import gradio as gr
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}")
model_name = "ruslanmv/Medical-Llama3-8B"
# Move model and tokenizer to the CUDA device
model = AutoModelForCausalLM.from_pretrained(model_name).to(device)
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
tokenizer.pad_token = tokenizer.eos_token
@spaces.GPU
def askme(symptoms, question):
    sys_message = '''\
    You are an AI Medical Assistant trained on a vast dataset of health information. Please be thorough and
    provide an informative answer. If you don't know the answer to a specific medical inquiry, advise seeking professional help.
    '''
    content = symptoms + " " + question
    messages = [{"role": "system", "content": sys_message}, {"role": "user", "content": content}]
    prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
    inputs = tokenizer(prompt, return_tensors="pt").to(device)  # Ensure inputs are on CUDA device
    outputs = model.generate(**inputs, max_new_tokens=200, use_cache=True)
    response_text = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0].strip() 
    # Remove system messages and content    
    # Extract and return the generated text, removing the prompt
    # Extract only the assistant's response
    answer = response_text.split('<|im_start|>assistant')[-1].strip()  
    return  answer




# Example usage
symptoms = '''\
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.
'''
question = '''\
Could these symptoms be related to hypothyroidism?
If so, what steps should I take to get a proper diagnosis and discuss treatment options?
'''
examples = [
    [symptoms, question]
]

css = """
/* General Container Styles */
.gradio-container {
    font-family: "IBM Plex Sans", sans-serif;  position: fixed; /* Ensure full-screen coverage */
    top: 0;
    left: 0;
    width: 100vw;  /* Set width to 100% viewport width */
    height: 100vh; /* Set height to 100% viewport height */
    margin: 0;    /* Remove margins for full-screen effect */
    padding: 0;    /* Remove padding fol-screen background */
    background-color: #212529; /* Dark background color */
    color: #fff;    /* Light text color for better readability */
    overflow: hidden; /* Hide potential overflow content */
    background-image: url("https://huggingface.co/spaces/ruslanmv/AI-Medical-Chatbot/resolve/main/notebook/local/img/background.jpg"); /* Replace with your image path */
    background-size: cover; /* Stretch the image to cover the container */
    background-position: center; /* Center the image horizontally and vertically */
}

/* Button Styles */
.gr-button {
    color: white;
    background: #007bff; /* Use a primary color for the background */
    white-space: nowrap;
    border: none;
    padding: 10px 20px;
    border-radius: 8px;
    cursor: pointer;
    transition: background-color 0.3s, color 0.3s;
}
.gr-button:hover {
    background-color: #0056b3; /* Darken the background color on hover */
}

/* Output box styles */
.gradio-textbox {
    background-color: #343a40; /* Dark background color */
    color: #fff; /* Light text color for better readability */
    border-color: #343a40; /* Dark border color */
    border-radius: 8px;
}
"""

welcome_message = """# AI Medical Llama 3 Chatbot
Ask any medical question giving first your symptoms and get answers from our AI Medical Llama3 Chatbot
Developed by Ruslan Magana. Visit [https://ruslanmv.com/](https://ruslanmv.com/) for more information."""







symptoms_input = gr.Textbox(label="Symptoms")
question_input = gr.Textbox(label="Question")
answer_output = gr.Textbox(label="Answer")


iface = gr.Interface(
    fn=askme,
    inputs=[symptoms_input, question_input],
    outputs=answer_output,
    examples=examples,
    css=css,
    description=welcome_message  # Add the welcome message here
)

iface.launch()

'''

with gr.Blocks(css=css) as interface:
    gr.Markdown(welcome_message)  # Display the welcome message

    with gr.Row():
        with gr.Column():
            symptoms_input = gr.Textbox(label="Symptoms", placeholder="Enter symptoms here")
            question_input = gr.Textbox(label="Question", placeholder="Enter question here")
            generate_button = gr.Button("Ask Me", variant="primary")

    with gr.Row():
        answer_output = gr.Textbox(type="text", label="Answer")

interface.launch()
'''


'''
iface = gr.Interface(
    fn=askme,
    inputs=["text", "text"],
    outputs="text",
    examples=examples,
    title="Medical AI Chatbot",
    description="Ask me a medical question!"
)

iface.launch()
'''

'''

iface = gr.Interface(
    fn=askme,
    inputs=[
        gr.Textbox(label="Symptoms", placeholder="Enter symptoms here"),
        gr.Textbox(label="Question", placeholder="Enter question here")
    ],
    outputs="text",
    examples=examples,
    title="Medical AI Chatbot",
    description="Ask me a medical question!",
    css=css
)

iface.launch()
'''