app.py

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()