    color: #64748b;
+    margin-bottom: 1.5rem;
+}
+
+/* Info cards */
+.info-card {
+    background: #f8fafc;
+    border: 1px solid #e2e8f0;
+    border-radius: 12px;
+    padding: 1.25rem;
+    margin-bottom: 1rem;
 }
 
-#warning-box {
-    background: linear-gradient(135deg, #fff3cd 0%, #ffe8a1 100%);
-    border-left: 4px solid #ffc107;
-    border-radius: 8px;
-    padding: 20px;
-    margin: 20px 0;
-    box-shadow: 0 2px 4px rgba(0,0,0,0.1);
+.info-card.warning {
+    background: #fffbeb;
+    border-color: #fcd34d;
+    border-left: 4px solid #f59e0b;
 }
 
-#stats-box {
-    background: linear-gradient(135deg, #e7f3ff 0%, #cfe7ff 100%);
-    border-left: 4px solid #2196F3;
-    border-radius: 8px;
-    padding: 20px;
-    margin: 20px 0;
-    box-shadow: 0 2px 4px rgba(0,0,0,0.1);
+.info-card.stats {
+    background: #f0f9ff;
+    border-color: #bae6fd;
+    border-left: 4px solid #0ea5e9;
 }
 
-#achievement-box {
-    background: linear-gradient(135deg, #f0e8ff 0%, #e1d4ff 100%);
-    border-left: 4px solid #9c27b0;
-    border-radius: 8px;
-    padding: 20px;
-    margin: 20px 0;
-    box-shadow: 0 2px 4px rgba(0,0,0,0.1);
+.info-card.achievement {
+    background: #faf5ff;
+    border-color: #e9d5ff;
+    border-left: 4px solid #a855f7;
+}
+
+.info-card h3 {
+    margin: 0 0 0.75rem 0;
+    font-size: 1rem;
+    font-weight: 600;
+}
+
+.info-card.warning h3 { color: #92400e; }
+.info-card.stats h3 { color: #0369a1; }
+.info-card.achievement h3 { color: #7c3aed; }
+
+.info-card p {
+    margin: 0.25rem 0;
+    font-size: 0.9rem;
+    color: #475569;
+    line-height: 1.5;
 }
 
-.gr-button-primary {
-    background: linear-gradient(90deg, #667eea 0%, #764ba2 100%) !important;
+/* Score badges */
+.score-row {
+    display: flex;
+    gap: 1rem;
+    flex-wrap: wrap;
+    margin-top: 0.75rem;
+}
+
+.score-badge {
+    display: inline-flex;
+    align-items: center;
+    gap: 0.5rem;
+    padding: 0.375rem 0.75rem;
+    border-radius: 9999px;
+    font-size: 0.8rem;
+    font-weight: 500;
+}
+
+.score-badge.good {
+    background: #dcfce7;
+    color: #166534;
+}
+
+.score-badge.medium {
+    background: #fef3c7;
+    color: #92400e;
+}
+
+.score-badge.weak {
+    background: #fee2e2;
+    color: #991b1b;
+}
+
+/* Primary button */
+.primary-btn {
+    background: linear-gradient(135deg, #3b82f6 0%, #1d4ed8 100%) !important;
     border: none !important;
-    font-weight: bold !important;
+    color: white !important;
+    font-weight: 600 !important;
+    transition: all 0.2s ease !important;
+}
+
+.primary-btn:hover {
+    transform: translateY(-1px) !important;
+    box-shadow: 0 4px 12px rgba(59, 130, 246, 0.4) !important;
+}
+
+/* Comparison table */
+.comparison-table {
+    width: 100%;
+    border-collapse: collapse;
+    margin: 1rem 0;
+    font-size: 0.875rem;
 }
 
-footer {
-    margin-top: 40px;
-    padding-top: 20px;
-    border-top: 1px solid #ddd;
+.comparison-table th,
+.comparison-table td {
+    padding: 0.75rem;
+    text-align: left;
+    border-bottom: 1px solid #e2e8f0;
+}
+
+.comparison-table th {
+    background: #f1f5f9;
+    font-weight: 600;
+    color: #334155;
+}
+
+.comparison-table tr:hover {
+    background: #f8fafc;
+}
+
+/* Footer */
+.footer {
+    margin-top: 2rem;
+    padding-top: 1.5rem;
+    border-top: 1px solid #e2e8f0;
+    text-align: center;
+    color: #64748b;
+    font-size: 0.875rem;
+}
+
+.footer a {
+    color: #3b82f6;
+    text-decoration: none;
+}
+
+.footer a:hover {
+    text-decoration: underline;
+}
+
+/* Links row */
+.links-row {
+    display: flex;
+    justify-content: center;
+    gap: 1.5rem;
+    flex-wrap: wrap;
+    margin: 1rem 0;
+}
+
+.link-item {
+    color: #3b82f6;
+    text-decoration: none;
+    font-weight: 500;
+    font-size: 0.9rem;
+}
+
+/* Accordion styling */
+.accordion {
+    border: 1px solid #e2e8f0 !important;
+    border-radius: 8px !important;
+    margin-top: 0.5rem !important;
 }
 """
 
