app.py

import numpy as np
from transformers import pipeline
from transformers import AutoModelForMaskedLM, AutoTokenizer

import gradio as gr
import matplotlib.pyplot as plt

dates = []
dates.extend([f"18 {m}" for m in range(1,13)])
dates.extend([f"19 {m}" for m in range(1,13)])
dates.extend([f"20 {m}" for m in range(1,13)])
dates.extend([f"21 {m}" for m in range(1,13)])

months = [x.split(" ")[-1] for x in dates]

model_name = "fvancesco/tmp_date"
model = AutoModelForMaskedLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
model.eval()
pipe = pipeline('fill-mask', model=model, tokenizer=tokenizer, device=0)

last_mf_dict = None


def get_mf_dict(text):

    # predictions
    texts = []
    for d in dates:
        texts.append(f"{d} {text}")
    tmp_preds = pipe(texts, top_k=50265)
    preds = {}
    for i in range(len(tmp_preds)):
        preds[dates[i]] = tmp_preds[i]

    # get preds summary (only top words)
    top_n = 5 # top n for each prediction
    most_freq_tokens = set()
    for d in dates:
        tmp = [t['token_str'] for t in preds[d][:top_n]]
        most_freq_tokens.update(tmp)

    token_prob = {}
    for d in dates:
        token_prob[d] = {p['token_str']:p['score'] for p in preds[d]}

    mf_dict = {p:np.zeros(len(dates)) for p in most_freq_tokens}

    c=0
    for d in dates:
        for t in most_freq_tokens:
            mf_dict[t][c] = token_prob[d][t]
        c+=1

    return mf_dict

def plot_time(text):
    mf_dict = get_mf_dict(text)
    #last_mf_dict = mf_dict # just for debugging, remove in final version
    
    #max_tokens = 10

    fig = plt.figure(figsize=(16,9))
    ax = fig.add_subplot(111)
    #fig, ax = plt.subplots(figsize=(16,9))

    x = [i for i in range(len(dates))]

    ax.set_xlabel('Month')
    ax.set_xlim(0)
    ax.set_xticks(x)
    ax.set_xticklabels(months)
    # ax.set_yticks([-1,0,1])

    ax2 = ax.twiny()
    ax2.set_xlabel('Year')
    ax2.set_xlim(0)
    ax2.set_xticks([0,12,24,36,47])

    ax2.set_xticklabels('')
    ax2.set_xticks([6,18,30,42,47], minor=True)
    ax2.set_xticklabels(['2018','2019','2020','2021',''], minor=True)

    ax2.grid()

    # plot lines
    for k in mf_dict.keys():
        ax.plot(x, mf_dict[k], label = k)
    # k = list(mf_dict.keys())
    # for i in range(max_tokens):
    #     ax.plot(x, mf_dict[k[i]], label = k[i])

    ax.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))
    
    return fig

def add_mask(text):
    out = ""
    if len(text) == 0 or text[-1] == " ": 
        out = text+"<mask>"
    else: 
        out = text+" <mask>"
    return out

with gr.Blocks() as demo:
    #textbox = gr.Textbox(placeholder="Type here and press enter...")
    textbox = gr.Textbox(value="Happy <mask>!", max_lines=1)

    with gr.Row():
        generate_btn = gr.Button("Generate Plot")
        mask_btn = gr.Button("Add <mask>")

    plot = gr.Plot()
    
    #textbox.change(fn=plot_time, inputs=textbox, outputs=plot)
    #textbox.click(fn=plot_time, inputs=textbox, outputs=plot)
    generate_btn.click(fn=plot_time, inputs=textbox, outputs=plot)
    mask_btn.click(fn=add_mask, inputs=textbox, outputs=textbox)

demo.launch(debug=True)