from huggingface_hub import Repository
import gradio as gr
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
import os

# Load the model from your Hugging Face account
model_name = "adi1193/mistral-postv6"
repository = Repository(model_name, clone_from="adi1193/mistral-postv6")

# Load the model
model_path = repository.local_dir
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)
tokenizer = AutoTokenizer.from_pretrained(model_path)

def format_prompt(message, history, enable_hinglish=False):
    prompt = "<s>"
    # Adding the Hinglish prompt
    if enable_hinglish and not any("[INST] You are a Hinglish LLM." in user_prompt for user_prompt, bot_response in history):
        prompt += Hinglish_Prompt

    for user_prompt, bot_response in history:
        prompt += f"[INST] {user_prompt} [/INST]"
        prompt += f" {bot_response}  "
    prompt += f"[INST] {message} [/INST]"
    return prompt

def generate(prompt, history, temperature=0.9, max_new_tokens=256, top_p=0.95, repetition_penalty=1.0, enable_hinglish=False):
    temperature = float(temperature)
    if temperature < 1e-2:
        temperature = 1e-2
    top_p = float(top_p)

    generate_kwargs = {
        "model": model,
        "tokenizer": tokenizer,
        "max_length": max_new_tokens + len(tokenizer.encode(prompt)),
        "temperature": temperature,
        "top_p": top_p,
        "repetition_penalty": repetition_penalty,
        "do_sample": True,
        "seed": 42,
    }

    formatted_prompt = format_prompt(prompt, history, enable_hinglish)
    input_ids = tokenizer.encode(formatted_prompt, return_tensors="pt")
    output = model.generate(input_ids, **generate_kwargs)
    return tokenizer.decode(output[0], skip_special_tokens=True)

additional_inputs=[
    gr.Slider(
        label="Temperature",
        value=0.9,
        minimum=0.0,
        maximum=1.0,
        step=0.05,
        interactive=True,
        info="Higher values produce more diverse outputs",
    ),
    gr.Slider(
        label="Max new tokens",
        value=256,
        minimum=0,
        maximum=1048,
        step=64,
        interactive=True,
        info="The maximum numbers of new tokens",
    ),
    gr.Slider(
        label="Top-p (nucleus sampling)",
        value=0.90,
        minimum=0.0,
        maximum=1,
        step=0.05,
        interactive=True,
        info="Higher values sample more low-probability tokens",
    ),
    gr.Slider(
        label="Repetition penalty",
        value=1.2,
        minimum=1.0,
        maximum=2.0,
        step=0.05,
        interactive=True,
        info="Penalize repeated tokens",
    ),
    gr.Checkbox(
        label="Hinglish",
        value=False,
        interactive=True,
        info="Enables the MistralTalk to talk in Hinglish (Combination of Hindi and English)",
    )
]

css = """
  #mkd {
    height: 500px;
    overflow: auto;
    border: 1px solid #ccc;
  }
"""

with gr.Blocks(css=css) as demo:
    gr.HTML("<h1><center>MistralTalk🗣️<h1><center>")
    gr.HTML("<h3><center>In this demo, you can chat with <a href='https://huggingface.co/adi1193/mistral-postv6'>Mistral-8x7B</a> model. 💬<h3><center>")
    gr.HTML("<h3><center>Learn more about the model <a href='https://huggingface.co/docs/transformers/main/model_doc/mistral'>here</a>. 📚<h3><center>")
    gr.ChatInterface(
        generate,
        additional_inputs=additional_inputs,
        theme = gr.themes.Soft(),
        examples=[["What is the interest?"], ["How does the universe work?"],["What can you do?"],["What is quantum mechanics?"],["Do you believe in an after life?"]]
    )

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