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Project Structure.txt

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+AgenticDeveloper/
+│
+├── agent.py
+├── app.py
+├── network.py
+├── requirements.txt
+├── train.py
+├── interface.py
+└── README.md

Readme.md

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+# AgenticDeveloper
+
+This project implements a text-based developer assistant using a Mixture of Experts (MoE) model. The assistant consists of a prime agent and several secondary agents specialized in different tasks such as code writing, code debugging, and code optimization.
+
+## Project Structure
+
+- **`agent.py`**: Defines the PrimeAgent and SecondaryAgent classes.
+- **`app.py`**: Main entry point for running the Gradio interface.
+- **`network.py`**: Contains functions to create neural network models for text processing and the gating network.
+- **`requirements.txt`**: Lists the dependencies required for the project.
+- **`train.py`**: Implements the training and evaluation pipeline.
+- **`interface.py`**: Sets up the Gradio interface for the chat window and code display.
+- **`README.md`**: Project documentation and setup instructions.
+
+## Setup Instructions
+
+Follow these steps to set up the project on your local machine:
+
+1. **Clone the repository**:
+    ```sh
+    git clone https://github.com/Dnnsdesigns/AgenticDeveloper.git
+    cd AgenticDeveloper
+    ```
+
+2. **Create a virtual environment** (optional but recommended):
+    ```sh
+    python -m venv venv
+    source venv/bin/activate  # On Windows use `venv\Scripts\activate`
+    ```
+
+3. **Install the required dependencies**:
+    ```sh
+    pip install -r requirements.txt
+    ```
+
+4. **Run the application**:
+    ```sh
+    python app.py
+    ```
+
+## Usage
+
+The AgenticDeveloper provides an interactive Gradio interface with two main components:
+1. **Chat Window**: Where you can interact with the assistant to get code suggestions, debugging help, and more.
+2. **Code Display Window**: Displays the code snippets generated by the assistant.
+
+To use the assistant, enter your request in the chat window and the assistant will respond with suggestions or code snippets based on the input.
+
+## Contribution Guidelines
+
+We welcome contributions to the AgenticDeveloper project! Here are some guidelines to help you get started:
+
+1. **Fork the repository**: Create a fork of the repository on your GitHub account.
+2. **Clone your fork**: Clone the forked repository to your local machine.
+    ```sh
+    git clone https://github.com/YOUR_USERNAME/AgenticDeveloper.git
+    cd AgenticDeveloper
+    ```
+3. **Create a new branch**: Create a new branch for your feature or bugfix.
+    ```sh
+    git checkout -b feature/your-feature-name
+    ```
+4. **Make your changes**: Implement your feature or bugfix.
+5. **Commit your changes**: Commit your changes with a descriptive commit message.
+    ```sh
+    git commit -m "Add feature/your-feature-name"
+    ```
+6. **Push your changes**: Push your changes to your forked repository.
+    ```sh
+    git push origin feature/your-feature-name
+    ```
+7. **Create a Pull Request**: Create a pull request from your forked repository to the main repository.
+
+## Future Work
+
+Here are some potential future improvements and features for the AgenticDeveloper:
+
+- **Expand to Multimodal Inputs**: Add support for image and audio inputs to provide

agent.py

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+import numpy as np
+
+class SecondaryAgent:
+    def __init__(self, model, specialty):
+        self.model = model
+        self.specialty = specialty
+    
+    def predict(self, state):
+        return self.model.predict(state)
+
+class PrimeAgent:
+    def __init__(self, gating_network, experts):
+        self.gating_network = gating_network
+        self.experts = experts
+    
+    def act(self, state):
+        gating_weights = self.gating_network.predict(state)
+        expert_outputs = [expert.predict(state) for expert in self.experts]
+        
+        # Weighted sum of expert outputs based on gating weights
+        combined_output = np.sum([weight * output for weight, output in zip(gating_weights[0], expert_outputs)], axis=0)
+        action = np.argmax(combined_output)
+        return action
+    
+    def train(self, states, actions, rewards):
+        self.gating_network.fit(states, actions, sample_weight=rewards, epochs=1, verbose=0)
+        for expert in self.experts:
+            expert.model.fit(states, actions, sample_weight=rewards, epochs=1, verbose=0)

interface.py

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+import gradio as gr
+import tensorflow as tf
+import numpy as np
+from datasets import load_dataset
+from network import create_text_neural_network, create_gating_network
+from agent import PrimeAgent, SecondaryAgent
+
+# Define the neural networks and agents as described above
+vocab_size = 10000
+embedding_dim = 128
+input_length = 100
+num_classes = 10
+num_experts = 3
+
+gating_network = create_gating_network((input_length,), num_experts)
+expert_networks = [create_text_neural_network(vocab_size, embedding_dim, input_length, num_classes) for _ in range(num_experts)]
+specialties = ['code writing', 'code debugging', 'code optimization']
+secondary_agents = [SecondaryAgent(expert_networks[i], specialties[i]) for i in range(num_experts)]
+prime_agent = PrimeAgent(gating_network, secondary_agents)
+
+# Define a simple function to handle chat input and produce a response
+def developer_assistant(input_text):
+    # For simplicity, use a random response from one of the experts
+    response = "Understood. Here's what I can suggest:"
+    # Convert the input text to numerical data
+    tokenizer = tf.keras.preprocessing.text.Tokenizer(num_words=vocab_size)
+    tokenizer.fit_on_texts([input_text])
+    input_data = tokenizer.texts_to_sequences([input_text])
+    input_data = tf.keras.preprocessing.sequence.pad_sequences(input_data, maxlen=input_length)
+
+    # Use the prime agent to get the action (response)
+    action = prime_agent.act(input_data)
+    
+    response += f"\\nExpert {action}: {specialties[action]}."
+    return response
+
+# Define a function to display code (placeholder for actual functionality)
+def display_code():
+    code_snippet = '''
+    def example_function(param1, param2):
+        # Example function
+        result = param1 + param2
+        return result
+    '''
+    return code_snippet
+
+# Create the Gradio interface
+gr_interface = gr.Interface(
+    fn=developer_assistant,
+    inputs=gr.inputs.Textbox(lines=5, placeholder="Enter your request here..."),
+    outputs=[
+        gr.outputs.Textbox(label="Response"),
+        gr.outputs.Code(language="python", label="Generated Code")
+    ],
+    title="Developer Assistant Chat Interface",
+    description="Interact with the assistant to get code suggestions, debugging help, and more."
+)
+
+# Launch the interface
+gr_interface.launch()

