app.py

import logging
import os
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Set up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# Checkpoint paths
model_checkpoint_path = "model_checkpoint.pth"
tokenizer_checkpoint_path = "tokenizer_checkpoint.pth"

# Load model and tokenizer from checkpoint if they exist
if os.path.exists(model_checkpoint_path) and os.path.exists(tokenizer_checkpoint_path):
    try:
        model = torch.load(model_checkpoint_path)
        tokenizer = torch.load(tokenizer_checkpoint_path)
        logger.info("Model and tokenizer loaded from checkpoint.")
    except Exception as e:
        logger.error(f"Failed to load model or tokenizer from checkpoint: {e}")
        raise
else:
    # Load model directly
    try:
        tokenizer = AutoTokenizer.from_pretrained("google/gemma-2-9b")
        model = AutoModelForCausalLM.from_pretrained("google/gemma-2-9b")
        logger.info("Model and tokenizer loaded successfully.")
    except Exception as e:
        logger.error(f"Failed to load model or tokenizer: {e}")
        raise

def respond(user_input, history, system_message, max_tokens=20, temperature=0.9, top_p=0.9):
    messages = [{"role": "system", "content": system_message}]
    messages.extend(history)
    messages.append({"role": "user", "content": user_input})

    # Convert messages to a single string
    input_text = " ".join([msg["content"] for msg in messages])

    # Tokenize the input text
    inputs = tokenizer(input_text, return_tensors="pt")

    # Generate attention mask
    attention_mask = inputs["attention_mask"]

    # Generate text using the model
    outputs = model.generate(
        inputs.input_ids,
        attention_mask=attention_mask,
        max_length=max_tokens,
        temperature=temperature,
        top_p=top_p,
        pad_token_id=tokenizer.eos_token_id,
        do_sample=True
    )

    # Decode the generated text
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)

    return response

if __name__ == "__main__":
    print("Welcome to the Chatbot!")
    while True:
        user_input = input("You: ")
        system_message = "Chatbot: "
        history = [{"role": "assistant", "content": "Hello, how can I assist you today?"}]
        response = respond(user_input, history, system_message)
        print(response)