import gradio as gr
from huggingface_hub import InferenceClient
from datasets import load_dataset
import threading
import time
import os

# Get Hugging Face API token from secrets
API_TOKEN = os.getenv("token")

if not API_TOKEN:
    print("ERROR: API token not found!")
else:
    print("API token retrieved successfully.")

# Initialize inference client with Zephyr-7B
client = InferenceClient("HuggingFaceH4/zephyr-7b-beta", token=API_TOKEN)


def load_data():
    """Load dataset from Hugging Face and store it in a dictionary."""
    dataset = load_dataset("accesscreate012/abhinav-academy-chatbot", split="train")
    return {entry["instruction"].strip(): entry["response"].strip() for entry in dataset}


# Global dataset
data = load_data()


def auto_update():
    """Automatically refresh the dataset every 24 hours."""
    global data
    while True:
        time.sleep(86400)  # 24 hours
        data = load_data()
        print("Dataset updated.")


# Start dataset auto-update in a separate thread
threading.Thread(target=auto_update, daemon=True).start()


def respond(
    message,
    history: list[tuple[str, str]],
    system_message,
    max_tokens,
    temperature,
    top_p,
):
    print("Received message:", message)

    # Check if the message matches an entry in the dataset
    if message.strip() in data:
        print("Found exact match in dataset.")
        yield data[message.strip()]  # Return the exact response from the dataset
        return

    print("No exact match found, using Zephyr-7B.")

    # Construct system message with dataset context
    dataset_context = "\n".join([f"Q: {q}\nA: {a}" for q, a in data.items()])
    full_system_message = (
        f"{system_message}\n\n"
        "Only use the following dataset for answers:\n"
        f"{dataset_context}\n"
        "If the exact answer is not found, infer based on the data.\n"
        "Do NOT generate unrelated information.\n"
        "Keep responses short and accurate."
    )

    # Construct conversation history
    messages = [{"role": "system", "content": full_system_message}]

    for user_input, bot_response in history:
        if user_input:
            messages.append({"role": "user", "content": user_input})
        if bot_response:
            messages.append({"role": "assistant", "content": bot_response})

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

    response = ""

    try:
        for msg in client.chat_completion(
            messages,
            max_tokens=max_tokens,
            stream=True,
            temperature=temperature,
            top_p=top_p,
        ):
            token = msg.choices[0].delta.content
            response += token
            yield response
    except Exception as e:
        print("Error during chat completion:", str(e))
        yield "An error occurred: " + str(e)


# Gradio Chat UI
demo = gr.ChatInterface(
    respond,
    additional_inputs=[
        gr.Textbox(value="You are a helpful and knowledgeable chatbot.", label="System message"),
        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max 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"),
    ],
)


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