app.py

from docx import Document
from transformers import GPT2Tokenizer, GPT2LMHeadModel, Trainer, TrainingArguments
import torch
import gradio as gr

model_name = "gpt2"
model = GPT2LMHeadModel.from_pretrained(model_name)
tokenizer = GPT2Tokenizer.from_pretrained(model_name)
 
# Load and tokenize your conversational dataset
dataset = TextDataset(tokenizer=tokenizer, file_path="your_dataset.txt")

# Load and tokenize your conversational dataset
dataset = TextDataset(tokenizer=tokenizer, file_path="your_dataset.txt")
 
# Define data collator
data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)
 
# Define training arguments
training_args = TrainingArguments(
    output_dir="./output",
    overwrite_output_dir=True,
    num_train_epochs=3,
    per_device_train_batch_size=4,
    save_steps=10_000,
    save_total_limit=2,
)
 
# Initialize Trainer
trainer = Trainer(
    model=model,
    args=training_args,
    data_collator=data_collator,
    train_dataset=dataset,
)
 
# Fine-tune the model
trainer.train()
 
# Evaluate the fine-tuned model
trainer.evaluate()

# Define the chatbot function
def chatbot(input_text):
    # Tokenize input text
    input_ids = tokenizer.encode(input_text, return_tensors="pt")

    # Generate response from the model
    output_ids = model.generate(input_ids, max_length=50, pad_token_id=tokenizer.eos_token_id)
    
    # Decode the generated response
    response = tokenizer.decode(output_ids[0], skip_special_tokens=True)
    
    return response

# Create the Gradio interface
chatbot_interface = gr.Interface(chatbot, "textbox", "textbox", title="Chatbot")

# Launch the Gradio interface
chatbot_interface.launch()