import gradio as gr

import os
from dataclasses import dataclass, asdict
from ctransformers import AutoModelForCausalLM, AutoConfig


@dataclass
class GenerationConfig:
    temperature: float
    top_k: int
    top_p: float
    repetition_penalty: float
    max_new_tokens: int
    seed: int
    reset: bool
    stream: bool
    threads: int
    stop: list[str]


def format_prompt(user_prompt: str):
    return f"""### Instruction:
{user_prompt}

### Response:"""


def generate(
    llm: AutoModelForCausalLM,
    generation_config: GenerationConfig,
    user_prompt: str,
):
    """run model inference, will return a Generator if streaming is true"""

    return llm(format_prompt(user_prompt), **asdict(generation_config))

config = AutoConfig.from_pretrained(
    "teknium/Replit-v2-CodeInstruct-3B", context_length=2048
)
llm = AutoModelForCausalLM.from_pretrained(
    os.path.abspath("replit-code-instruct-glaive.ggmlv1.q4_1.bin"),
    model_type="replit",
    config=config,
)

generation_config = GenerationConfig(
    temperature=0.2,
    top_k=50,
    top_p=0.9,
    repetition_penalty=1.0,
    max_new_tokens=512,  # adjust as needed
    seed=42,
    reset=True,  # reset history (cache)
    stream=True,  # streaming per word/token
    threads=int(os.cpu_count() / 6),  # adjust for your CPU
    stop=["<|endoftext|>"],
)

print(os.cpu_count())

user_prefix = "[user]: "
assistant_prefix = f"[assistant]:"

title = "Replit-v2-CodeInstruct-3b-ggml"
description = "This space is an attempt to run the GGML 4 bit quantized version of 'Replit's CodeInstruct 3B' on a CPU"

example_1 = "Write a python script for a function which calculates the factorial of the number inputted by user."
example_2 = "Write a python script which prints 'you are logged in' only if the user inputs a number between 1-10"

examples = [example_1, example_2]

def generate_code(user_input):
    response = generate(llm, generation_config, user_input)
    code = ""
    for word in response:
        code = code + word
    return code

UI = gr.Interface(
    fn=generate_code,
    inputs=gr.Textbox(label="user_prompt", placeholder="Ask your queries here...."),
    outputs=gr.Textbox(label="Assistant"),
    title=title,
    description=description,
    examples=examples
)

UI.launch()