app.py

import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
import torch
import spaces

# Define quantization configuration
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,  # Specify 4-bit quantization
    bnb_4bit_use_double_quant=True,  # Use double quantization for better efficiency
    bnb_4bit_quant_type="nf4",  # Set the quantization type to NF4
    bnb_4bit_compute_dtype=torch.float16  # Use float16 for computations
)

# Load the tokenizer and quantized model from Hugging Face
model_name = "llSourcell/medllama2_7b"
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Load model with quantization
model = AutoModelForCausalLM.from_pretrained(model_name, 
                                             quantization_config=quantization_config, 
                                             device_map="auto")
model.eval()

def format_history(msg: str, history: list[list[str, str]], system_prompt: str):
    chat_history = system_prompt
    for query, response in history:
        chat_history += f"\nUser: {query}\nAssistant: {response}"
    chat_history += f"\nUser: {msg}\nAssistant:"
    return chat_history

@spaces.GPU(duration=30)
def generate_response(msg: str, history: list[list[str, str]], system_prompt: str):
    chat_history = format_history(msg, history, system_prompt)
    
    # Tokenize the input prompt
    inputs = tokenizer(chat_history, return_tensors="pt").to("cuda")
    
    # Generate a response using the model
    outputs = model.generate(inputs["input_ids"], max_length=1024, pad_token_id=tokenizer.eos_token_id)

    # Decode the response back to a string
    response = tokenizer.decode(outputs[:, inputs["input_ids"].shape[-1]:][0], skip_special_tokens=True)
    
    # Yield the generated response
    yield response

# Define the Gradio ChatInterface
chatbot = gr.ChatInterface(
                generate_response,
                chatbot=gr.Chatbot(
                        height="64vh"
                    ),
                additional_inputs=[
                    gr.Textbox(
                        "Behave as if you are a medical doctor providing answers for patients' clinical questions.",
                        label="System Prompt"
                    )
                ],
                title="Medical QA Chat",
                description="Feel free to ask any question to Medllama2 Chatbot.",
                theme="soft",
                submit_btn="Send",
                retry_btn="Regenerate Response",
                undo_btn="Delete Previous",
                clear_btn="Clear Chat"
)

# Following line is important to queue the messages
chatbot.queue()

# Enable share = True if you want to create a public link for people to use your application
chatbot.launch()