import gradio as gr
from openai import OpenAI
import os

# Retrieve the access token from the environment variable
ACCESS_TOKEN = os.getenv("HF_TOKEN")
print("Access token loaded.")

# Initialize the OpenAI client with the Hugging Face Inference API endpoint
client = OpenAI(
    base_url="https://api-inference.huggingface.co/v1/",
    api_key=ACCESS_TOKEN,
)
print("OpenAI client initialized.")

def respond(
    message,
    history: list[tuple[str, str]],
    system_message,
    max_tokens,
    temperature,
    top_p,
    frequency_penalty,
    seed,
    model,
    custom_model
):
    """
    Handles the chatbot response with given parameters.
    """
    print(f"Received message: {message}")
    print(f"History: {history}")
    print(f"System message: {system_message}")
    print(f"Model: {model}, Custom Model: {custom_model}")

    # Use custom model if provided, else use selected model
    selected_model = custom_model.strip() if custom_model.strip() else model
    print(f"Selected model: {selected_model}")

    # Construct the messages array required by the API
    messages = [{"role": "system", "content": system_message}]

    # Add conversation history to the context
    for val in history:
        user_part = val[0]
        assistant_part = val[1]
        if user_part:
            messages.append({"role": "user", "content": user_part})
            print(f"Added user message to context: {user_part}")
        if assistant_part:
            messages.append({"role": "assistant", "content": assistant_part})
            print(f"Added assistant message to context: {assistant_part}")

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

    # Start with an empty string to build the response as tokens stream in
    response = ""
    print("Sending request to OpenAI API.")

    # Make the streaming request to the HF Inference API via OpenAI-like client
    for message_chunk in client.chat.completions.create(
        model=selected_model,
        max_tokens=max_tokens,
        stream=True,
        temperature=temperature,
        top_p=top_p,
        frequency_penalty=frequency_penalty,
        seed=seed if seed != -1 else None,
        messages=messages,
    ):
        # Extract the token text from the response chunk
        token_text = message_chunk.choices[0].delta.content
        print(f"Received token: {token_text}")
        response += token_text
        yield response

    print("Completed response generation.")

# Create a Chatbot component
chatbot = gr.Chatbot(height=600)
print("Chatbot interface created.")

# Define the featured models for the dropdown
models_list = [
    "meta-llama/Llama-3.3-70B-Instruct",
    "bigscience/bloom-176b",
    "gpt-j-6b",
    "opt-30b",
    "flan-t5-xxl",
]

# Function to filter models based on user input
def filter_models(search_term):
    return [m for m in models_list if search_term.lower() in m.lower()]

# Gradio interface
with gr.Blocks(theme="Nymbo/Nymbo_Theme") as demo:
    with gr.Row():
        chatbot = gr.Chatbot(height=600)

    with gr.Tab("Chat Interface"):
        with gr.Row():
            user_input = gr.Textbox(label="Your Message", placeholder="Type your message here...")
        with gr.Row():
            system_message = gr.Textbox(value="", label="System Message")
        with gr.Row():
            max_tokens = gr.Slider(minimum=1, maximum=4096, value=512, step=1, label="Max Tokens")
            temperature = gr.Slider(minimum=0.1, maximum=2.0, value=0.7, step=0.1, label="Temperature")
        with gr.Row():
            top_p = gr.Slider(minimum=0.1, maximum=1.0, value=0.9, step=0.05, label="Top-P")
            frequency_penalty = gr.Slider(minimum=-2.0, maximum=2.0, value=0.0, step=0.1, label="Frequency Penalty")
            seed = gr.Slider(minimum=-1, maximum=65535, value=-1, step=1, label="Seed (-1 for random)")
        with gr.Row():
            model = gr.Dropdown(label="Select a Model", choices=models_list, value="meta-llama/Llama-3.3-70B-Instruct")
            custom_model = gr.Textbox(label="Custom Model", placeholder="Enter custom model path")
        with gr.Row():
            run_button = gr.Button("Generate Response")

    with gr.Tab("Information"):
        with gr.Accordion("Featured Models", open=False):
            gr.HTML(
                """
                <table>
                    <tr><th>Model Name</th><th>Description</th></tr>
                    <tr><td>meta-llama/Llama-3.3-70B-Instruct</td><td>Instruction-tuned LLaMA model</td></tr>
                    <tr><td>bigscience/bloom-176b</td><td>Multilingual large language model</td></tr>
                    <tr><td>gpt-j-6b</td><td>Open-source GPT model</td></tr>
                    <tr><td>opt-30b</td><td>Meta's OPT model</td></tr>
                    <tr><td>flan-t5-xxl</td><td>Google's Flan-tuned T5 XXL</td></tr>
                </table>
                """
            )
        with gr.Accordion("Parameters Overview", open=False):
            gr.Markdown(
                """
                ### Parameters Overview
                - **Max Tokens**: Maximum number of tokens in the response.
                - **Temperature**: Controls the randomness of responses. Lower values make the output more deterministic.
                - **Top-P**: Controls the diversity of responses by limiting the token selection to a probability mass.
                - **Frequency Penalty**: Penalizes repeated tokens in the output.
                - **Seed**: Fixes randomness for reproducibility. Use -1 for a random seed.
                """
            )

    run_button.click(
        respond,
        inputs=[
            user_input,
            chatbot.state,
            system_message,
            max_tokens,
            temperature,
            top_p,
            frequency_penalty,
            seed,
            model,
            custom_model
        ],
        outputs=chatbot
    )

print("Launching the demo application.")
demo.launch()