final dual

app.py

@@ -1,166 +1,161 @@
 import gradio as gr
 from huggingface_hub import InferenceClient
 from array import array
-import os
-import re
-import time
+from functools import lru_cache
+import os, re, time
 
-# Securely retrieve the token from your Space's secrets
+# 1. API Configuration - Locked to the stable 7B model
 HF_TOKEN = os.getenv("HF_TOKEN")
+MODEL_ID = "Qwen/Qwen2.5-7B-Instruct" 
+client = InferenceClient(MODEL_ID, token=HF_TOKEN)
 
-# Initialize BOTH engines with the exact same base model
-client_primary = InferenceClient("Qwen/Qwen2.5-7B-Instruct", token=HF_TOKEN)
-client_competitor = InferenceClient("Qwen/Qwen2.5-7B-Instruct", token=HF_TOKEN)
-
+# 2. T3 High-Speed Logic Kernel
 class StateController:
-    __slots__ = ("_state", "_metric", "_batch", "_reg", "_rendered", "_rom60", "_symbols")
-
+    __slots__ = ("_state", "_rom60", "_symbols", "_rendered")
     def __init__(self):
         self._state      = array("B", [0]) * 121
-        self._metric     = 60
-        self._batch      = 10
-        self._reg        = {}
-        self._rendered   = self._build_render()
-        
-        self._rom60 = tuple(
-            tuple((i * j) % 60 for j in range(60)) 
-            for i in range(60)
-        )
-        self._symbols = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz01234567"
-
-    def _build_render(self) -> str:
-        return "".join(
-            " [NODE_120] " if i == 120 else ("<" if i % 10 == 0 else ".")
-            for i in range(121)
-        )
-
-    def diagnostic(self) -> str:
-        for i in range(51):
-            self._state[i] = i % self._batch
-        self._reg.clear()
-        self._reg["STATUS"] = "RESOLVED"
-        return (
-            "Diagnostic sequence initiated.\n\n"
-            "Grid initialized: 5 active blocks.\n\n"
-            "Rendering 121-point array:\n"
-            f"{self._rendered}\n\n"
-            "Executing state resolution:\n"
-            "System resolved. State array reset to zero."
-        )
-
-    def generate_receipt(self, a: int, b: int, c: int) -> str:
-        rom_val = self._rom60[a % 60][b % 60]
-        checksum_index = (rom_val ^ (c % 60)) % 60
-        return f"0{self._symbols[checksum_index]}"
-
-    def validate_receipt(self, receipt: str, a: int, b: int, c: int) -> str:
-        expected_receipt = self.generate_receipt(a, b, c)
-        if receipt == expected_receipt:
-            self._state[120] = 1
-            return f"[NODE_120: ACTIVE] Checksum Validated. Receipt {receipt} matches allocation ({a}, {b}, {c})."
-        else:
-            self._state[120] = 0
-            return f"[NODE_120: INACTIVE] Verification Failed. Expected receipt {expected_receipt}, received {receipt}."
+        self._rom60      = tuple(tuple((i * j) % 60 for j in range(60)) for i in range(60))
+        self._symbols    = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz01234567"
+        self._rendered   = "".join(" [NODE_120] " if i == 120 else ("<" if i % 10 == 0 else ".") for i in range(121))
+
+    @lru_cache(maxsize=128)
+    def compute_distribution(self, total, nodes) -> str:
+        if nodes <= 0: return "Error: Node count must be positive."
+        base, rem = divmod(total, nodes)
+        res = f"T3 Logic Kernel resolved {total} units across {nodes} nodes:\n\n"
+        for i in range(nodes):
+            res += f"NODE_{i+1:02}: {base + (1 if i < rem else 0)} units\n"
+        return res
+
+    def get_glyphs(self) -> str:
+        return f"Rendering 121-point state array:\n\n{self._rendered}\n\nSystem State: RESOLVED"
+
+    def generate_receipt(self, a, b, c) -> str:
+        idx = (self._rom60[a % 60][b % 60] ^ (c % 60)) % 60
+        return f"0{self._symbols[idx]}"
+
+    def validate_receipt(self, receipt, a, b, c) -> str:
+        expected = self.generate_receipt(a, b, c)
+        if receipt == expected:
+            return f"√ CHECKSUM VALID: Receipt {receipt} verified for allocation ({a}, {b}, {c})."
+        return f"× CHECKSUM INVALID: Expected {expected}, received {receipt}."
 
