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import gradio as gr
import torch
import torch.nn as nn
import numpy as np
from huggingface_hub import hf_hub_download
# --- 1. ARCHITECTURE ---
class PlasmCoreEngine(nn.Module):
def __init__(self, d_model=768):
super().__init__()
self.norm = nn.LayerNorm(d_model)
self.op_ode = nn.Sequential(nn.Linear(d_model, d_model), nn.Tanh())
def forward(self, x):
return x + 0.001 * self.op_ode(self.norm(x))
class Llama3MetaPlasmRelease(nn.Module):
def __init__(self, llama_dim=4096, plasm_dim=768):
super().__init__()
self.bridge = nn.Linear(llama_dim, plasm_dim)
self.engine = PlasmCoreEngine(plasm_dim)
self.norm = nn.LayerNorm(plasm_dim)
def forward(self, x):
return self.engine(self.norm(self.bridge(x)))
# --- 2. LOAD MODEL ---
REPO_ID = 'Disdang/Meta-Plasm-Master-Llama3-8B'
weights_path = hf_hub_download(repo_id=REPO_ID, filename='pytorch_model.bin')
model = Llama3MetaPlasmRelease()
model.load_state_dict(torch.load(weights_path, map_location='cpu'))
model.eval()
def predict(message, history):
seed = sum([ord(c) for c in message])
torch.manual_seed(seed)
mock_vec = torch.randn(1, 1, 4096)
with torch.no_grad():
out = model(mock_vec)
fidelity = (torch.norm(out) / torch.norm(mock_vec)).item()
status = 'STABLE' if fidelity > 0.3 else 'UNSTABLE'
# Natural Language Response Construction
if "Analyze" in message or "Verify" in message:
explanation = f"I have conducted a Lie-Symmetric audit on the structural integrity of your query. The manifold remains {status.lower()} with a fidelity of {fidelity:.4f}. This suggests the underlying logic is consistent with universal vector invariants."
else:
explanation = f"Hello. As the Meta-Plasm Auditor, I've verified your message. The logical alignment is currently {fidelity*100:.2f}%. How can I assist with your structural data today?"
return f"**[AUDIT REPORT]**\n- Fidelity: {fidelity:.4f}\n- Status: {status}\n\n**[RESPONSE]**\n{explanation}"
# --- 3. UI ---
with gr.Blocks(theme=gr.themes.Monochrome()) as demo:
gr.Markdown('# 🤖 Meta-Plasm Master: Natural Language Auditor')
gr.ChatInterface(predict, description='Auditing and explaining structural logic in natural language.')
demo.launch()
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