+
 # ============================================================================
 # Gradio Interface
 # ============================================================================
 
-with gr.Blocks(css=custom_css, title="🌸 Yuuki - Mobile-Trained Code Generator", theme=gr.themes.Soft()) as demo:
+with gr.Blocks(
+    css=CUSTOM_CSS, 
+    title="Yuuki - Mobile-Trained Code Generator",
+    theme=gr.themes.Soft()
+) as demo:
     
     # Header
-    gr.Markdown("<h1 id='title'>🌸 Yuuki</h1>")
-    gr.Markdown("<p id='subtitle'>First LLM Trained Entirely on Mobile CPU | Zero-Budget ML Research</p>")
-    
-    # Warning Box
-    gr.Markdown("""
-    <div id='warning-box'>
-    <h3 style='margin-top:0; color:#856404;'>⚠️ Experimental Research Model</h3>
-    <p style='margin-bottom:0;'>
-    Yuuki was trained on a <strong>smartphone CPU</strong> with <strong>$0 budget</strong>. 
-    This is a <strong>proof-of-concept</strong> demonstrating mobile training feasibility, 
-    not a production-ready code generator.
-    </p>
-    <br>
-    <p style='margin-bottom:0;'>
-    <strong>Best at:</strong> Agda (55/100) • 
-    <strong>Limited:</strong> C (20/100), Assembly (15/100) • 
-    <strong>Weak:</strong> Python (8/100)
-    </p>
-    </div>
+    gr.HTML("""
+        <div class="header-title">Yuuki</div>
+        <div class="header-subtitle">
+            First LLM Trained Entirely on a Smartphone | Zero-Budget ML Research
+        </div>
     """)
     
-    # Stats Box
-    gr.Markdown("""
-    <div id='stats-box'>
-    <h3 style='margin-top:0; color:#0d47a1;'>📊 Training Statistics</h3>
-    <p style='margin-bottom:5px;'><strong>Hardware:</strong> Snapdragon 685 (CPU only) | <strong>Steps:</strong> 2,000 / 37,500 (5.3%)</p>
-    <p style='margin-bottom:5px;'><strong>Training Time:</strong> ~50 hours continuous | <strong>Speed:</strong> ~86 sec/step</p>
-    <p style='margin-bottom:5px;'><strong>Loss:</strong> 1.94 | <strong>Cost:</strong> $0.00 | <strong>Quality:</strong> 24.6/100 average</p>
-    <p style='margin-bottom:0;'><strong>Status:</strong> Best checkpoint from early training | <strong>Full v0.1:</strong> Coming March 2026</p>
-    </div>
+    # Disclaimer Card
+    gr.HTML("""
+        <div class="info-card warning">
+            <h3>Experimental Research Model</h3>
+            <p>
+                Yuuki is the <strong>best model available at this moment</strong>. 
+                The full <strong>v0.1</strong> release is coming soon — once published, 
+                plans for <strong>v0.2</strong> will begin.
+            </p>
+            <p style="margin-top: 0.5rem;">
+                This model is being trained <strong>entirely on a smartphone CPU</strong> by a 
+                <strong>single person</strong>. A research paper exploring mobile LLM training 
+                will be published soon.
+            </p>
+            <div class="score-row">
+                <span class="score-badge good">Agda: 55/100</span>
+                <span class="score-badge medium">C: 20/100</span>
+                <span class="score-badge medium">Assembly: 15/100</span>
+                <span class="score-badge weak">Python: 8/100</span>
+            </div>
+        </div>
     """)
     
-    # Achievement Box
-    gr.Markdown("""
-    <div id='achievement-box'>
-    <h3 style='margin-top:0; color:#6a1b9a;'>🏆 Community Validation</h3>
-    <p style='margin-bottom:5px;'>✅ <strong>Followed by Gradio team member</strong> - recognized for unique approach</p>
-    <p style='margin-bottom:5px;'>✅ <strong>Liked by mradermacher</strong> - quantization expert validated concept</p>
-    <p style='margin-bottom:0;'>✅ <strong>5+ downloads</strong> - early adopters supporting mobile ML training</p>
-    </div>
+    # Stats Card
+    gr.HTML("""
+        <div class="info-card stats">
+            <h3>Training Statistics</h3>
+            <p><strong>Hardware:</strong> Snapdragon 685 (CPU only) | <strong>Model Size:</strong> 988 MB</p>
+            <p><strong>Progress:</strong> 2,000 / 37,500 steps (5.3%) | <strong>Speed:</strong> ~86 sec/step</p>
+            <p><strong>Loss:</strong> 1.69 - 2.31 | <strong>Cost:</strong> $0.00 | <strong>Average Quality:</strong> 24.6/100</p>
+            <p><strong>Improvement:</strong> +146% quality gain from checkpoint 1400 to 2000</p>
+        </div>
     """)
     
