import gradio as gr
import numpy as np
from datasets import load_dataset
import matplotlib.pyplot as plt
import seaborn as sns

dataset = load_dataset('dar-tau/grammar-attention-maps-opt-350m')['train']

def analyze_sentence(index):
    row = dataset[index]
    text = row['text']
    _, seq_len, _ = row['attention_maps_shape']
    
    attn_maps = np.array(row['attention_maps']).reshape(*attn_map_shape)
    plot = sns.heatmap(attn_maps.sum(1).sum(0))
    plt.xticks(np.arange(seq_len - 1) + 0.5,
           tokenizer.tokenize(text, add_special_tokens=False), rotation=90);
    plt.yticks(np.arange(seq_len - 1) + 0.5,
               tokenizer.tokenize(text, add_special_tokens=False), rotation=0);
    plt.ylabel('TARGET')
    plt.xlabel('SOURCE')    
    plt.grid()
    return row['text'], plot


iface = gr.Interface(fn=analyze_sentence, inputs=[gr.Dropdown(choices=dataset['text'], type='index')], 
                     outputs=[gr.Label(), gr.Plot(label="Plot")])
iface.launch()