Update app.py

app.py

@@ -312,15 +312,18 @@ def start_training_wrapper(hf_token, model_name, new_repo_name, lora_r, lora_alp
     thread.daemon = True
     thread.start()
     
-    base_url = str(request.request.url).split('?')[0]
-    share_url = f"{base_url}?job_id={new_job.id}"
+    try:
+        base_url = str(request.request.url).split('?')[0]
+        share_url = f"{base_url}?job_id={new_job.id}"
+    except:
+        share_url = f"Job ID: {new_job.id}"
 
-    return new_job.id, gr.update(visible=True, value=f"Job Started: {new_job.id}"), gr.update(visible=True, value=share_url)
+    return new_job.id, gr.update(visible=True, value=f"SESSION ID: {new_job.id}"), gr.update(visible=True, value=share_url)
 
 def get_job_update(job_id):
     if job_id not in JOBS:
         return (
-            "Job Not Found", 
+            "<span style='color: #ef4444'>INVALID SESSION ID</span>", 
             "--:--", 
             "0%", 
             "", 
@@ -329,84 +332,241 @@ def get_job_update(job_id):
     
     job = JOBS[job_id]
     
-    log_text = "\n".join(job.logs[-30:])
+    log_html = "<br>".join([f"<div class='log-line'>{l}</div>" for l in job.logs[-50:]])
+    
+    progress_html = f"""
+    <div class="p-bar-wrapper">
+        <div class="p-bar-fill" style="width: {job.progress * 100}%"></div>
+    </div>
+    <div class="p-text">{int(job.progress * 100)}% COMPLETE</div>
+    """
+    
+    status_map = {
+        "IDLE": "#94a3b8",
+        "ACTIVE": "#3b82f6",
+        "COMPLETED": "#10b981",
+        "FAILED": "#ef4444"
+    }
     
-    progress_text = f"Progress: {int(job.progress * 100)}%"
+    status_html = f"<span style='color: {status_map.get(job.status, '#fff')}; font-weight: 900; letter-spacing: 1px;'>{job.status}</span>"
     
     result_comp = gr.update(visible=False)
     if job.status == "COMPLETED" and job.repo_url:
-        result_comp = gr.update(visible=True, value=f"Success! Model URL: {job.repo_url}")
+        result_comp = gr.update(visible=True, value=f"ACCESS MODEL ARTIFACT: {job.repo_url}")
     
-    return job.status, job.created_at, progress_text, log_text, result_comp
+    return status_html, job.created_at, progress_html, log_html, result_comp
 
 def load_from_url(request: gr.Request):
-    params = request.query_params
-    job_id = params.get("job_id")
-    if job_id:
-        return gr.update(selected="monitor_tab"), job_id
+    try:
+        params = request.query_params
+        job_id = params.get("job_id")
+        if job_id:
+            return gr.update(selected="monitor_tab"), job_id
+    except:
+        pass
     return gr.update(selected="launch_tab"), ""
 
