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, 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})

    response = ""

    for message in client.chat_completion(
        messages,
        max_tokens=max_tokens,
        stream=True,
        temperature=temperature,
        top_p=top_p,
    ):
        token = message.choices[0].delta.content

        response += token
        yield response

# Create the chat interface
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 = gr.Interface(
    fn=respond,
    inputs=["text", "state", "text", "number", "number", "number"],
    outputs="text",
    title="NVS AI: Health Conversational Chatbot",
    description="Get answers to your health-related questions!",
)

demo.launch()