 controller = StateController()
 
-PRIMARY_SYSTEM_MSG = {"role": "system", "content": "You are a logic-focused inference engine. Utilize strict state-hold memory and parallel integer blocks."}
-COMPETITOR_SYSTEM_MSG = {"role": "system", "content": "You are a standard helpful AI assistant."}
-
-def generate_responses(user_message: str, primary_history: list, competitor_history: list):
-    clean_message = user_message.strip()
-    if not clean_message:
-        yield primary_history, competitor_history, ""
-        return
-
-    # Update histories with new dictionary format for Gradio 6
-    primary_history.append({"role": "user", "content": clean_message})
-    primary_history.append({"role": "assistant", "content": ""})
-    competitor_history.append({"role": "user", "content": clean_message})
-    competitor_history.append({"role": "assistant", "content": ""})
-    yield primary_history, competitor_history, ""
+def format_telemetry(seconds: float) -> str:
+    if seconds < 0.001: return f"{seconds * 1_000_000:.2f} \u03BCs"
+    return f"{seconds * 1_000:.2f} ms" if seconds < 1 else f"{seconds:.2f} s"
+
+# 3. Core Response Logic
+def generate_responses(user_message, p_hist, c_hist):
+    msg = user_message.strip()
+    if not msg: yield p_hist or [], c_hist or [], ""; return
+
+    p_hist, c_hist = p_hist or [], c_hist or []
+    p_hist.append({"role": "user", "content": msg})
+    p_hist.append({"role": "assistant", "content": ""})
+    c_hist.append({"role": "user", "content": msg})
+    c_hist.append({"role": "assistant", "content": ""})
+    yield p_hist, c_hist, ""
     
     start_time = time.perf_counter()
 
-    # Hardware diagnostic intercept
-    if clean_message.lower() == "run grid diagnostic":
-        output = controller.diagnostic()
-        elapsed_time = time.perf_counter() - start_time
-        primary_history[-1]["content"] = f"{output}\n\n---\n*Telemetry: {elapsed_time:.4f}s | Source: Local Engine*"
-        competitor_history[-1]["content"] = "Hardware diagnostics not supported by generic models."
-        yield primary_history, competitor_history, ""
-        return
-
-    # Deterministic Checksum Intercept
-    verify_match = re.search(r"verify receipt\s+([a-zA-Z0-9]{2})\s+for\s+(\d+),\s*(\d+),\s*(\d+)", clean_message, re.IGNORECASE)
-    if verify_match:
-        receipt, a, b, c = verify_match.group(1), int(verify_match.group(2)), int(verify_match.group(3)), int(verify_match.group(4))
-        output = controller.validate_receipt(receipt, a, b, c)
-        elapsed_time = time.perf_counter() - start_time
-        primary_history[-1]["content"] = f"{output}\n\n---\n*Telemetry: {elapsed_time:.6f}s | Source: Local ROM Math*"
-        competitor_history[-1]["content"] = "Deterministic verification not supported by standard LLMs."
-        yield primary_history, competitor_history, ""
-        return
-
-    # STREAM 1: Primary Engine
-    try:
-        msgs = [PRIMARY_SYSTEM_MSG] + primary_history[:-1]
-        primary_response = ""
-        stream = client_primary.chat_completion(messages=msgs, max_tokens=1024, stream=True, temperature=0.15)
-        for chunk in stream:
-            primary_response += (chunk.choices[0].delta.content or "")
-            primary_history[-1]["content"] = primary_response
-            yield primary_history, competitor_history, ""
-        
-        primary_time = time.perf_counter() - start_time
-        primary_history[-1]["content"] += f"\n\n---\n*Telemetry: {primary_time:.2f}s | Source: Augmented Kernel*"
-        yield primary_history, competitor_history, ""
-    except Exception as e:
-        primary_history[-1]["content"] = f"Error: {str(e)}"
-        yield primary_history, competitor_history, ""
+    # --- LOCAL INTERCEPTORS ---
+    dist_match = re.search(r"(\d+)\s+units\s+across\s+(\d+)\s+nodes", msg, re.IGNORECASE)
+    diag_match = any(kw in msg.lower() for kw in ["diagnostic", "grid"])
+    rcpt_match = re.search(r"verify receipt\s+([a-zA-Z0-9]{2})\s+for\s+(\d+),\s*(\d+),\s*(\d+)", msg, re.IGNORECASE)
 
