import torch
import gradio as gr
from transformers import pipeline
import os
from mtranslate import translate

device = torch.cuda.current_device() if torch.cuda.is_available() else "cpu"
HF_AUTH_TOKEN = os.environ.get("HF_AUTH_TOKEN")
text_generation_model = "cahya/indochat-tiny"
text_generation = pipeline("text-generation", text_generation_model, use_auth_token=HF_AUTH_TOKEN, device=device)


def get_answer(user_input, decoding_methods, num_beams, top_k, top_p, temperature, repetition_penalty, penalty_alpha):
    if decoding_methods == "Beam Search":
        do_sample = False
        penalty_alpha = 0
    elif decoding_methods == "Sampling":
        do_sample = True
        penalty_alpha = 0
        num_beams = 1
    else:
        do_sample = False
        num_beams = 1
    print(user_input, decoding_methods, do_sample, top_k, top_p, temperature, repetition_penalty, penalty_alpha)
    prompt = f"User: {user_input}\nAssistant: "
    generated_text = text_generation(f"{prompt}", min_length=50, max_length=200, num_return_sequences=1,
                                     num_beams=num_beams, do_sample=do_sample, top_k=top_k, top_p=top_p,
                                     temperature=temperature, repetition_penalty=repetition_penalty,
                                     penalty_alpha=penalty_alpha)
    answer = generated_text[0]["generated_text"]
    answer_without_prompt = answer[len(prompt)+1:]
    user_input_en = translate(user_input, "en", "id")
    answer_without_prompt_en = translate(answer_without_prompt, "en", "id")
    return [(f"{user_input} ", None), (answer_without_prompt, "")], \
        [(f"{user_input_en} ", None), (answer_without_prompt_en, "")]


with gr.Blocks() as demo:
    with gr.Row():
        gr.Markdown("""## IndoChat
        
        A Prove of Concept of a multilingual Chatbot (in this case a bilingual, English and Indonesian), fine-tuned with 
        multilingual instructions dataset. The base model is a GPT2-Medium (340M params) which was pretrained with 75GB 
        of Indonesian and English dataset, where English part is only less than 1% of the whole dataset.
        """)
    with gr.Row():
        with gr.Column():
            user_input = gr.inputs.Textbox(placeholder="",
                                           label="Ask me something in Indonesian or English",
                                           default="Bagaimana cara mendidik anak supaya tidak berbohong?")
            decoding_methods = gr.inputs.Dropdown(["Beam Search", "Sampling", "Contrastive Search"],
                                                  default="Sampling")
            num_beams = gr.inputs.Slider(label="Number of beams for beam search",
                                     default=1, minimum=1, maximum=10, step=1)
            top_k = gr.inputs.Slider(label="Top K",
                                     default=30, maximum=50, minimum=1, step=1)
            top_p = gr.inputs.Slider(label="Top P", default=0.9, step=0.05, minimum=0.1, maximum=1.0)
            temperature = gr.inputs.Slider(label="Temperature", default=0.5, step=0.05, minimum=0.1, maximum=1.0)
            repetition_penalty = gr.inputs.Slider(label="Repetition Penalty", default=1.1, step=0.05, minimum=1.0, maximum=2.0)
            penalty_alpha = gr.inputs.Slider(label="The penalty alpha for contrastive search",
                                             default=0.5, step=0.05, minimum=0.05, maximum=1.0)
            with gr.Row():
                button_generate_story = gr.Button("Submit")
        with gr.Column():
            # generated_answer = gr.Textbox()
            generated_answer = gr.HighlightedText(
                label="Generated Text",
                combine_adjacent=True,
            ).style(color_map={"": "blue", "-": "green"})
            generated_answer_en = gr.HighlightedText(
                label="Translation",
                combine_adjacent=True,
            ).style(color_map={"": "blue", "-": "green"})
    with gr.Row():
        gr.Markdown("![visitor badge](https://visitor-badge.glitch.me/badge?page_id=cahya_indochat)")

    button_generate_story.click(get_answer,
                                inputs=[user_input, decoding_methods, num_beams, top_k, top_p, temperature,
                                        repetition_penalty, penalty_alpha],
                                outputs=[generated_answer, generated_answer_en])

demo.launch(enable_queue=False)