app.py

import gradio as gr
import torch
import numpy as np
import pickle

import pandas as pd
from tqdm import tqdm

import altair as alt
import matplotlib.pyplot as plt
from datetime import date, timedelta

from transformers import AutoTokenizer, AutoConfig, AutoModel, AutoModelForSequenceClassification

"""
description_sentence = "<h3>Demo EmotioNL</h3>\nThis demo allows you to analyse the emotion in a sentence."
description_dataset = "<h3>Demo EmotioNL</h3>\nThis demo allows you to analyse the emotions in a dataset.\nThe data should be in tsv-format with two named columns: the first column (id) should contain the sentence IDs, and the second column (text) should contain the actual texts. Optionally, there is a third column named 'date', which specifies the date associated with the text (e.g., tweet date). This column is necessary when the options 'emotion distribution over time' and 'peaks' are selected."

inference_modelpath = "model/checkpoint-128"

def inference_sentence(text):
    tokenizer = AutoTokenizer.from_pretrained(inference_modelpath)
    model = AutoModelForSequenceClassification.from_pretrained(inference_modelpath)
    for text in tqdm([text]):
        inputs = tokenizer(text, return_tensors="pt")
    with torch.no_grad(): # run model
        logits = model(**inputs).logits
        predicted_class_id = logits.argmax().item()
    output = model.config.id2label[predicted_class_id]
    return output

def frequencies(preds):
	preds_dict = {"neutral": 0, "anger": 0, "fear": 0, "joy": 0, "love": 0, "sadness": 0}
	for pred in preds:
		preds_dict[pred] = preds_dict[pred] + 1
	bars = list(preds_dict.keys())
	height = list(preds_dict.values())

	x_pos = np.arange(len(bars))
	plt.bar(x_pos, height, color=['lightgrey', 'firebrick', 'rebeccapurple', 'orange', 'palevioletred', 'cornflowerblue'])
	plt.xticks(x_pos, bars)
	return plt
    
def inference_dataset(file_object, option_list):
    tokenizer = AutoTokenizer.from_pretrained(inference_modelpath)
    model = AutoModelForSequenceClassification.from_pretrained(inference_modelpath)
    data_path = open(file_object.name, 'r')
    df = pd.read_csv(data_path, delimiter='\t', header=0, names=['id', 'text'])
    ids = df["id"].tolist()
    texts = df["text"].tolist()
    preds = []
    for text in tqdm(texts): # progressbar
        inputs = tokenizer(text, return_tensors="pt")
        with torch.no_grad(): # run model
            logits = model(**inputs).logits
        predicted_class_id = logits.argmax().item()
        prediction = model.config.id2label[predicted_class_id]
        preds.append(prediction)
    predictions_content = list(zip(ids, texts, preds))
    # write predictions to file
    output = "output.txt"
    f = open(output, 'w')
    f.write("id\ttext\tprediction\n")
    for line in predictions_content:
        f.write(str(line[0]) + '\t' + str(line[1]) + '\t' + str(line[2]) + '\n')
    output1 = output
    output2 = output3 = output4 = output5 = "This option was not selected."
    if "emotion frequencies" in option_list:
        output2 = frequencies(preds)
    else:
        output2 = None
    if "emotion distribution over time" in option_list:
        output3 = "This option was selected."
    if "peaks" in option_list:
        output4 = "This option was selected."
    if "topics" in option_list:
        output5 = "This option was selected."
    return [output1, output2, output3, output4, output5]

iface_sentence = gr.Interface(
            fn=inference_sentence,
            description = description_sentence,
            inputs = gr.Textbox(
                    label="Enter a sentence",
                    lines=1),
            outputs="text")

inputs = [gr.File(
            label="Upload a dataset"),
          gr.CheckboxGroup(
            ["emotion frequencies", "emotion distribution over time", "peaks", "topics"],
            label = "Select options")]

outputs = [gr.File(),
           gr.Plot(label="Emotion frequencies"),
           gr.Textbox(label="Emotion distribution over time"),
           gr.Textbox(label="Peaks"),
           gr.Textbox(label="Topics")]

iface_dataset = gr.Interface(
            fn = inference_dataset,
            description = description_dataset,
            inputs=inputs,
            outputs = outputs)

iface = gr.TabbedInterface([iface_sentence, iface_dataset], ["Sentence", "Dataset"])

iface.queue().launch()
"""

inference_modelpath = "model/checkpoint-128"

