from dotenv import load_dotenv
import gradio as gr
import random

from utils.model import Model
from utils.data import dataset
from utils.metric import metric_rouge_score

from summarization_playground import model, generate_answer

load_dotenv()

def process(seed, model_selection, prompt, num=10):
    random.seed(seed)
    response_list = []

    for data in random.choices(dataset, k=num):
        dialogue = data['dialogue']
        summary = data['summary']
        response = generate_answer(dialogue, model, model_selection, prompt)

        rouge_score = metric_rouge_score(response, summary)

        response_list.append(
            {
                'dialogue': dialogue,
                'summary': summary,
                'response': response,
                'metric_score': {
                    'rouge_score': rouge_score
                }
            }
        )

    return response_list

def create_batch_evaluation_interface():
    with gr.blocks() as demo:
        gr.Markdown("## Here are evaluation setups")
        with gr.Row():
            seed = gr.Number(value=8, placeholder="pick your favoriate random seed")
            model_dropdown = gr.Dropdown(choices=Model.__model_list__, label="Choose a model", value=Model.__model_list__[0])
        Template_text = gr.Textbox(value="""Summariza the following dialogue""", label='Input Prompting Template', lines=8, placeholder='Input your prompts')
        submit_button = gr.Button("✨ Submit ✨")
        output = gr.Markdown()

        submit_button.click(
            process,
            inputs=[seed, model_dropdown, Template_text],
            outputs=output
        )

    return demo

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