-with gr.Blocks(title="Nucleus Enterprise Native") as demo:
-    gr.Markdown("# Nucleus Enterprise")
-    gr.Markdown("Autonomous Neural Foundry - Native Mode")
-    
-    with gr.Tabs() as main_tabs:
-        with gr.TabItem("Configuration & Deployment", id="launch_tab"):
-            with gr.Row():
-                with gr.Column(scale=2):
+css = """
+@import url('https://fonts.googleapis.com/css2?family=Space+Grotesk:wght@300;500;700&family=JetBrains+Mono:wght@400;700&display=swap');
+
+:root {
+    --bg-dark: #0a0a0f;
+    --panel-dark: #13131f;
+    --primary: #6366f1;
+    --accent: #8b5cf6;
+    --text-main: #e2e8f0;
+    --text-dim: #64748b;
+    --border: #1e1e2e;
+}
+
+body {
+    background-color: var(--bg-dark) !important;
+    font-family: 'Space Grotesk', sans-serif !important;
+}
+
+.gradio-container {
+    background-color: transparent !important;
+    max-width: 1400px !important;
+}
+
+.header-container {
+    text-align: center;
+    padding: 3rem 0;
+    background: radial-gradient(circle at center, rgba(99, 102, 241, 0.05) 0%, transparent 60%);
+    margin-bottom: 2rem;
+    border-bottom: 1px solid var(--border);
+}
+
+h1 {
+    font-size: 3.5rem;
+    background: linear-gradient(135deg, #fff 0%, #94a3b8 100%);
+    -webkit-background-clip: text;
+    -webkit-text-fill-color: transparent;
+    text-transform: uppercase;
+    letter-spacing: -2px;
+    margin-bottom: 0.5rem;
+}
+
+.sub-header {
+    font-family: 'JetBrains Mono', monospace;
+    color: var(--primary);
+    font-size: 0.9rem;
+    letter-spacing: 2px;
+    text-transform: uppercase;
+}
+
+.gr-box, .gr-panel {
+    background: var(--panel-dark) !important;
+    border: 1px solid var(--border) !important;
+    border-radius: 4px !important;
+}
+
+.gr-input, .gr-textarea, .gr-number, .gr-dropdown {
+    background: #0d0d12 !important;
+    border: 1px solid var(--border) !important;
+    color: var(--text-main) !important;
+    font-family: 'JetBrains Mono', monospace;
+    font-size: 13px;
+    border-radius: 4px !important;
+}
+
+.gr-input:focus {
+    border-color: var(--primary) !important;
+    box-shadow: 0 0 0 1px var(--primary) !important;
+}
+
+.primary-btn {
+    background: var(--primary) !important;
+    border: none !important;
+    color: #fff !important;
+    font-family: 'JetBrains Mono', monospace !important;
+    text-transform: uppercase;
+    letter-spacing: 1px;
+    padding: 12px 24px !important;
+    border-radius: 2px !important;
+    transition: all 0.2s ease;
+}
+
+.primary-btn:hover {
+    background: var(--accent) !important;
+    box-shadow: 0 0 15px rgba(99, 102, 241, 0.3);
+}
+
+.p-bar-wrapper {
+    width: 100%;
+    height: 4px;
+    background: #1e1e2e;
+    margin-top: 15px;
+}
+
+.p-bar-fill {
+    height: 100%;
+    background: linear-gradient(90deg, var(--primary), var(--accent));
+    transition: width 0.4s cubic-bezier(0.4, 0, 0.2, 1);
+}
+
+.p-text {
+    font-family: 'JetBrains Mono', monospace;
+    font-size: 10px;
+    color: var(--primary);
+    text-align: right;
+    margin-top: 5px;
+}
+
+.log-line {
+    font-family: 'JetBrains Mono', monospace;
+    font-size: 11px;
+    color: var(--text-dim);
+    padding: 2px 0;
+    border-bottom: 1px solid rgba(255,255,255,0.03);
+}
+
+.session-box {
+    background: rgba(99, 102, 241, 0.1);
+    border: 1px solid var(--primary);
+    color: var(--primary);
+    font-family: 'JetBrains Mono', monospace;
+    padding: 1rem;
+    text-align: center;
+    font-size: 1.2rem;
+    margin: 1rem 0;
+}
+
+.label-wrap {
+    background: var(--panel-dark) !important;
+    border: 1px solid var(--border);
+    color: var(--text-main) !important;
+}
+"""
+
+with gr.Blocks(title="Nucleus Enterprise") as demo:
+    gr.HTML(f"<style>{css}</style>")
+    with gr.Column():
+        gr.HTML("""
+        <div class="header-container">
+            <h1>Nucleus Enterprise</h1>
+            <div class="sub-header">Autonomous Neural Foundry // V.4.0</div>
+        </div>
+        """)
+        
+        with gr.Tabs() as main_tabs:
+            with gr.TabItem("DEPLOYMENT", id="launch_tab"):
+                with gr.Row():
+                    with gr.Column(scale=2):
+                        with gr.Row():
+                            hf_token = gr.Textbox(label="HUGGIGFACE KEY", type="password", value=os.getenv("HF_TOKEN", ""))
+                            model_name = gr.Textbox(label="BASE MODEL ID", placeholder="Qwen/Qwen2.5-0.5B")
+                        
+                        repo_name = gr.Textbox(label="TARGET REPOSITORY", value="nucleus-build-v1")
+                        datasets = gr.Textbox(label="DATA STREAMS (CSV)", placeholder="Salesforce/fineweb_deduplicated", lines=4)
+                        
+                        reasoning_toggle = gr.Checkbox(label="ENABLE REASONING CORE (INJECTS LOGIC DATASETS)", value=False, elem_id="reasoning-switch")
+
+                    with gr.Column(scale=1):
+                        gr.Markdown("### HYPERPARAMETERS")
+                        train_steps = gr.Number(label="STEPS", value=100)
+                        lr = gr.Number(label="LEARNING RATE", value=2e-4)
+                        batch = gr.Number(label="BATCH SIZE", value=1)
+                        
+                        gr.Markdown("### LORA ADAPTERS")
+                        lora_r = gr.Slider(8, 256, 32, step=8, label="RANK")
+                        lora_a = gr.Slider(8, 512, 64, step=8, label="ALPHA")
+                        lora_d = gr.Slider(0, 0.5, 0.05, label="DROPOUT")
+
+                with gr.Accordion("ADVANCED CONFIGURATION INJECTION", open=False):
                     with gr.Row():
-                        hf_token = gr.Textbox(label="HuggingFace Write Token", type="password", value=os.getenv("HF_TOKEN", ""))
-                        model_name = gr.Textbox(label="Base Model ID", placeholder="Qwen/Qwen2.5-0.5B")
-                    
-                    repo_name = gr.Textbox(label="New Repository Name", value="nucleus-model-v1")
-                    datasets = gr.Textbox(label="Datasets (CSV format)", placeholder="Salesforce/fineweb_deduplicated", lines=4)
-                    
-                    reasoning_toggle = gr.Checkbox(label="Enable Reasoning Core (GSM8K/Logic Injection)", value=False)
+                        conf_json = gr.Code(label="CONFIG.JSON", language="json")
+                        tok_json = gr.Code(label="TOKENIZER_CONFIG.JSON", language="json")
+                        gen_json = gr.Code(label="GENERATION_CONFIG.JSON", language="json")
 
