interface.py

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()