app.py

import gradio as gr
from huggingface_hub import InferenceClient
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
import torch
import spaces  
import os
IS_SPACES_ZERO = os.environ.get("SPACES_ZERO_GPU", "0") == "1"
IS_SPACE = os.environ.get("SPACE_ID", None) is not None

device = "cuda" if torch.cuda.is_available() else "cpu"
LOW_MEMORY = os.getenv("LOW_MEMORY", "0") == "1"
print(f"Using device: {device}")
print(f"low memory: {LOW_MEMORY}")
# Define BitsAndBytesConfig
bnb_config = BitsAndBytesConfig(load_in_4bit=True,
                                bnb_4bit_quant_type="nf4",
                                bnb_4bit_compute_dtype=torch.float16)

# Model name
model_name = "ruslanmv/Medical-Llama3-v2"

# Load tokenizer and model with BitsAndBytesConfig
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True, bnb_config=bnb_config)
model = AutoModelForCausalLM.from_pretrained(model_name, config=bnb_config)

# Ensure model is on the correct device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
@spaces.GPU
# Define the respond function
def respond(
    message,
    history: list[tuple[str, str]],
    system_message,
    max_tokens,
    temperature,
    top_p,
):
    messages = [{"role": "system", "content": system_message}]

    for val in history:
        if val[0]:
            messages.append({"role": "user", "content": val[0]})
        if val[1]:
            messages.append({"role": "assistant", "content": val[1]})

    messages.append({"role": "user", "content": message})

    # Format the conversation as a single string for the model
    prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
    inputs = tokenizer(prompt, return_tensors="pt", truncation=True, padding=True, max_length=512)
    
    # Move inputs to device
    input_ids = inputs['input_ids'].to(device)
    attention_mask = inputs['attention_mask'].to(device)
    
    # Generate the response
    with torch.no_grad():
        outputs = model.generate(
            input_ids=input_ids,
            attention_mask=attention_mask,
            max_length=max_tokens,
            temperature=temperature,
            top_p=top_p,
            use_cache=True
        )
    
    # Extract the response
    response_text = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]
    
    # Remove the prompt and system message from the response
    response_text = response_text.replace(system_message, '').strip()
    response_text = response_text.replace(f"Human: {message}\n\nAssistant: ", '').strip()
    
    return response_text

# Create the Gradio interface
demo = gr.ChatInterface(
    respond,
    additional_inputs=[
        gr.Textbox(value="You are a Medical AI Assistant. Please be thorough and provide an informative answer. If you don't know the answer to a specific medical inquiry, advise seeking professional help.", label="System message", lines=3),
        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
        gr.Slider(minimum=0.1, maximum=4.0, value=0.8, step=0.1, label="Temperature"),
        gr.Slider(minimum=0.1, maximum=1.0, value=0.9, step=0.05, label="Top-p (nucleus sampling)"),
    ],
    title="Medical AI Assistant",
    description="Give me your symptoms and ask me a health problem. The AI will provide informative answers. If the AI doesn't know the answer, it will advise seeking professional help.",
   
    examples=[["I'm a 35-year-old male and for the past few months, I've been experiencing fatigue, increased sensitivity to cold, and dry, itchy skin. Could these symptoms be related to hypothyroidism?"], ["I have a headache and a fever. What should I do?"], ["How can I improve my sleep?"]],

)

if __name__ == "__main__":
    demo.launch()