import gradio as gr
from huggingface_hub import InferenceClient
from dotenv import load_dotenv
import os

# Load environment variables from .env file
load_dotenv()

# Get the system message from environment variables
system_message = os.getenv("SYSTEM_MESSAGE")

client = InferenceClient(model="HuggingFaceH4/zephyr-7b-beta")

def respond(message, history, max_tokens, temperature, top_p):
    # Prepare the initial message list with the system message
    messages = [{"role": "system", "content": system_message}]

    # Add the conversation history to the messages list
    for user_msg, assistant_msg in history:
        if user_msg:
            messages.append({"role": "user", "content": user_msg})
        if assistant_msg:
            messages.append({"role": "assistant", "content": assistant_msg})

    # Add the latest user message to the messages list
    messages.append({"role": "user", "content": message})

    # Initialize an empty response string
    response = ""

    # Generate the response using the Hugging Face InferenceClient
    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

# Define the Gradio interface
demo = gr.ChatInterface(
    respond,
    additional_inputs=[
        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)"),
    ]
)

# Launch the Gradio app
if __name__ == "__main__":
    demo.launch()