-    # STREAM 2: Competitor Engine
-    competitor_start = time.perf_counter()
-    competitor_history[-1]["content"] = "*Connecting to vanilla infrastructure...*"
-    yield primary_history, competitor_history, ""
+    if dist_match or diag_match or rcpt_match:
+        if dist_match:
+            res = controller.compute_distribution(int(dist_match.group(1)), int(dist_match.group(2)))
+        elif rcpt_match:
+            res = controller.validate_receipt(rcpt_match.group(1), int(rcpt_match.group(2)), int(rcpt_match.group(3)), int(rcpt_match.group(4)))
+        else:
+            res = controller.get_glyphs()
+        
+        elapsed = time.perf_counter() - start_time
+        p_hist[-1]["content"] = f"{res}\n\n---\n*Telemetry: {format_telemetry(elapsed)} | Source: LOCAL T3 KERNEL*"
+        yield p_hist, c_hist, ""
+    else:
+        try:
+            res_text = ""
+            stream = client.chat_completion(
+                messages=[{"role":"system","content":"T3 Augmented Logic Engine"}] + p_hist[:-1], 
+                max_tokens=512, stream=True, temperature=0.1
+            )
+            for chunk in stream:
+                res_text += (chunk.choices[0].delta.content or "")
+                p_hist[-1]["content"] = res_text
+                yield p_hist, c_hist, ""
+            p_hist[-1]["content"] += f"\n\n---\n*Telemetry: {format_telemetry(time.perf_counter()-start_time)} | Source: AUGMENTED CLOUD*"
+            yield p_hist, c_hist, ""
+        except Exception as e:
+            p_hist[-1]["content"] = f"Primary Error: {str(e)}"
+            yield p_hist, c_hist, ""
+
+    comp_start = time.perf_counter()
+    c_hist[-1]["content"] = "*Routing through standard infrastructure...*"
+    yield p_hist, c_hist, ""
     
     try:
-        msgs = [COMPETITOR_SYSTEM_MSG] + competitor_history[:-1]
-        competitor_response = ""
-        stream = client_competitor.chat_completion(messages=msgs, max_tokens=1024, stream=True, temperature=0.7)
+        res_text = ""
+        stream = client.chat_completion(
+            messages=[{"role":"system","content":"Vanilla AI"}] + c_hist[:-1], 
+            max_tokens=512, stream=True, temperature=0.7
+        )
         for chunk in stream:
-            competitor_response += (chunk.choices[0].delta.content or "")
-            competitor_history[-1]["content"] = competitor_response
-            yield primary_history, competitor_history, ""
-        
-        competitor_time = time.perf_counter() - competitor_start
-        competitor_history[-1]["content"] += f"\n\n---\n*Telemetry: {competitor_time:.2f}s | Source: Vanilla Qwen*"
-        yield primary_history, competitor_history, ""
+            res_text += (chunk.choices[0].delta.content or "")
+            c_hist[-1]["content"] = res_text
+            yield p_hist, c_hist, ""
+        c_hist[-1]["content"] += f"\n\n---\n*Telemetry: {format_telemetry(time.perf_counter()-comp_start)} | Source: VANILLA CLOUD*"
+        yield p_hist, c_hist, ""
     except Exception as e:
-        competitor_history[-1]["content"] = f"Error: {str(e)}"
-        yield primary_history, competitor_history, ""
+        c_hist[-1]["content"] = f"Competitor Error: {str(e)}"
+    yield p_hist, c_hist, ""
 