def inference_sentence(text):
    tokenizer = AutoTokenizer.from_pretrained(inference_modelpath)
    model = AutoModelForSequenceClassification.from_pretrained(inference_modelpath)
    for text in tqdm([text]):
        inputs = tokenizer(text, return_tensors="pt")
    with torch.no_grad(): # run model
        logits = model(**inputs).logits
        predicted_class_id = logits.argmax().item()
    output = model.config.id2label[predicted_class_id]
    return "Predicted emotion:\n" + output
"""
def inference_sentence(text):
    output = "This sentence will be processed:\n" + text
    return output
"""

def unavailable(input_file, input_checks):
    output = "As we are currently updating this demo, submitting your own data is unavailable for the moment. However, you can try out the showcase mode 😊"
    return gr.update(value=output, label="Oops!", visible=True)

def showcase(input_file):
    output = "showcase/example_predictions.txt"
    return gr.update(visible=False), gr.update(value=output, visible=True), gr.update(visible=True), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)  # next_button_freq becomes available

def file(input_file, input_checks):
    #output = "output.txt"
    #f = open(output, 'w')
    #f.write("The predictions come here.")
    #f.close()
    output = "showcase/example_predictions.txt"
    if "emotion frequencies" in input_checks:
        return gr.update(value=output, visible=True), gr.update(visible=True), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)  # next_button_freq becomes available
    elif "emotion distribution over time" in input_checks:
        return gr.update(value=output, visible=True), gr.update(visible=False), gr.update(visible=True), gr.update(visible=False), gr.update(visible=False)  # next_button_dist becomes available
    elif "peaks" in input_checks:
        return gr.update(value=output, visible=True), gr.update(visible=False), gr.update(visible=False), gr.update(visible=True), gr.update(visible=False)  # next_button_peaks becomes available
    elif "topics" in input_checks:
        return gr.update(value=output, visible=True), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=True)  # next_button_topics becomes available
    else:
        return gr.update(value=output, visible=True), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)  # no next_button becomes available

def freq(output_file, input_checks):
    #simple = pd.DataFrame({
    #'Emotion category': ['neutral', 'anger', 'fear', 'joy', 'love', 'sadness'],
    #'Frequency': [10, 8, 2, 15, 3, 4]})
    
    f = open("showcase/example_predictions.txt", 'r')
    data = f.read().split("\n")
    f.close()
    data = [line.split("\t") for line in data[1:-1]]
    
    freq_dict = {}
    for line in data:
    	if line[1] not in freq_dict.keys():
    		freq_dict[line[1]] = 1
    	else:
    		freq_dict[line[1]] += 1
    
    simple = pd.DataFrame({
    	'Emotion category': ['neutral', 'anger', 'fear', 'joy', 'love', 'sadness'],
    	'Frequency': [freq_dict['neutral'], freq_dict['anger'], freq_dict['fear'], freq_dict['joy'], freq_dict['love'], freq_dict['sadness']]})

    domain = ['neutral', 'anger', 'fear', 'joy', 'love', 'sadness']
    range_ = ['#999999', '#b22222', '#663399', '#ffcc00', '#db7093', '#6495ed']
    n = max(simple['Frequency'])

    plot = alt.Chart(simple).mark_bar().encode(
    x=alt.X("Emotion category", sort=['neutral', 'anger', 'fear', 'joy', 'love', 'sadness']),
    y=alt.Y("Frequency", axis=alt.Axis(grid=False), scale=alt.Scale(domain=[0, (n + 9) // 10 * 10])),
    color=alt.Color("Emotion category", scale=alt.Scale(domain=domain, range=range_), legend=None),
    tooltip=['Emotion category', 'Frequency']).properties(
    width=600).configure_axis(
    grid=False).interactive()

    if "emotion distribution over time" in input_checks or (output_file.name).startswith('/tmp/example_predictions'):
        return gr.update(value=plot, visible=True), gr.update(visible=True), gr.update(visible=False), gr.update(visible=False)  # next_button_dist becomes available
    elif "peaks" in input_checks:
        return gr.update(value=plot, visible=True), gr.update(visible=False), gr.update(visible=True), gr.update(visible=False)  # next_button_peaks becomes available
    elif "topics" in input_checks:
        return gr.update(value=plot, visible=True), gr.update(visible=False), gr.update(visible=False), gr.update(visible=True)  # next_button_topics becomes available
    else:
        return gr.update(value=plot, visible=True), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)  # no next_button becomes available


