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import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, Trainer, TrainingArguments
import gradio as gr
from datasets import load_dataset
# Load the GPT-J model and tokenizer from Hugging Face
model_name = "EleutherAI/gpt-j-6B" # GPT-J model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
# Define a function for generating responses
def generate_response(prompt):
# Tokenize the input text
inputs = tokenizer(prompt, return_tensors="pt")
# Generate response using the model
outputs = model.generate(
inputs.input_ids,
max_length=200, # Adjust response length
do_sample=True, # Sample from the model
temperature=0.7, # Adjust creativity (lower = more conservative)
top_p=0.9, # Nucleus sampling for diverse answers
pad_token_id=tokenizer.eos_token_id # EOS token to avoid error
)
# Decode and return the response
return tokenizer.decode(outputs[0], skip_special_tokens=True)
# Create a Gradio interface for the chatbot
def chatbot_interface(user_input):
response = generate_response(user_input)
return response
# Fine-tuning function
def fine_tune_model(dataset_name, output_dir='./fine-tuned-model'):
# Load dataset (assuming it's in Hugging Face format or a similar format)
dataset = load_dataset(dataset_name)
# Define training arguments
training_args = TrainingArguments(
output_dir=output_dir, # Output directory for the fine-tuned model
per_device_train_batch_size=2, # Adjust batch size according to GPU capacity
num_train_epochs=1, # Adjust the number of epochs based on the dataset
save_steps=500, # Save checkpoint every 500 steps
save_total_limit=2, # Keep only last two checkpoints
)
# Define the Trainer object
trainer = Trainer(
model=model,
args=training_args,
train_dataset=dataset['train'],
eval_dataset=dataset['test'],
)
# Fine-tune the model
trainer.train()
return f"Model fine-tuned and saved to {output_dir}"
# Gradio interface for fine-tuning the model
def fine_tune_interface(dataset_name):
result = fine_tune_model(dataset_name)
return result
# Gradio UI for both chatbot and fine-tuning functionalities
with gr.Blocks() as demo:
gr.Markdown("# Cable Industry Customer Service Chatbot")
# Chatbot section
with gr.Row():
with gr.Column():
chatbot_input = gr.Textbox(label="Ask the Cable Industry Chatbot")
chatbot_output = gr.Textbox(label="Chatbot Response")
chatbot_button = gr.Button("Generate Response")
chatbot_button.click(fn=chatbot_interface, inputs=chatbot_input, outputs=chatbot_output)
# Fine-tuning section
with gr.Row():
with gr.Column():
gr.Markdown("### Fine-tune the Chatbot")
dataset_input = gr.Textbox(label="Enter Hugging Face Dataset Name (e.g., 'cable_data')")
fine_tune_button = gr.Button("Fine-tune Model")
fine_tune_output = gr.Textbox(label="Fine-tune Status")
fine_tune_button.click(fn=fine_tune_interface, inputs=dataset_input, outputs=fine_tune_output)
# Launch the Gradio interface
demo.launch()
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