import gradio as gr
from huggingface_hub import InferenceClient
import torch
from transformers import pipeline
import json
import random
from prometheus_client import start_http_server, Counter, Summary
#p=9100 are standard docker metrics

# Inference client setup
client = InferenceClient(model="HuggingFaceH4/zephyr-7b-beta")
pipe = pipeline(
    "text-generation",
    "microsoft/Phi-3-mini-4k-instruct",
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

base_message = """You are a chatbot that responds with famous quotes from books, movies, philosophers, and business leaders.
Provide no advice, commentary, or additional context.
Your responses should be concise, no more than 3 quotes, and consist only of famous motivational quotes."""

# Global flag to handle cancellation
stop_inference = False

def respond(
    message,
    history: list[tuple[str, str]],
    system_message=base_message,
    max_tokens=256,
    temperature=0.7,
    # practicality=0.95,
    use_local_model=False,
):
    global stop_inference
    stop_inference = False  # Reset cancellation flag

    # if practicality <= 0.5:
    #     practicality = round(random.uniform(0,1), 1)  # Initialize random practicality score
    # if practicality > 0.5:
    #     append_message = "Provide actionable advice or direct instructions."
    # else:
    #     append_message = "Provide theoretical concepts or abstract quotes."
    # system_message_val = f"{base_message} {append_message}"

    # # Keeping the base message as it is without modifications
    # system_message_val = base_message

    # Initialize history if it's None
    if history is None:
        history = []

    if use_local_model:
        # local inference 
        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 output in pipe(
            messages,
            max_new_tokens=max_tokens,
            temperature=temperature,
            do_sample=True,
        ):
            if stop_inference:
                response = "Inference cancelled."
                yield history + [(message, response)]
                return
            token = output['generated_text'][-1]['content']
            response += token
            yield history[:-1] + [(message, response)]  # Yield history + new response

    else:
        # API-based inference 
        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_chunk in client.chat_completion(
            messages,
            stream=True,
            max_tokens=256,
            temperature=temperature,
        ):
            if stop_inference:
                response = "Inference cancelled."
                yield history + [(message, response)]
                return
            if stop_inference:
                response = "Inference cancelled."
                break
            token = message_chunk.choices[0].delta.content
            response += token
            yield history[:-1] + [(message, response)]  # Yield history + new response

def cancel_inference():
    global stop_inference
    stop_inference = True

# Custom CSS for a fancy look
custom_css = """
#main-container {
    background-color: #f0f0f0;
    font-family: 'Arial', sans-serif;
}
.gradio-container {
    max-width: 700px;
    margin: 0 auto;
    padding: 20px;
    background: white;
    box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);
    border-radius: 10px;
}
.gr-button {
    background-color: #4CAF50;
    color: white;
    border: none;
    border-radius: 5px;
    padding: 10px 20px;
    cursor: pointer;
    transition: background-color 0.3s ease;
}
.gr-button:hover {
    background-color: #45a049;
}
.gr-slider input {
    color: #4CAF50;
}
.gr-chat {
    font-size: 16px;
}
#title {
    text-align: center;
    font-size: 2em;
    margin-bottom: 20px;
    color: #333;
}
"""

# Define the interface
with gr.Blocks(css=custom_css) as demo:
    gr.Markdown("<h1 style='text-align: center;'>💡 Ask the Greats 💡</h1>")
    gr.Markdown("Want to know the secret to life? Ask away!")

    with gr.Row():
        system_message_box = gr.Textbox(
            value=base_message,
            label="System message",
            visible=False
        )
        use_local_model = gr.Checkbox(label="Use Local Model", value=False)
        temperature = gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature")
        # practicality = gr.Slider(minimum=0.1, maximum=1.0, value=0.5, step=0.05, label="Practicality")  # Commented out

    chat_history = gr.Chatbot(label="Chat")

    user_input = gr.Textbox(show_label=False, placeholder="What is the meaning of life?")

    cancel_button = gr.Button("Cancel Inference", variant="danger")

    user_input.submit(respond, [user_input, chat_history, system_message_box, temperature, use_local_model], chat_history)

    cancel_button.click(cancel_inference)

if __name__ == "__main__":
    start_http_server(8000) # expose metrics on port 8000
    demo.launch(share=True)