def dist(output_file, input_checks):
    #data = pd.DataFrame({
    #'Date': ['1/1', '1/1', '1/1', '1/1', '1/1', '1/1', '2/1', '2/1', '2/1', '2/1', '2/1', '2/1', '3/1', '3/1', '3/1', '3/1', '3/1', '3/1'],
    #'Frequency': [3, 5, 1, 8, 2, 3, 4, 7, 1, 12, 4, 2, 3, 6, 3, 10, 3, 4],
    #'Emotion category': ['neutral', 'anger', 'fear', 'joy', 'love', 'sadness', 'neutral', 'anger', 'fear', 'joy', 'love', 'sadness', 'neutral', 'anger', 'fear', 'joy', 'love', 'sadness']})

    f = open("showcase/data.txt", 'r')
    data = f.read().split("\n")
    f.close()
    data = [line.split("\t") for line in data[1:-1]]
    
    freq_dict = {}
    for line in data:
    	dat = str(date(2000+int(line[0].split("/")[2]), int(line[0].split("/")[1]), int(line[0].split("/")[0])))
    	if dat not in freq_dict.keys():
    		freq_dict[dat] = {}
    		if line[1] not in freq_dict[dat].keys():
    			freq_dict[dat][line[1]] = 1
    		else:
    			freq_dict[dat][line[1]] += 1
    	else:
    		if line[1] not in freq_dict[dat].keys():
    			freq_dict[dat][line[1]] = 1
    		else:
    			freq_dict[dat][line[1]] += 1
    
    start_date = date(2000+int(data[0][0].split("/")[2]), int(data[0][0].split("/")[1]), int(data[0][0].split("/")[0]))
    end_date = date(2000+int(data[-1][0].split("/")[2]), int(data[-1][0].split("/")[1]), int(data[-1][0].split("/")[0]))
    delta = end_date - start_date   # returns timedelta
    date_range = [str(start_date + timedelta(days=i)) for i in range(delta.days + 1)]
    
    dates = [dat for dat in date_range for i in range(6)]
    frequency = [freq_dict[dat][emotion] if (dat in freq_dict.keys() and emotion in freq_dict[dat].keys()) else 0 for dat in date_range for emotion in ['neutral', 'anger', 'fear', 'joy', 'love', 'sadness']]
    categories = [emotion for dat in date_range for emotion in ['neutral', 'anger', 'fear', 'joy', 'love', 'sadness']]
    
    data = pd.DataFrame({
    	'Date': dates,
    	'Frequency': frequency,
    	'Emotion category': categories})

    domain = ['neutral', 'anger', 'fear', 'joy', 'love', 'sadness']
    range_ = ['#999999', '#b22222', '#663399', '#ffcc00', '#db7093', '#6495ed']
    n = max(data['Frequency'])
    
    highlight = alt.selection(
    type='single', on='mouseover', fields=["Emotion category"], nearest=True)

    
    base = alt.Chart(data).encode(
    x ="Date:T",
    y=alt.Y("Frequency", scale=alt.Scale(domain=[0, (n + 9) // 10 * 10])),
    color=alt.Color("Emotion category", scale=alt.Scale(domain=domain, range=range_), legend=alt.Legend(orient='bottom', direction='horizontal')))
    
    
    points = base.mark_circle().encode(
        opacity=alt.value(0),
        tooltip=[
            alt.Tooltip('Emotion category', title='Emotion category'),
            alt.Tooltip('Date:T', title='Date'),
            alt.Tooltip('Frequency', title='Frequency')
        ]).add_selection(highlight)

    
    lines = base.mark_line().encode(
        size=alt.condition(~highlight, alt.value(1), alt.value(3)))
    
    plot = (points + lines).properties(width=600, height=350).interactive()
    
    if "peaks" in input_checks or (output_file.name).startswith('/tmp/example_predictions'):
        return gr.Plot.update(value=plot, visible=True), gr.update(visible=True), gr.update(visible=False)  # next_button_peaks becomes available
    elif "topics" in input_checks:
        return gr.Plot.update(value=plot, visible=True), gr.update(visible=False), gr.update(visible=True)  # next_button_topics becomes available
    else:
        return gr.Plot.update(value=plot, visible=True), gr.update(visible=False), gr.update(visible=False)  # no next_button becomes available

def peaks(output_file, input_checks):
    plot = pickle.load(open('showcase/peaks_covid.p', 'rb'))
	if "topics" in input_checks or (output_file.name).startswith('/tmp/example_predictions'):
		return gr.Plot.update(value=plot, visible=True), gr.update(visible=True)  # next_button_topics becomes available
	else:
		return gr.Plot.update(value=plot, visible=True), gr.update(visible=False)  # no next_button becomes available
    

