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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()
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