-                with gr.Column(scale=1):
-                    gr.Markdown("### Training Parameters")
-                    train_steps = gr.Number(label="Max Steps", value=100)
-                    lr = gr.Number(label="Learning Rate", value=2e-4)
-                    batch = gr.Number(label="Batch Size", value=1)
-                    
-                    gr.Markdown("### LoRA Config")
-                    lora_r = gr.Slider(8, 256, 32, step=8, label="Rank (r)")
-                    lora_a = gr.Slider(8, 512, 64, step=8, label="Alpha")
-                    lora_d = gr.Slider(0, 0.5, 0.05, label="Dropout")
+                launch_btn = gr.Button("INITIALIZE TRAINING SEQUENCE", elem_classes="primary-btn")
+                
+                job_info_area = gr.Group(visible=False)
+                with job_info_area:
+                    new_job_id_display = gr.HTML()
+                    share_link_display = gr.Textbox(label="DIRECT MONITOR UPLINK", interactive=False)
 
-            with gr.Accordion("Advanced Config Injection (JSON)", open=False):
+            with gr.TabItem("TELEMETRY", id="monitor_tab"):
                 with gr.Row():
-                    conf_json = gr.Code(label="config.json", language="json")
-                    tok_json = gr.Code(label="tokenizer_config.json", language="json")
-                    gen_json = gr.Code(label="generation_config.json", language="json")
-
-            launch_btn = gr.Button("Start Background Job", variant="primary")
-            
-            job_info_area = gr.Group(visible=False)
-            with job_info_area:
-                new_job_id_display = gr.HTML()
-                share_link_display = gr.Textbox(label="Direct Monitor Link", interactive=False, show_copy_button=True)
-
-        with gr.TabItem("Job Monitor", id="monitor_tab"):
-            with gr.Row():
-                input_job_id = gr.Textbox(label="Enter Job ID", placeholder="xxxxxxxx")
-                refresh_btn = gr.Button("Refresh Status")
-            
-            with gr.Row():
-                with gr.Column(scale=1):
-                    status_display = gr.Textbox(label="Current Status", interactive=False)
-                    created_display = gr.Textbox(label="Start Time", interactive=False)
-                    final_link = gr.Markdown(visible=False)
+                    input_job_id = gr.Textbox(label="SESSION ID", placeholder="ENTER 8-DIGIT ID")
+                    refresh_btn = gr.Button("ESTABLISH UPLINK", elem_classes="primary-btn")
                 
-                with gr.Column(scale=2):
-                    progress_display = gr.Textbox(label="Progress", interactive=False)
-                    logs_display = gr.TextArea(label="System Logs", interactive=False, lines=10)
+                with gr.Row():
+                    with gr.Column(scale=1):
+                        status_display = gr.HTML(label="STATUS")
+                        created_display = gr.Textbox(label="TIMESTAMP", interactive=False)
+                        final_link = gr.Markdown(visible=False)
+                    
+                    with gr.Column(scale=2):
+                        progress_display = gr.HTML()
+                        with gr.Accordion("SYSTEM LOGS", open=False):
+                            logs_display = gr.HTML()
 
     timer = gr.Timer(3000, active=False)
 
     def activate_timer():
         return gr.Timer(active=True)
-        
+
     demo.load(
         load_from_url,
         None,
@@ -418,7 +578,7 @@ with gr.Blocks(title="Nucleus Enterprise Native") as demo:
         inputs=[hf_token, model_name, repo_name, lora_r, lora_a, lora_d, train_steps, lr, batch, datasets, reasoning_toggle, conf_json, tok_json, gen_json],
         outputs=[new_job_id_display, job_info_area, share_link_display]
     ).then(
-        fn=lambda id: f"<b>Job Started! ID: {id}</b>",
+        fn=lambda id: f"<div class='session-box'>{id}</div>",
         inputs=[new_job_id_display],
         outputs=[new_job_id_display]
     )