app.py

import gradio as gr
import os
import spaces
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
from threading import Thread

# Set an environment variable
HF_TOKEN = os.environ.get("HF_TOKEN", None)

DESCRIPTION = '''
<div>
  <h1 style="text-align: center;">A.I. Healthcare</h1>
</div>
'''

LICENSE = """
<p>
This Health Assistant is designed to provide helpful healthcare information; however, it may make mistakes and is not designed to replace professional medical care. It is not intended to diagnose any condition or disease. Always consult with a qualified healthcare provider for any medical concerns.
</p>
"""

PLACEHOLDER = """
<div style="padding: 30px; text-align: center; display: flex; flex-direction: column; align-items: center;">
   <h1 style="font-size: 28px; margin-bottom: 2px; opacity: 0.55;">A.I. Healthcare</h1>
   <p style="font-size: 18px; margin-bottom: 2px; opacity: 0.65;">Ask me anything...</p>
</div>
"""

css = """
h1 {
  text-align: center;
  display: block;
}

#duplicate-button {
  margin: auto;
  color: white;
  background: #1565c0;
  border-radius: 100vh;
}
"""

# Load the tokenizer and model with the updated model name
tokenizer = AutoTokenizer.from_pretrained("reedmayhew/HealthCare-Reasoning-Assistant-Llama-3.1-8B-HF", device_map="cuda")
model = AutoModelForCausalLM.from_pretrained("reedmayhew/HealthCare-Reasoning-Assistant-Llama-3.1-8B-HF", device_map="cuda")

terminators = [
    tokenizer.eos_token_id,
    tokenizer.convert_tokens_to_ids("<|eot_id|>")
]

@spaces.GPU(duration=60)
def chat_llama3_8b(message: str, 
                   history: list, 
                   temperature: float, 
                   max_new_tokens: int,
                   confirm: bool) -> str:
    """
    Generate a streaming response using the Healthcare-Reasoning-Assistant-Llama-3.1-8B-HF model.
    
    Args:
        message (str): The input message.
        history (list): The conversation history.
        temperature (float): The temperature for generating the response.
        max_new_tokens (int): The maximum number of new tokens to generate.
        confirm (bool): Whether the user has confirmed the usage disclaimer.
        
    Yields:
        str: The generated response, streamed token-by-token.
    """
    # Ensure the user has confirmed the disclaimer
    if not confirm:
        return "⚠️ You must confirm that you meet the usage requirements before sending a message."
    
    # Prepare the conversation history for the model input
    conversation = []
    for user, assistant in history:
        conversation.extend([
            {"role": "user", "content": user}, 
            {"role": "assistant", "content": assistant}
        ])
    
    # Append the current user message
    conversation.append({"role": "user", "content": message})
    
    # Convert the conversation into input ids using the chat template
    input_ids = tokenizer.apply_chat_template(conversation, return_tensors="pt").to(model.device)
    
    # Set up the streamer to stream text output
    streamer = TextIteratorStreamer(tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True)
    
    generate_kwargs = dict(
        input_ids=input_ids,
        streamer=streamer,
        max_new_tokens=max_new_tokens,
        do_sample=True,
        temperature=temperature,
        eos_token_id=terminators,
    )
    
    if temperature == 0:
        generate_kwargs['do_sample'] = False
        
    # Launch the generation in a separate thread
    t = Thread(target=model.generate, kwargs=generate_kwargs)
    t.start()
    
    full_response = ""
    # Simply stream each token as it comes from the model
    for text in streamer:
        full_response += text
        yield text

    # Save the full response (for context in the conversation history)
    history.append((message, full_response))

# Custom JavaScript to disable the send button until confirmation is given.
CUSTOM_JS = """
<script>
document.addEventListener("DOMContentLoaded", function() {
    const interval = setInterval(() => {
        const checkbox = document.querySelector('input[type="checkbox"][aria-label*="I hereby confirm that I am at least 18 years of age"]');
        const sendButton = document.querySelector('button[title="Send"]');
        if (checkbox && sendButton) {
            sendButton.disabled = !checkbox.checked;
            checkbox.addEventListener('change', function() {
                sendButton.disabled = !checkbox.checked;
            });
            clearInterval(interval);
        }
    }, 500);
});
</script>
"""

with gr.Blocks(css=css, title="A.I. Healthcare") as demo:
    gr.Markdown(DESCRIPTION)
    gr.HTML(CUSTOM_JS)
    
    chat_interface = gr.ChatInterface(
        fn=chat_llama3_8b,
        title="A.I. Healthcare Chat",
        chatbot=gr.Chatbot(height=450, placeholder=PLACEHOLDER, label='Conversation'),
        additional_inputs=[
            gr.Checkbox(
                value=False,
                label=("I hereby confirm that I am at least 18 years of age (or accompanied by a legal guardian "
                       "who is at least 18 years old), understand that the information provided by this service "
                       "is for informational purposes only and is not intended to diagnose or treat any medical condition, "
                       "and acknowledge that I am solely responsible for verifying any information provided."),
                elem_id="age_confirm_checkbox"
            ),
            gr.Slider(minimum=0.6, maximum=0.6, step=0.1, value=0.6, label="Temperature", visible=False),
            gr.Slider(minimum=128, maximum=4096, step=64, value=1024, label="Max new tokens", visible=False),
        ],
        examples=[
            ['What are the common symptoms of diabetes?'],
            ['How can I manage high blood pressure with lifestyle changes?'],
            ['What nutritional advice can help improve heart health?'],
            ['Can you explain the benefits of regular exercise for mental well-being?'],
            ['What should I know about the side effects of common medications?']
        ],
        cache_examples=False,
    )
    
    gr.Markdown(LICENSE)

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