Update interface.py

interface.py

@@ -11,7 +11,19 @@ from lineage_visualizer import render_lineage_tree
 from waveform_renderer import render_waveform
 from leaderboard import generate_leaderboard
 from codex.formulas import GVU_FORMULAS, rft_invariants
-import stage1   # keep imports at the top
+import stage1
+import stage2
+import stage3
+import stage4
+import stage5
+import stage6
+import stage7
+import stage8
+import stage9
+import stage10
+import stage11
+import stage12
+
 
 # Safety guard to ensure agent has required keys
 def ensure_agent_shape(agent: dict, mutation_profile: dict) -> dict:
@@ -161,23 +173,140 @@ with gr.Blocks(theme="soft") as demo:
     with gr.Tab("Codex Reference"):
         gr.Markdown("... your existing reference markdown ...")
 
-    # --- Validation Stages Tab ---
-    with gr.Tab("Validation Stages"):
-        stage = gr.Dropdown(
-            ["Stage 1 — CIFAR-10 Baseline"],  # extend with Stage 2–12 later
-            label="Select Stage"
+   # --- Validation Stages Tab ---
+def run_stage(stage_name, mode, epochs, batch, lr):
+    if stage_name == "Stage 1 — CIFAR-10 Baseline":
+        return stage1.train(
+            mode=mode,
+            epochs=int(epochs),
+            batch=int(batch),
+            lr=float(lr),
+            log_path="stage1_cifar10_log.jsonl"
+        )
+    elif stage_name == "Stage 2 — Orbital & Agent Coupling":
+        return stage2.train(
+            mode=mode,
+            steps=int(epochs),
+            n=int(batch),
+            r0=0.165,
+            log_path="stage2_agents.jsonl"
+        )
+    elif stage_name == "Stage 3 — Unified Telemetry":
+        return stage3.train(
+            mode=mode,
+            steps=int(epochs),
+            batch=int(batch),
+            log_path="stage3_telemetry.jsonl"
+        )
+    elif stage_name == "Stage 4 — ViT-Tiny (ImageNet Subset)":
+        return stage4.train(
+            mode=mode,
+            data_dir=None,
+            steps=int(epochs),
+            batch=int(batch),
+            lr=float(lr),
+            log_path="stage4_vit_tiny.jsonl"
+        )
+    elif stage_name == "Stage 5 — ViT-Small/B32 (ImageNet Subset)":
+        return stage5.run(
+            mode=mode,
+            data_dir=None,
+            steps=int(epochs),
+            batch=int(batch),
+            lr=float(lr),
+            log="stage5_vit_small_b32.jsonl"
+        )
+    elif stage_name == "Stage 6 — ViT-Base (Full ImageNet-1K)":
+        return stage6.run(
+            mode=mode,
+            data_dir=None,
+            epochs=int(epochs),
+            batch=int(batch),
+            lr=float(lr),
+            log_path="stage6_vit_base.jsonl"
         )
-        mode = gr.Dropdown(["RFT", "BASE"], label="Mode")
-        epochs = gr.Number(label="Epochs", value=5)
-        batch = gr.Number(label="Batch Size", value=256)
-        lr = gr.Number(label="Learning Rate", value=5e-4)
-        val_output = gr.Textbox(label="Validation Output")
-        run_button = gr.Button("Run Stage")
-        run_button.click(
-            fn=run_stage,
-            inputs=[stage, mode, epochs, batch, lr],
-            outputs=val_output
+    elif stage_name == "Stage 7 — CLIP Multi-Modal (Text–Image)":
+        return stage7.run(
+            mode=mode,
+            steps=int(epochs),
+            batch=int(batch),
+            lr=float(lr),
+            log="stage7_clip.jsonl"
+        )
+    elif stage_name == "Stage 8 — RFT-LLM (Language-Only Transformer)":
+        return stage8.run(
+            mode=mode,
+            steps=int(epochs),
+            batch=int(batch),
+            lr=float(lr),
+            log="stage8_llm.jsonl"
+        )
+    elif stage_name == "Stage 9 — Distributed LLM (DDP, 4×A100)":
+        return stage9.run_ddp(
+            mode=mode,
+            steps=int(epochs),
+            batch=int(batch),
+            seq=256,
+            vocab=32768,
+            lr=float(lr),
+            log="stage9_dist_llm.jsonl"
+        )
+    elif stage_name == "Stage 10 — RFT-GPT-30B (DDP, 8×A100)":
+        return stage10.run(
+            mode=mode,
+            steps=int(epochs),
+            batch=int(batch),
+            seq=1024,
+            vocab=32768,
+            lr=float(lr),
+            log="stage10_gpt30b.jsonl"
         )
+    elif stage_name == "Stage 11 — RFT-GPT-70B (DDP, 16×A100)":
+        return stage11.run(
+            mode=mode,
+            steps=int(epochs),
+            batch=int(batch),
+            vocab=32768,
+            lr=float(lr),
+            log="stage11_gpt70b.jsonl"
+        )
+    elif stage_name == "Stage 12 — Production Pilot & Monitoring":
+        # Stage 12 is a monitor, not a training run; call its main entry
+        return stage12.main()
+    else:
+        return "Stage not yet implemented."
+
+
+with gr.Tab("Validation Stages"):
+    stage = gr.Dropdown(
+        [
+            "Stage 1 — CIFAR-10 Baseline",
+            "Stage 2 — Orbital & Agent Coupling",
+            "Stage 3 — Unified Telemetry",
+            "Stage 4 — ViT-Tiny (ImageNet Subset)",
+            "Stage 5 — ViT-Small/B32 (ImageNet Subset)",
+            "Stage 6 — ViT-Base (Full ImageNet-1K)",
+            "Stage 7 — CLIP Multi-Modal (Text–Image)",
+            "Stage 8 — RFT-LLM (Language-Only Transformer)",
+            "Stage 9 — Distributed LLM (DDP, 4×A100)",
+            "Stage 10 — RFT-GPT-30B (DDP, 8×A100)",
+            "Stage 11 — RFT-GPT-70B (DDP, 16×A100)",
+            "Stage 12 — Production Pilot & Monitoring"
+        ],
+        label="Select Stage"
+    )
+    mode = gr.Dropdown(["RFT", "BASE"], label="Mode")
+    epochs = gr.Number(label="Epochs/Steps", value=200)
+    batch = gr.Number(label="Batch Size", value=256)
+    lr = gr.Number(label="Learning Rate", value=5e-4)
+    val_output = gr.Textbox(label="Validation Output")
+    run_button = gr.Button("Run Stage")
+    run_button.click(
+        fn=run_stage,
+        inputs=[stage, mode, epochs, batch, lr],
+        outputs=val_output
+    )
 
 if __name__ == "__main__":
     demo.launch()
+