app.py

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

# Read data from DOCX file
def read_docx(file_path):
    doc = Document(file_path)
    text = ""
    for paragraph in doc.paragraphs:
        text += paragraph.text + "\n"
    return text

# Load and tokenize your conversational dataset
docx_file_path = "test.txt"
text_data = read_docx(docx_file_path)

# Tokenization
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
tokenized_text = tokenizer.encode(text_data, return_tensors="pt")

# Define dataset
class TextDataset(torch.utils.data.Dataset):
    def __init__(self, tokenized_text):
        self.tokenized_text = tokenized_text

    def __len__(self):
        return len(self.tokenized_text)

    def __getitem__(self, idx):
        return self.tokenized_text[idx]

dataset = TextDataset(tokenized_text)

# 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()