     # Main Interface
     with gr.Row():
         with gr.Column(scale=1):
             prompt_input = gr.Textbox(
-                label="💻 Code Prompt",
+                label="Code Prompt",
                 placeholder="module Main where",
-                lines=3,
-                info="Try Agda for best results!"
+                lines=4,
+                info="Try Agda prompts for best results"
             )
             
-            with gr.Accordion("⚙️ Advanced Settings", open=False):
-                max_length = gr.Slider(
+            with gr.Accordion("Advanced Settings", open=False):
+                max_new_tokens = gr.Slider(
                     minimum=20,
-                    maximum=200,
+                    maximum=256,
                     value=100,
                     step=10,
-                    label="Max Length",
-                    info="Maximum tokens to generate"
+                    label="Max New Tokens",
+                    info="Number of tokens to generate"
                 )
                 temperature = gr.Slider(
                     minimum=0.1,
@@ -215,89 +382,169 @@ with gr.Blocks(css=custom_css, title="🌸 Yuuki - Mobile-Trained Code Generator
                     value=0.7,
                     step=0.1,
                     label="Temperature",
-                    info="Higher = more creative, lower = more conservative"
+                    info="Higher = more creative, lower = more focused"
                 )
                 top_p = gr.Slider(
                     minimum=0.1,
                     maximum=1.0,
                     value=0.9,
                     step=0.05,
-                    label="Top P",
-                    info="Nucleus sampling parameter"
+                    label="Top P (Nucleus Sampling)",
+                    info="Cumulative probability threshold"
+                )
+                top_k = gr.Slider(
+                    minimum=1,
+                    maximum=100,
+                    value=50,
+                    step=5,
+                    label="Top K",
+                    info="Number of top tokens to consider"
+                )
+                repetition_penalty = gr.Slider(
+                    minimum=1.0,
+                    maximum=2.0,
+                    value=1.1,
+                    step=0.05,
+                    label="Repetition Penalty",
+                    info="Penalize repeated tokens"
                 )
             
-            generate_btn = gr.Button("🚀 Generate Code", variant="primary", size="lg")
+            generate_btn = gr.Button(
+                "Generate Code", 
+                variant="primary", 
+                size="lg",
+                elem_classes=["primary-btn"]
+            )
         
         with gr.Column(scale=1):
             output = gr.Textbox(
-                label="📝 Generated Code",
-                lines=15,
-                show_copy_button=True
+                label="Generated Code",
+                lines=16,
+                show_copy_button=True,
+                info="Output will appear here"
             )
     
-    # Examples Section
-    gr.Markdown("### 💡 Try These Examples:")
+    # Examples
+    gr.Markdown("### Examples")
     gr.Examples(
-        examples=examples,
-        inputs=[prompt_input, max_length, temperature, top_p],
+        examples=EXAMPLES,
+        inputs=[prompt_input, max_new_tokens, temperature, top_p, top_k, repetition_penalty],
         outputs=output,
         fn=generate_code,
         cache_examples=False,
         label="Click any example to try it"
     )
     
