Update README.md

README.md

@@ -1,112 +1,23 @@
----
-license: apache-2.0
-datasets:
-- stanfordnlp/imdb
-- uoft-cs/cifar10
-- superlazycoder/slc-titanic
-language:
-- en
-metrics:
-- bertscore
-library_name: transformers
-pipeline_tag: text-generation
-tags:
-- code
-- medical
----
-# Agentic Unified Mind UANN
-
-This repository contains the implementation of the Agentic Unified Mind Universal Adaptive Neural Network (UANN), a multi-modal AI model designed to integrate text, image, and structured data processing. The model uses advanced neural network architectures and reinforcement learning to deliver robust performance across various applications.
+# UANN Model
 
 ## Model Description
 
-The Agentic Unified Mind UANN integrates:
-- Text processing using BERT.
-- Image processing using ResNet50.
-- Structured data processing with dense neural networks.
-- Reinforcement learning for autonomous decision-making.
-
-## Features
-
-- **Multi-modal Inputs:** Handles text, images, and structured data.
-- **Advanced Neural Network Architectures:** Uses BERT for text, ResNet50 for images, and dense layers for structured data.
-- **Unified Cognitive Framework:** Combines information from multiple modalities for better decision-making.
-- **Reinforcement Learning:** Enhances the model's ability to learn and adapt from interactions.
-
-## Setup
-
-### 1. Installation
-
-Install the required dependencies:
-
-```bash
-pip install -r requirements.txt
-```
-
-### 2. Model Training
-
-To train the model, run:
-
-```bash
-python app.py
-```
-
-### 3. API Integration
-
-The project includes a Flask API for storing and retrieving model predictions.
-
-**API Setup:**
-
-1. Install Flask and necessary libraries:
-
-    ```bash
-    pip install flask flask_sqlalchemy flask_cors
-    ```
-
-2. Configure your database URI in `api.py`.
-
-3. Run the Flask API:
-
-    ```bash
-    python api.py
-    ```
-
-### 4. Gradio Interface
-
-To launch the Gradio interface:
-
-```bash
-python app.py
-```
-
-### Directory Structure
-
-```
-agentic_uann_model/
-├── app.py
-├── api.py
-├── requirements.txt
-└── models/
-    └── model_files/
-```
-
-## Deployment
-
-1. Push your repository to Hugging Face Spaces.
-2. Navigate to Hugging Face Spaces and create a new Space.
-3. Select "Gradio" as the framework.
-4. Connect your GitHub repository or upload the files directly.
-5. Choose the desired hardware, such as an A100 40GB GPU.
+This is the Universal Adaptive Neural Network (UANN) designed for multi-modal AI agents. The model incorporates a Mixture of Experts (MoE) architecture.
 
 ## Usage
 
-- **Chat Interface:** Interact with the model using a chat interface.
-- **Code Execution:** Execute code snippets and view outputs.
-
-## License
+```python
+import torch
+from models.moe_model import MoEModel
 
-This project is licensed under the Apache 2.0 License. See the [LICENSE](LICENSE) file for more details.
+# Initialize model
+model = MoEModel(input_dim=512, num_experts=3)
 
----
+# Dummy inputs for testing
+vision_input = torch.randn(1, 3, 32, 32)
+audio_input = torch.randn(1, 100, 40)
+sensor_input = torch.randn(1, 10)
 
-By following this guide, you will be able to set up and deploy the Agentic Unified Mind UANN, leveraging its multi-modal processing capabilities and reinforcement learning framework.
+# Forward pass
+output = model(vision_input, audio_input, sensor_input)
+print(output)