File size: 2,409 Bytes
cbceeb4 ddeb720 072654d cbceeb4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 | import torch
import torch.nn as nn
import torch.optim as optim
from transformers import AutoModel, AutoTokenizer
import gradio as gr
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() |