Architecture: Stripped deprecated pixel constraints, implemented fluid Vega-Lite containers for iOS

app.py

@@ -28,21 +28,18 @@ DEFAULT_MODEL_REPO = "Dexifried/dex-router-model"
 # 📊 DEEP AUDIT: VEGA-LITE ENGINE
 # ==========================================
 def run_lab_audit():
-    """Reads the CSV, computes the Signal-to-Noise Ratio, and formats for Gradio 6 BarPlot."""
+    """Computes the Signal-to-Noise Ratio and formats for Gradio 6 Fluid BarPlot."""
     if not os.path.exists(DATA_PATH):
-        # Return empty DF and error markdown
-        return pd.DataFrame(columns=["Intent", "Samples", "Percentage"]), "⚠️ **CRITICAL:** `intent_dataset.csv` not found in Space."
+        return pd.DataFrame(columns=["Intent", "Samples", "Percentage"]), "⚠️ **CRITICAL:** `intent_dataset.csv` not found."
 
     try:
         df = pd.read_csv(DATA_PATH)
         total_samples = len(df)
         
-        # Calculate matrix distributions
         counts = df['label'].value_counts().reset_index()
         counts.columns = ['Intent', 'Samples']
         counts['Percentage'] = (counts['Samples'] / total_samples * 100).round(2)
         
-        # Signal to Noise Physics
         major_class = counts.iloc[0]['Intent']
         minor_class = counts.iloc[-1]['Intent']
         major_count = counts.iloc[0]['Samples']
@@ -69,21 +66,21 @@ def run_lab_audit():
         return pd.DataFrame(columns=["Intent", "Samples", "Percentage"]), f"⚠️ **Audit Failed:** {str(e)}"
 
 # ==========================================
-# 🔥 THE OVEN: ZEROGPU BAKE SEQUENCE
+# 🔥 THE OVEN: ASYNCHRONOUS ZEROGPU SEQUENCE
 # ==========================================
 @spaces.GPU(duration=600) 
 def start_bake(hf_token, target_repo):
-    """The ZeroGPU training loop with real-time UI yielding and auto-deployment."""
+    """ZeroGPU training loop with real-time yielding and Auto-Repo Provisioning."""
     if not hf_token or len(hf_token) < 10:
         raise gr.Error("Authentication Error: Valid HF Write Token required.")
     
     if not os.path.exists(DATA_PATH):
         raise gr.Error("Data Missing: intent_dataset.csv is not loaded.")
 
-    yield "⏳ **Phase 1/5:** Analyzing DNA & Prepping Auto-Deploy..."
-    time.sleep(1) # UX padding for mobile observation
+    yield "⏳ **Phase 1/5:** Analyzing DNA & Prepping Sovereign Auto-Deploy..."
+    time.sleep(1) # UX observation padding
 
-    # --- 1. Repository Auto-Provisioning (The 1GB Fix) ---
+    # --- 1. Repository Auto-Provisioning (The 1GB Space Limit Bypass) ---
     try:
         api = HfApi()
         create_repo(
@@ -94,18 +91,16 @@ def start_bake(hf_token, target_repo):
             private=True 
         )
     except Exception as e:
-        raise gr.Error(f"Repo Setup Failed. Is your token a 'Write' token? Error: {e}")
+        raise gr.Error(f"Repo Setup Failed. Verify Write Token permissions. Error: {e}")
 
-    # --- 2. Dynamic DNA Mapping ---
     df = pd.read_csv(DATA_PATH)
     unique_labels = sorted(df['label'].unique().tolist())
     num_labels = len(unique_labels)
     label2id = {label: i for i, label in enumerate(unique_labels)}
     id2label = {i: label for i, label in enumerate(unique_labels)}
 
-    yield f"🧬 **Phase 2/5:** Tokenizing {len(df)} samples across {num_labels} intents..."
+    yield f"🧬 **Phase 2/5:** Tokenizing {len(df)} High-Fidelity samples across {num_labels} intents..."
 
