#!/usr/bin/env python

import os
from collections.abc import Iterator
from threading import Thread

import gradio as gr
import spaces
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer

#
# 1) Custom Pastel Gradient CSS
#
CUSTOM_CSS = """
.gradio-container {
    background: linear-gradient(to right, #FFDEE9, #B5FFFC);
}
"""

#
# 2) Description: Add French greeting, plus any info
#
DESCRIPTION = """# Bonjour Dans le chat du consentement
Mistral-7B Instruct Demo
"""

if not torch.cuda.is_available():
    DESCRIPTION += (
        "\n<p style='color:red;'>Running on CPU - This is likely too large to run effectively.</p>"
    )

MAX_MAX_NEW_TOKENS = 2048
DEFAULT_MAX_NEW_TOKENS = 1024
MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "4096"))

#
# 3) Load Mistral-7B Instruct (requires gating, GPU recommended)
#
if torch.cuda.is_available():
    model_id = "mistralai/Mistral-7B-Instruct-v0.3"
    tokenizer = AutoTokenizer.from_pretrained(
        model_id, 
        trust_remote_code=True  # Might be needed for custom code
    )
    model = AutoModelForCausalLM.from_pretrained(
        model_id,
        torch_dtype=torch.float16,
        device_map="auto",
        trust_remote_code=True
    )

def generate(
    message: str,
    chat_history: list[dict],
    max_new_tokens: int = 1024,
    temperature: float = 0.6,
    top_p: float = 0.9,
    top_k: int = 50,
    repetition_penalty: float = 1.2,
) -> Iterator[str]:
    """
    This function handles streaming chat text as the model generates it.
    Uses Mistral's 'apply_chat_template' to handle chat-style prompting.
    """
    conversation = [*chat_history, {"role": "user", "content": message}]

    input_ids = tokenizer.apply_chat_template(conversation, return_tensors="pt")
    if input_ids.shape[1] > MAX_INPUT_TOKEN_LENGTH:
        input_ids = input_ids[:, -MAX_INPUT_TOKEN_LENGTH:]
        gr.Warning(
            f"Trimmed input from conversation as it was longer than {MAX_INPUT_TOKEN_LENGTH} tokens."
        )
    input_ids = input_ids.to(model.device)

    streamer = TextIteratorStreamer(
        tokenizer, 
        timeout=20.0, 
        skip_prompt=True, 
        skip_special_tokens=True
    )
    generate_kwargs = dict(
        {"input_ids": input_ids},
        streamer=streamer,
        max_new_tokens=max_new_tokens,
        do_sample=True,
        top_p=top_p,
        top_k=top_k,
        temperature=temperature,
        num_beams=1,
        repetition_penalty=repetition_penalty,
    )
    t = Thread(target=model.generate, kwargs=generate_kwargs)
    t.start()

    outputs = []
    for text in streamer:
        outputs.append(text)
        # Stream partial output as it's generated
        yield "".join(outputs)

#
# 4) Build the Chat Interface with extra sliders
#
demo = gr.ChatInterface(
    fn=generate,
    description=DESCRIPTION,
    css=CUSTOM_CSS,  # Use our pastel gradient
    additional_inputs=[
        gr.Slider(
            label="Max new tokens",
            minimum=1,
            maximum=MAX_MAX_NEW_TOKENS,
            step=1,
            value=DEFAULT_MAX_NEW_TOKENS,
        ),
        gr.Slider(
            label="Temperature",
            minimum=0.1,
            maximum=4.0,
            step=0.1,
            value=0.6,
        ),
        gr.Slider(
            label="Top-p (nucleus sampling)",
            minimum=0.05,
            maximum=1.0,
            step=0.05,
            value=0.9,
        ),
        gr.Slider(
            label="Top-k",
            minimum=1,
            maximum=1000,
            step=1,
            value=50,
        ),
        gr.Slider(
            label="Repetition penalty",
            minimum=1.0,
            maximum=2.0,
            step=0.05,
            value=1.2,
        ),
    ],
    stop_btn=None,
    examples=[
        ["Hello there! How are you doing?"],
        ["Can you explain briefly what the Python programming language is?"],
        ["Explain the plot of Cinderella in a sentence."],
        ["How many hours does it take a man to eat a Helicopter?"],
        ["Write a 100-word article on 'Benefits of Open-Source in AI research'"],
    ],
    type="messages",
)

if __name__ == "__main__":
    demo.queue(max_size=20).launch(share=True)