app.py

import gradio as gr
from huggingface_hub import InferenceClient

# Initialize the InferenceClient
client = InferenceClient("meta-llama/Llama-3.2-3B-Instruct")

def is_health_related(message):
    # Simple heuristic to check if the message is health-related
    health_keywords = ["health", "medical", "disease", "symptom", "treatment", "doctor", "patient", "medicine"]
    message = message.lower()
    for keyword in health_keywords:
        if keyword in message:
            return True
    return False

def respond(
    message,
    history: list[tuple[str, str]],
    system_message,
    max_tokens,
    temperature,
    top_p,
):
    if not is_health_related(message):
        return "Sorry, I can't help you with that because I am just a bot who can help with health-related queries."

    messages = [{"role": "system", "content": system_message}]

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

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

    response = ""

    for message in client.chat_completion(
        messages,
        max_tokens=max_tokens,
        stream=True,
        temperature=temperature,
        top_p=top_p,
    ):
        token = message.choices.delta.content

        response += token
        yield response

# Custom CSS to make it look like ChatGPT
css = """
body {
    font-family: 'Inter', sans-serif;
    background-color: #f0f0f0;
}

.gradio-container {
    max-width: 700px;
    margin: auto;
}

.gr-chat-container {
    border: 1px solid #ddd;
    border-radius: 5px;
    padding: 10px;
    background-color: #fff;
}

.gr-message {
    padding: 10px;
    border-bottom: 1px solid #ddd;
}

.gr-message:last-child {
    border-bottom: none;
}

.gr-user-message {
    color: #333;
    background-color: #f7f7f7;
    border-radius: 5px;
    padding: 5px;
}

.gr-assistant-message {
    color: #333;
    background-color: #fff;
    border-radius: 5px;
    padding: 5px;
}

.gr-input {
    padding: 10px;
    border: 1px solid #ccc;
    border-radius: 5px;
    width: 100%;
}

.gr-input:focus {
    border-color: #aaa;
}

.gr-button {
    background-color: #4CAF50;
    color: #fff;
    padding: 10px 20px;
    border: none;
    border-radius: 5px;
    cursor: pointer;
}

.gr-button:hover {
    background-color: #3e8e41;
}
"""

# Create a custom chat interface using gr.Blocks
with gr.Blocks(css=css) as demo:
    gr.Markdown("# Health Assistant Chatbot")
    gr.Markdown("### Ask me any health-related questions.")
    chatbot = gr.Chatbot(
        value=[],
        show_user_avatar=False,
        show_bot_avatar=False,
        width="100%",
    )
    input_box = gr.Textbox(
        label="Type your message here",
        placeholder="Type your message here",
        show_label=False,
    )
    system_message = gr.Textbox(
        value="You are a virtual Doctor Assistant. Your role is to assist healthcare professionals by providing accurate, evidence-based medical information, offering treatment options, and supporting patient care. Always prioritize patient safety, provide concise answers, and clearly state that your advice does not replace a doctor's judgment. Do not diagnose or prescribe treatments without human oversight.",
        label="System message",
        visible=False,
    )
    max_tokens = gr.Slider(
        minimum=1,
        maximum=2048,
        value=512,
        step=1,
        label="Max new tokens",
        visible=False,
    )
    temperature = gr.Slider(
        minimum=0.1,
        maximum=4.0,
        value=0.7,
        step=0.1,
        label="Temperature",
        visible=False,
    )
    top_p = gr.Slider(
        minimum=0.1,
        maximum=1.0,
        value=0.95,
        step=0.05,
        label="Top-p (nucleus sampling)",
        visible=False,
    )

    def update_chat(message, history, system_message, max_tokens, temperature, top_p):
        response = respond(message, history, system_message, max_tokens, temperature, top_p)
        return chatbot.update(value=history + [(message, response)])

    input_box.submit(
        update_chat,
        inputs=[input_box, chatbot, system_message, max_tokens, temperature, top_p],
        outputs=chatbot,
    )

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