-    # --- 3. Tokenization ---
     tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL_ID)
     def tokenize_func(examples):
         return tokenizer(examples["text"], truncation=True, padding=True, max_length=128)
@@ -114,8 +109,7 @@ def start_bake(hf_token, target_repo):
     dataset = Dataset.from_pandas(df)
     tokenized_dataset = dataset.map(tokenize_func, batched=True)
 
-    # --- 4. Brain Instantiation ---
-    yield "🧠 **Phase 3/5:** Instantiating ModernBERT & Allocating ZeroGPU Hardware..."
+    yield "🧠 **Phase 3/5:** Instantiating ModernBERT & Allocating ZeroGPU A100 Hardware..."
     model = AutoModelForSequenceClassification.from_pretrained(
         BASE_MODEL_ID, 
         num_labels=num_labels,
@@ -144,24 +138,21 @@ def start_bake(hf_token, target_repo):
         data_collator=DataCollatorWithPadding(tokenizer=tokenizer),
     )
 
-    # --- 5. The Bake ---
     yield "🔥 **Phase 4/5:** Synaptic Bake Commencing! A100 Matrix engaged (Est. 30-45s)..."
     try:
         trainer.train()
         
-        # Save locally to container RAM
         model.save_pretrained(OUTPUT_DIR)
         tokenizer.save_pretrained(OUTPUT_DIR)
         
-        yield "📡 **Phase 5/5:** Bake Complete. Bypassing 1GB limit -> Uploading to Dedicated Model Repo..."
+        yield "📡 **Phase 5/5:** Bake Complete. Bypassing 1GB limit -> Executing LFS Upload to Dedicated Repo..."
         
-        # --- 6. The 50GB Limit Upload Hook ---
         api.upload_folder(
             folder_path=OUTPUT_DIR,
             repo_id=target_repo,
             repo_type="model",
             token=hf_token,
-            commit_message=f"Dex Update: Stabilized {num_labels}-Intent Matrix"
+            commit_message=f"Dex Neural Framework: Stabilized {num_labels}-Intent Matrix"
         )
         
         yield f"✅ **SUCCESS:** {num_labels}-Intent Brain deployed to `https://huggingface.co/{target_repo}`"
@@ -169,38 +160,46 @@ def start_bake(hf_token, target_repo):
         raise gr.Error(f"Lab Failure during execution: {str(e)}")
 
 # ==========================================
-# 📱 MOBILE-NATIVE UX (iPhone 12 Mini Profile)
+# 📱 MOBILE-NATIVE UX (iPhone 12 Mini Architecture)
 # ==========================================
 custom_css = """
-/* iPhone Form Factor Overrides */
+/* Mobile-First Fluid Overrides */
 .gr-button-primary { 
-    background: linear-gradient(135deg, #2b5876 0%, #4e4376 100%) !important; 
+    background: linear-gradient(135deg, #0f2027 0%, #203a43 50%, #2c5364 100%) !important; 
     border: none !important; 
-    box-shadow: 0 4px 15px rgba(0,0,0,0.2) !important;
-    transition: all 0.3s ease !important;
+    box-shadow: 0 4px 15px rgba(0,0,0,0.3) !important;
+    transition: transform 0.2s ease, box-shadow 0.2s ease !important;
 }
 .gr-button-primary:active {
-    transform: scale(0.98);
+    transform: scale(0.97);
+    box-shadow: 0 2px 8px rgba(0,0,0,0.2) !important;
 }
 .gr-box { 
     border-radius: 16px !important; 
-    border: 1px solid rgba(255,255,255,0.1) !important;
+    border: 1px solid rgba(255,255,255,0.05) !important;
+    background: rgba(20, 20, 20, 0.4) !important;
+    backdrop-filter: blur(10px);
 }
-/* Force inputs to be touch-friendly */
+/* Force inputs to 16px to prevent iOS Safari auto-zoom */
 input, textarea { font-size: 16px !important; } 
+
+/* Fluid Chart Container for iPhone */
+#fluid_chart {
+    width: 100% !important;
+    min-height: 400px !important;
+}
 """
 