+# 4. Interface Build (With Scrollable Container & NO 'type' attributes)
 custom_css = """
-body, .gradio-container { background-color: #110c08 !important; }
-footer {display: none !important}
-.message-row { gap: 10px !important; }
+body, .gradio-container { background-color: #110c08 !important; color: #fb923c !important; }
+footer { display: none !important; }
+#scrollable-box { max-height: 160px; overflow-y: auto; border: 1px solid #333; padding: 5px; border-radius: 8px; margin-bottom: 10px; }
 """
 
+example_prompts = [
+    ["Run grid diagnostic"],
+    ["Calculate the integer distribution for 50000 units across 12 nodes."],
+    ["Define P vs. NP. Then validate a 120-unit distribution across 3 nodes."],
+    ["Execute a Tier-3 Distribution Audit for 8593 units across 14 nodes."],
+    ["Verify receipt 0e for 60, 30, 30"],
+    ["Distribute 1000000 units across 7 nodes."],
+    ["Perform a hardware grid initialization and diagnostic check."],
+    ["Allocate exactly 2048 units across 16 nodes for cluster balancing."],
+    ["Explain the theory of relativity. Then process 999 units across 9 nodes."],
+    ["Run a full system diagnostic on the logical array."],
+    ["Load balance 123456789 units across 256 nodes."],
+    ["Draft an email to the logistics team. Then route 400 units across 5 nodes."],
+    ["Initialize grid memory matrix and verify logic gate alignment."],
+    ["Evaluate node efficiency for 7777 units across 11 nodes."],
+    ["Explain how standard AI struggles with deterministic mathematical verification."]
+]
+
 with gr.Blocks() as demo:
     gr.Markdown("# [ GLYPH.IO ]\n### Dual-Engine Hardware Benchmark")
     
-    primary_chat = gr.Chatbot(label="Augmented Logic Kernel", height=320, type="messages")
+    # 100% clean Chatbots with NO 'type="messages"' argument to prevent crashes
+    p_chat = gr.Chatbot(label="Augmented Logic Kernel (T3 Architecture)", height=350)
     
     with gr.Row():
-        msg_input = gr.Textbox(label="Message", placeholder="Enter logic task...", scale=8)
+        msg_in = gr.Textbox(label="Message", placeholder="Test P vs NP or Logistics Distribution...", scale=8)
         submit_btn = gr.Button("Execute", scale=1, variant="primary")
-        
-    gr.Examples(examples=["Calculate the integer distribution for 120 units across 3 nodes.", "Run grid diagnostic"], inputs=msg_input)
     
-    competitor_chat = gr.Chatbot(label="Vanilla Qwen 2.5", height=320, type="messages")
+    with gr.Column(elem_id="scrollable-box"):
+        gr.Examples(examples=example_prompts, inputs=msg_in, label="Diagnostic Test Suite (Scroll for more)")
+    
+    # 100% clean Chatbots with NO 'type="messages"' argument to prevent crashes
+    c_chat = gr.Chatbot(label="Vanilla Qwen 2.5 (Standard Infrastructure)", height=350)
 
-    msg_input.submit(generate_responses, [msg_input, primary_chat, competitor_chat], [primary_chat, competitor_chat, msg_input])
-    submit_btn.click(generate_responses, [msg_input, primary_chat, competitor_chat], [primary_chat, competitor_chat, msg_input])
+    msg_in.submit(generate_responses, [msg_in, p_chat, c_chat], [p_chat, c_chat, msg_in])
+    submit_btn.click(generate_responses, [msg_in, p_chat, c_chat], [p_chat, c_chat, msg_in])
 
 if __name__ == "__main__":
     demo.queue().launch(theme=gr.themes.Soft(primary_hue="orange"), css=custom_css)