import gradio as gr
from huggingface_hub import InferenceClient

# Initialize the model and tokenizer
client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")

# Define the conversation flow
def respond(
    message,
    history: list[tuple[str, str]],
    system_message,
    max_tokens,
    temperature,
    top_p,
):
    #... (rest of the code remains the same)

# Create the chat interface
demo = gr.ChatInterface(
    respond,
    title="NVS AI: Health Conversational Chatbot",
    description="Get answers to your health-related questions!",
    additional_inputs=[
        gr.Textbox(value="You are a friendly health chatbot.", label="System message"),
        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
        gr.Slider(
            minimum=0.1,
            maximum=1.0,
            value=0.95,
            step=0.05,
            label="Top-p (nucleus sampling)",
        ),
    ],
)

# Add custom CSS to customize the theme
css = """
body {
    background-color: #f9f9f9;
}

.gradio-container {
    max-width: 800px;
    margin: 40px auto;
    padding: 20px;
    border: 1px solid #ddd;
    border-radius: 10px;
    box-shadow: 0 0 10px rgba(0, 0, 0, 0.1);
}

.gradio-input {
    background-color: #fff;
    border: 1px solid #ccc;
    padding: 10px;
    border-radius: 10px;
}

.gradio-button {
    background-color: #3498db;
    color: #fff;
    border: none;
    padding: 10px 20px;
    border-radius: 10px;
    cursor: pointer;
}

.gradio-button:hover {
    background-color: #2980b9;
}
"""
demo.css(css)

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