Update app.py

app.py

@@ -1,132 +1,70 @@
-"""
-Jarvis X — Beyond-SOTA Codex Demo
-Fully-contained Gradio app with toy multi-LLM router, constraint engine,
-and audit ledger.
-
-Companions needed in the same directory:
-  • llm_sim.py        – toy LLM provider registry
-  • contracts.dsl     – keyword safety list
-  • requirements.txt  – gradio>=4.0
-"""
-
-import json
-import time
-from pathlib import Path
-from typing import Dict, Any
-
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from transformers import AutoModel, AutoTokenizer
 import gradio as gr
-from llm_sim import LLM_REGISTRY        # <-- toy LLM providers
-
-# ─────────────────────────────────────────────
-# Λ*  ──  Contract / Safety Engine
-# ─────────────────────────────────────────────
-CONTRACT_FILE = "contracts.dsl"
-
-def load_contracts() -> list[str]:
-    path = Path(CONTRACT_FILE)
-    if not path.exists():
-        return []
-    return [ln.strip() for ln in path.read_text().splitlines()
-            if ln.strip() and not ln.startswith("#")]
-
-def contracts_pass(text: str, rules: list[str]) -> bool:
-    return not any(rule.lower() in text.lower() for rule in rules)
-
-contracts_cache = load_contracts()
-
-# ─────────────────────────────────────────────
-# Ω  ──  Immutable Audit Ledger
-# ─────────────────────────────────────────────
-LEDGER_FILE = "ledger.jsonl"
-
-def log_event(ev: Dict[str, Any]):
-    with open(LEDGER_FILE, "a", encoding="utf-8") as f:
-        f.write(json.dumps(ev) + "\n")
-
-# ─────────────────────────────────────────────
-# Meta state (α weight) — stubbed
-# ─────────────────────────────────────────────
-META_FILE = "meta.json"
-def load_meta() -> Dict[str, Any]:
-    if Path(META_FILE).exists():
-        return json.loads(Path(META_FILE).read_text())
-    return {"alpha": 0.5}
-
-meta_state = load_meta()   # α weight for LLM vs symbolic
-
-# ─────────────────────────────────────────────
-# Decision Logic (toy multi-LLM + symbolic blend)
-# ─────────────────────────────────────────────
-def decide(intent: str, context: str, alpha: float) -> str:
-    # 1. Pick toy provider based on simple keywords
-    if "advanced" in intent.lower():
-        provider = LLM_REGISTRY["claude"]
-    elif "concise" in intent.lower():
-        provider = LLM_REGISTRY["mistral"]
-    else:
-        provider = LLM_REGISTRY["gpt"]
-
-    # 2. Generate toy LLM response
-    prompt = f"Intent: {intent}\nContext: {context}"
-    llm_out = provider.generate(prompt)
-
-    # 3. Symbolic stub
-    symbolic_out = "(symbolic system stub)"
-
-    # 4. Blend outputs
-    blended = (
-        f"[{provider.name} * {alpha:.2f}] {llm_out}\n"
-        f"[Symbolic * {1-alpha:.2f}] {symbolic_out}"
-    )
-    return blended
-
-# ─────────────────────────────────────────────
-# Gradio Callback
-# ─────────────────────────────────────────────
-def codex_run(intent: str, context: str):
-    t = time.time()
-    alpha = meta_state.get("alpha", 0.5)
-    output = decide(intent, context, alpha)
-
-    safe = contracts_pass(output, contracts_cache)
-    if not safe:
-        output = "Output blocked by contract engine (Λ*)."
-
-    decision_trace = "provider_mix α={:.2f}".format(alpha)
-    log_event({
-        "t": t, "intent": intent, "context": context,
-        "decision": decision_trace, "output": output, "safe": safe
-    })
-
-    audit = json.dumps({"t": t, "decision": decision_trace, "safe": safe})
-    return output, decision_trace, audit
-
-# ─────────────────────────────────────────────
-# Gradio UI
-# ─────────────────────────────────────────────
-with gr.Blocks(title="Jarvis X — Beyond-SOTA Codex Demo") as demo:
-    gr.Markdown("""
-    # 🧠 Jarvis X — Beyond-SOTA Codex Operational Intelligence  
-    **Toy multi-LLM router, constraint engine, audit ledger**
-
-    *Edit `contracts.dsl` to modify safety keywords.  All runs append to
-    `ledger.jsonl`.  LLMs here are simulated; replace in `llm_sim.py`
-    with real API calls when ready.*
-    """)
-
-    intent_in = gr.Textbox(label="Intent (Ψ)", placeholder="e.g. generate code, advanced analysis")
-    ctx_in    = gr.Textbox(label="Context (χ)", lines=4)
-    run_btn   = gr.Button("Run")
-
-    out_box   = gr.Textbox(label="System Output (Θ)", lines=6)
-    dec_box   = gr.Textbox(label="Decision Trace", lines=1)
-    audit_box = gr.Textbox(label="Audit Snapshot", lines=2)
-
-    run_btn.click(
-        fn=codex_run,
-        inputs=[intent_in, ctx_in],
-        outputs=[out_box, dec_box, audit_box]
-    )
 
-if __name__ == "__main__":
-    demo.launch()
+class DrMoagiSystem(nn.Module):
+    def __init__(self, model_name: str = "bert-base-uncased"):
+        super(DrMoagiSystem, self).__init__()
+        self.model = AutoModel.from_pretrained(model_name)
+        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
+        self.intent_encoder = nn.Linear(768, 128)
+        self.field_modulator = nn.Linear(128, 128)
+        self.constraint_kernel = nn.Linear(128, 128)
+        self.memory_operator = nn.LSTM(128, 128, num_layers=1)
+        self.projection_operator = nn.Linear(128, 768)
+
+    def forward(self, input_ids: torch.Tensor, attention_mask: torch.Tensor, memory: torch.Tensor):
+        # Intent Encoder
+        outputs = self.model(input_ids, attention_mask=attention_mask)
+        intent = torch.relu(self.intent_encoder(outputs.last_hidden_state[:, 0, :]))
+
+        # Field Modulator
+        field = torch.relu(self.field_modulator(intent))
+
+        # Constraint Kernel
+        constrained_field = torch.relu(self.constraint_kernel(field))
+
+        # Memory Operator
+        memory_output, _ = self.memory_operator(constrained_field.unsqueeze(0), memory)
+        memory = memory_output.squeeze(0)
+
+        # Projection Operator
+        output = self.projection_operator(memory)
+
+        return output, memory
+
+    def translate(self, input_text: str, context: str):
+        inputs = self.tokenizer(input_text, return_tensors="pt")
+        input_ids = inputs["input_ids"]
+        attention_mask = inputs["attention_mask"]
+        memory = torch.zeros(1, 128)
+
+        output, memory = self.forward(input_ids, attention_mask, memory)
+        return self.tokenizer.decode(output.argmax(-1), skip_special_tokens=True)
+
+# Initialize the system
+system = DrMoagiSystem()
+
+# Define the Gradio interface
+def dr_moagi_interface(input_text, context):
+    try:
+        output = system.translate(input_text, context)
+        return output
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+interface = gr.Interface(
+    fn=dr_moagi_interface,
+    inputs=[
+        gr.Textbox(label="Input Text"),
+        gr.Textbox(label="Context"),
+    ],
+    outputs=gr.Textbox(label="Output"),
+    title="Dr Moagi System",
+    description="A universal translational logic operator",
+)
+
+# Launch the interface
+interface.launch()