-# Note: CSS and Theme parameters are now strictly bound to the launch() method per Gradio 6.0 specs
 with gr.Blocks(title="Dex Mission Control") as demo:
     
-    with gr.Column(elem_id="header_container"):
+    with gr.Column():
         gr.Markdown("# 🧠 Dex Sovereign Lab")
         gr.Markdown("*High-Fidelity Neural Baking & Diagnostics*")
 
     with gr.Tabs():
         # --- TAB 1: THE OVEN ---
         with gr.TabItem("🔥 The Oven"):
-            gr.Markdown("Trigger a ZeroGPU bake and deploy directly to your 50GB Model Repository.")
+            gr.Markdown("Execute ZeroGPU bake and route weights directly to 50GB Model Repo.")
             
             with gr.Column(variant="panel"):
                 token_input = gr.Textbox(
@@ -211,13 +210,11 @@ with gr.Blocks(title="Dex Mission Control") as demo:
                 repo_input = gr.Textbox(
                     label="Target Repository", 
                     value=DEFAULT_MODEL_REPO,
-                    info="Auto-provisioned on execution."
+                    info="Auto-provisioned to bypass Space constraints."
                 )
             
             bake_btn = gr.Button("🚀 INITIATE BAKE & DEPLOY", variant="primary", size="lg")
-            
-            # Real-time Telemetry feed
-            status_output = gr.Markdown("### 💤 Telemetry: Idle\nWaiting for deployment authorization.")
+            status_output = gr.Markdown("### 💤 Telemetry: Idle\nAwaiting authorization.")
             
             bake_btn.click(
                 fn=start_bake, 
@@ -227,21 +224,19 @@ with gr.Blocks(title="Dex Mission Control") as demo:
 
         # --- TAB 2: DNA AUDIT ---
         with gr.TabItem("📊 DNA Audit"):
-            gr.Markdown("Verify dataset equilibrium natively on mobile.")
+            gr.Markdown("Verify dataset equilibrium natively on your device.")
             
             audit_btn = gr.Button("🔍 Execute Deep Audit", variant="secondary", size="lg")
-            
             audit_md = gr.Markdown("Hit the button to analyze `intent_dataset.csv`.")
             
-            # Gradio 6.0 BarPlot Fix: Swapping X (Samples) and Y (Intent) forces a horizontal layout automatically.
-            audit_plot = gr.BarPlot(
-                x="Samples",      # Numerical axis on the bottom
-                y="Intent",       # Categorical axis on the side (Makes it horizontal for iPhone)
-                title="Neural Pathway Distribution",
-                tooltip=["Intent", "Samples", "Percentage"],
-                width=350,        # Tailored for 5.4" displays
-                height=450
-            )
+            # Fluid Flexbox Layout: width/height removed, relying on CSS & Gr.Column inheritance
+            with gr.Column(elem_id="fluid_chart"):
+                audit_plot = gr.BarPlot(
+                    x="Samples",      # Numerical X
+                    y="Intent",       # Categorical Y creates the Mobile-friendly Horizontal Chart
+                    title="Neural Pathway Distribution",
+                    tooltip=["Intent", "Samples", "Percentage"]
+                )
 
             audit_btn.click(
                 fn=run_lab_audit,
@@ -250,7 +245,6 @@ with gr.Blocks(title="Dex Mission Control") as demo:
             )
 
 if __name__ == "__main__":
-    # Gradio 6.0 Requirement: CSS and Themes are injected at launch
-    demo.launch(css=custom_css, theme=gr.themes.Base())
+    demo.launch(css=custom_css, theme=gr.themes.Monochrome())