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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()
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