network.py

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+import tensorflow as tf
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Embedding, LSTM, Dense, Flatten
+
+def create_text_neural_network(vocab_size, embedding_dim, input_length, num_classes):
+    model = Sequential([
+        Embedding(input_dim=vocab_size, output_dim=embedding_dim, input_length=input_length),
+        LSTM(128, return_sequences=True),
+        LSTM(128),
+        Dense(64, activation='relu'),
+        Dense(num_classes, activation='softmax')
+    ])
+    model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
+    return model
+
+def create_gating_network(input_shape, num_experts):
+    model = Sequential([
+        Flatten(input_shape=input_shape),
+        Dense(128, activation='relu'),
+        Dense(num_experts, activation='softmax')
+    ])
+    model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
+    return model

python-app.yml

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+name: Python application
+
+on:
+  push:
+    branches: [ main ]
+  pull_request:
+    branches: [ main ]
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+
+    strategy:
+      matrix:
+        python-version: [3.8, 3.9, 3.10]
+
+    steps:
+    - uses: actions/checkout@v3
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v4
+      with:
+        python-version: ${{ matrix.python-version }}
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        pip install -r requirements.txt
+    - name: Test with pytest
+      run: |
+        pip install pytest
+        pytest

requirements.txt

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+tensorflow
+gradio
+datasets

train.py

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+import gradio as gr
+import tensorflow as tf
+import numpy as np
+from datasets import load_dataset
+from network import create_text_neural_network, create_gating_network
+from agent import PrimeAgent, SecondaryAgent
+
+# Verify GPU availability
+print("Num GPUs Available: ", len(tf.config.list_physical_devices('GPU')))
+
+# Define the model training and evaluation function
+def train_and_test_model(epochs, batch_size):
+    vocab_size = 10000
+    embedding_dim = 128
+    input_length = 100
+    num_classes = 10
+    num_experts = 3  # Number of experts
+
+    # Create models for the gating network and secondary agents
+    gating_network = create_gating_network((input_length,), num_experts)
+    expert_networks = [create_text_neural_network(vocab_size, embedding_dim, input_length, num_classes) for _ in range(num_experts)]
+    
+    # Define specialties for secondary agents
+    specialties = ['code writing', 'code debugging', 'code optimization']
+
+    # Create secondary agents
+    secondary_agents = [SecondaryAgent(expert_networks[i], specialties[i]) for i in range(num_experts)]
+
+    # Create prime agent with secondary agents
+    prime_agent = PrimeAgent(gating_network, secondary_agents)
+
+    # Load dataset using Hugging Face datasets library
+    dataset = load_dataset('imdb')
+    train_data = np.array([example['text'][:input_length] for example in dataset['train']])
+    train_labels = np.array([example['label'] for example in dataset['train']])
+    test_data = np.array([example['text'][:input_length] for example in dataset['test']])
+    test_labels = np.array([example['label'] for example in dataset['test']])
+
+    # Convert text to numerical data (tokenization)
+    tokenizer = tf.keras.preprocessing.text.Tokenizer(num_words=vocab_size)
+    tokenizer.fit_on_texts(train_data)
+    train_data = tokenizer.texts_to_sequences(train_data)
+    test_data = tokenizer.texts_to_sequences(test_data)
+    train_data = tf.keras.preprocessing.sequence.pad_sequences(train_data, maxlen=input_length)
+    test_data = tf.keras.preprocessing.sequence.pad_sequences(test_data, maxlen=input_length)
+
+    # Train and test the prime agent's model
+    results = ""
+    with tf.device('/GPU:0'):
+        prime_agent.gating_network.fit(train_data, train_labels, epochs=epochs, batch_size=batch_size)
+        test_loss, test_acc = prime_agent.gating_network.evaluate(test_data, test_labels, verbose=2)
+        results += f'Gating Network Test Accuracy: {test_acc}\\n'
+        
+        for expert in prime_agent.experts:
+            expert.model.fit(train_data, train_labels, epochs=epochs, batch_size=batch_size)
+            test_loss, test_acc = expert.model.evaluate(test_data, test_labels, verbose=2)
+            results += f'{expert.specialty.capitalize()} Expert Test Accuracy: {test_acc}\\n'
+
+    return results
+
+# Define the Gradio interface
+gr_interface = gr.Interface(
+    fn=train_and_test_model,
+    inputs=[
+        gr.inputs.Slider(minimum=1, maximum=50, step=1, default=10, label="Epochs"),
+        gr.inputs.Slider(minimum=16, maximum=512, step=16, default=128, label="Batch Size")
+    ],
+    outputs="text",
+    title="Developer Assistant Training Interface",
+    description="Adjust the training parameters and train the model."
+)
+
+# Launch the interface
+gr_interface.launch()