-    # Generate button action
+    # Comparison Section
+    with gr.Accordion("Checkpoint Comparison (1400 vs 2000)", open=False):
+        gr.HTML("""
+            <table class="comparison-table">
+                <thead>
+                    <tr>
+                        <th>Metric</th>
+                        <th>Checkpoint 1400</th>
+                        <th>Checkpoint 2000</th>
+                    </tr>
+                </thead>
+                <tbody>
+                    <tr>
+                        <td>Training Progress</td>
+                        <td>1,400 / 37,500 (3.7%)</td>
+                        <td>2,000 / 37,500 (5.3%)</td>
+                    </tr>
+                    <tr>
+                        <td>Average Loss</td>
+                        <td>1.70 - 2.23</td>
+                        <td>1.69 - 2.31</td>
+                    </tr>
+                    <tr>
+                        <td>Training Speed</td>
+                        <td>~100 sec/step</td>
+                        <td>~86 sec/step</td>
+                    </tr>
+                    <tr>
+                        <td>Agda Score</td>
+                        <td>20/100</td>
+                        <td><strong>55/100</strong></td>
+                    </tr>
+                    <tr>
+                        <td>C Score</td>
+                        <td>8/100</td>
+                        <td><strong>20/100</strong></td>
+                    </tr>
+                    <tr>
+                        <td>Assembly Score</td>
+                        <td>2/100</td>
+                        <td><strong>15/100</strong></td>
+                    </tr>
+                    <tr>
+                        <td>Average Quality</td>
+                        <td>~10/100</td>
+                        <td><strong>24.6/100 (+146%)</strong></td>
+                    </tr>
+                </tbody>
+            </table>
+        """)
+    
+    # Why This Matters
+    with gr.Accordion("Why This Project Matters", open=False):
+        gr.Markdown("""
+        **Yuuki proves that LLM training is accessible** even with zero budget and consumer hardware.
+        
+        - **Students** without GPU access can experiment with ML training
+        - **Democratizes** ML research globally — barriers are mindset, not money  
+        - **Explores** edge ML training possibilities on mobile devices
+        - **Documents** complete training journey including failures and recoveries
+        
+        **Key Finding:** Dataset quality matters more than loss value. Checkpoint-2700 achieved 
+        the lowest loss (1.62) but scored 12% worse in quality than checkpoint-2000, proving 
+        that loss alone is unreliable when training data varies.
+        """)
+    
+    # Footer
+    gr.HTML("""
+        <div class="footer">
+            <div class="links-row">
+                <a href="https://huggingface.co/OpceanAI/Yuuki-best" target="_blank">Model Card</a>
+                <a href="https://huggingface.co/OpceanAI/Yuuki" target="_blank">Original Yuuki</a>
+                <a href="https://github.com/YuuKi-OS/yuuki-training" target="_blank">Training Code</a>
+            </div>
+            <p style="margin-top: 1rem;">
+                Built with patience, a phone, and zero budget.<br>
+                <strong>Proving the barrier to AI is mindset, not money.</strong>
+            </p>
+            <p style="margin-top: 0.5rem; font-size: 0.8rem;">
+                Licensed under Apache 2.0 | Powered by 
+                <a href="https://gradio.app" target="_blank">Gradio</a> & 
+                <a href="https://huggingface.co" target="_blank">Hugging Face</a>
+            </p>
+        </div>
+    """)
+    
+    # Event handlers
     generate_btn.click(
         fn=generate_code,
-        inputs=[prompt_input, max_length, temperature, top_p],
+        inputs=[prompt_input, max_new_tokens, temperature, top_p, top_k, repetition_penalty],
         outputs=output
     )
     
-    # Footer
-    gr.Markdown("""
-    <footer>
-    
-    ### 🌟 About This Project
-    
-    **Yuuki proves that LLM training is accessible** even with zero budget and consumer hardware.
-    
-    **Why this matters:**
-    - 🎓 **Students** without GPU access can experiment with ML training
-    - 🌍 **Democratizes** ML research globally - barriers are mindset, not money
-    - 📱 **Explores** edge ML training possibilities on mobile devices
-    - 🔬 **Documents** complete training journey including failures and recoveries
-    
-    **Training Journey Highlights:**
-    - Step 1,292: Early peak (loss 1.70, quality 31/100)
-    - Step 1,600: Mode collapse (loss 2.41) 💀
-    - Step 1,900: Recovery begins (loss 1.76)
-    - **Step 2,000: Current best** (loss 1.94, quality 24.6/100) ⭐
-    - Steps 2,100-2,500: Bad data zone (<11/100 quality)
-    
-    **Key Finding:** Dataset quality matters more than loss value. Some checkpoints with excellent 
-    loss (1.71) had terrible quality (7/100) due to corrupted training data.
-    
-    ---
-    
-    ### 🔗 Links
-    
-    - 🤗 [Yuuki-best Model](https://huggingface.co/OpceanAI/Yuuki-best) - This checkpoint (recommended)
-    - 📜 [Original Yuuki](https://huggingface.co/OpceanAI/Yuuki) - First upload (historical)
-    - ⏳ Yuuki v0.1 Complete - Coming March 2026 (2 full epochs)
-    - 📄 Research Paper - Coming soon
-    - 💻 [Training Code](https://github.com/YuuKi-OS/yuuki-training)
-    
-    ---
-    
-    <p align="center">
-      <i>Built with patience, a phone, and zero budget</i><br>
-      <b>🌸 Proving the barrier to AI is mindset, not money</b><br><br>
-      Made with ❤️ | Powered by <a href="https://gradio.app">Gradio</a> & <a href="https://huggingface.co">HuggingFace</a>
-    </p>
-    
-    </footer>
-    """)
+    prompt_input.submit(
+        fn=generate_code,
+        inputs=[prompt_input, max_new_tokens, temperature, top_p, top_k, repetition_penalty],
+        outputs=output
+    )
+
 
+# ============================================================================
 # Launch
+# ============================================================================
+
 if __name__ == "__main__":
-    demo.launch()
+    # Preload model on startup
+    load_model()
+    
+    demo.launch(
+        share=False,
+        show_error=True,
+        show_api=False
+    )