import gradio as gr
import sys
import os
from datasets import load_dataset
from typing import List
import utils


DESCRIPTIONS = """# 🐯TIGERScore

We present ***TIGERScore***, a **T**rained metric that follows **I**nstruction **G**uidance to perform **E**xplainable, and **R**eference-free evaluation over a wide spectrum of text generation tasks. Different from other automatic evaluation methods that only provide arcane scores, TIGERScore is guided by the natural language instruction to provide error analysis to pinpoint the mistakes in the generated text.

### [**Website**](https://tiger-ai-lab.github.io/TIGERScore/)  [**Paper**](https://arxiv.org/abs/2310.00752)   [**Code**](https://github.com/TIGER-AI-Lab/TIGERScore)   [**TIGERScore-7B**](https://huggingface.co/TIGER-Lab/TIGERScore-7B-V1.0)   [**TIGERScore-13B**](https://huggingface.co/TIGER-Lab/TIGERScore-13B-V1.0)

"""

EXAMPLES_DATASET = load_dataset("TIGER-Lab/MetricInstruct", split="train_mix")
SHUFFLED_EXAMPLES_DATASET = EXAMPLES_DATASET.shuffle(seed=42)
EXAMPLES = []
fields = ["task", "instruction", "input_context", "hypo_output"]
print("Loading examples...")
for i, ex in enumerate(SHUFFLED_EXAMPLES_DATASET):
    if any([not ex[field] for field in fields]):
        continue
    EXAMPLES.append([ex[field] for field in fields])
    if i >= 100:
        break

def tigerscore(task, input_context, generation_instruction, hypo_output, max_new_tokens=512, temperature=0.7, top_p=1.0):
    return utils.generate(
        task, input_context, 
        generation_instruction, hypo_output, 
        max_new_tokens=max_new_tokens, 
        temperature=temperature, top_p=top_p
    )

def get_examples(task, inst_textbox, input_textbox, hypo_output_textbox):
    return gr.Dropdown.update(value=task), inst_textbox, input_textbox, hypo_output_textbox

## initialize the model
print("Loading TIGERScore model...")
utils.load_tigerscore("7b")

with gr.Blocks(theme='gradio/soft') as demo:
    gr.Markdown(DESCRIPTIONS)
    gr.Markdown("## TIGERScore Inputs")

    tasks_dropdown = gr.Dropdown(label="Task", choices=utils.tasks + ["other"], value="translation", show_label=True)
    inst_textbox = gr.Textbox(lines=1, label="Instruction", placeholder="Enter instruction here", show_label=True)
    input_textbox = gr.Textbox(lines=4, label="Input Context", placeholder="Enter input context here", show_label=True)
    hypo_output_textbox = gr.Textbox(lines=4, label="Hypothesis Output", placeholder="Enter hypothesis output to be evaluated here", show_label=True)
    
    with gr.Row():
        clear_button = gr.Button('Clear', variant='primary')
        submit_button = gr.Button('Submit', variant='primary')
    
    with gr.Accordion(label='Advanced options', open=False):
        max_new_tokens = gr.Slider(
            label='Max new tokens fuser can generate',
            minimum=256,
            maximum=1024,
            step=1,
            value=512,
        )
        temperature = gr.Slider(
            label='Temperature of fuser generation',
            minimum=0.1,
            maximum=2.0,
            step=0.1,
            value=0.7,
        )
        top_p = gr.Slider(
            label='Top-p of fuser generation',
            minimum=0.05,
            maximum=1.0,
            step=0.05,
            value=1.0,
        )

    gr.Markdown("## TIGERScore Outputs")
    evaluation_output_textbox = gr.Textbox(lines=4, label="Evaluation Output", placeholder="Evaluation output", show_label=True)


    submit_button.click(
        fn=tigerscore,
        inputs=[tasks_dropdown, input_textbox, inst_textbox, hypo_output_textbox, max_new_tokens, temperature, top_p],
        outputs=evaluation_output_textbox,
    )
    
    batch_examples = gr.Examples(
        examples=EXAMPLES,
        fn=get_examples,
        cache_examples=True,
        examples_per_page=5,
        inputs=[tasks_dropdown, inst_textbox, input_textbox, hypo_output_textbox],
        outputs=[tasks_dropdown, inst_textbox, input_textbox, hypo_output_textbox],
    )  

    citations = gr.Markdown("""## Citation
@article{jiang2023TIGERScore,
  title={TIGERScore: Towards Building Explainable Metric for All Text Generation Tasks},
  author={Dongfu Jiang, Yishan Li, Ge Zhang, Wenhao Huang, Bill Yuchen Lin, Wenhu Chen},
  journal={arXiv preprint arXiv:2310.00752},
  year={2023}
}""")

demo.queue(max_size=20).launch()