def peaks2(output_file, input_checks):
	peaks_anger = {"9/2/2020": "up", "18/2/2020": "down", "8/3/2020": "up", "20/3/2020": "up", "31/5/2020": "up", "6/6/2020": "up", "19/6/2020": "up", "19/7/2020": "up"}
	peaks_fear = {"8/2/2020": "up", "11/2/2020": "down", "31/5/2020": "down", "12/6/2020": "up", "5/7/2020": "up", "19/7/2020": "up"}
	peaks_joy = {"13/3/2020": "up", "4/4/2020": "up", "19/6/2020": "up", "26/6/2020": "up"}
	peaks_love = {"12/3/2020": "up", "5/5/2020": "down", "26/6/2020": "up", "7/8/2020": "up",}
	peaks_sadness = {"14/2/2020": "up", "3/4/2020": "up", "5/5/2020": "down", "18/5/2020": "down", "30/6/2020": "up", "5/7/2020": "up"}

	text_anger = ", ".join([str(key) + " (↑)" if value == "up" else str(key) + " (↓)" for key, value in peaks_anger.items()])
	text_fear = ", ".join([str(key) + " (↑)" if value == "up" else str(key) + " (↓)" for key, value in peaks_fear.items()])
	text_joy = ", ".join([str(key) + " (↑)" if value == "up" else str(key) + " (↓)" for key, value in peaks_joy.items()])
	text_love = ", ".join([str(key) + " (↑)" if value == "up" else str(key) + " (↓)" for key, value in peaks_love.items()])
	text_sadness = ", ".join([str(key) + " (↑)" if value == "up" else str(key) + " (↓)" for key, value in peaks_sadness.items()])
 
	html = (
			'<html>'
			'<head>'
			'<meta name="viewport" content="width=device-width, initial-scale=1">'
			'<style>'
			'.dot_neutral {'
			  'height: 11px;'
			  'width: 11px;'
			  'background-color: #999999;'
			  'border-radius: 50%;'
			  'display: inline-block;'
			'}'
			'.dot_anger {'
			  'height: 11px;'
			  'width: 11px;'
			  'background-color: #b22222;'
			  'border-radius: 50%;'
			  'display: inline-block;'
			'}'
			'.dot_fear {'
			  'height: 11px;'
			  'width: 11px;'
			  'background-color: #663399;'
			  'border-radius: 50%;'
			  'display: inline-block;'
			'}'
			'.dot_joy {'
			  'height: 11px;'
			  'width: 11px;'
			  'background-color: #ffcc00;'
			  'border-radius: 50%;'
			  'display: inline-block;'
			'}'
			'.dot_love {'
			  'height: 11px;'
			  'width: 11px;'
			  'background-color: #db7093;'
			  'border-radius: 50%;'
			  'display: inline-block;'
			'}'
			'.dot_sadness {'
			  'height: 11px;'
			  'width: 11px;'
			  'background-color: #6495ed;'
			  'border-radius: 50%;'
			  'display: inline-block;'
			'}'
			'.tab {'
			  'padding-left: 1em;'
			'}'
			'</style>'
			'</head>'
			'<body>'
			'<div>'
			  '<p>These significant fluctuations were found:</p>'
			  '<p><span class="dot_anger"></span> anger:</p>'
			  '<p class="tab">' + text_anger + '<p>'
			  '<p><span class="dot_fear"></span> fear:</p>'
			  '<p class="tab">' + text_fear + '<p>'
			  '<p><span class="dot_joy"></span> joy:</p>'
			  '<p class="tab">' + text_joy + '<p>'
			  '<p><span class="dot_love"></span> love:</p>'
			  '<p class="tab">' + text_love + '<p>'
			  '<p><span class="dot_sadness"></span> sadness:</p>'
			  '<p class="tab">' + text_sadness + '<p>'
			'</div>'
			'</body>'
			'</html>'
			)
	if "topics" in input_checks or (output_file.name).startswith('/tmp/example_predictions'):
		return gr.update(value=html, visible=True), gr.update(visible=True)  # next_button_topics becomes available
	else:
		return gr.update(value=html, visible=True), gr.update(visible=False)  # no next_button becomes available

def topics(output_file, input_checks):
    plot = pickle.load(open('showcase/vis_classes_covid.p', 'rb'))
    plot.update_layout(width=600, height=400)
    return gr.Plot.update(value=plot, visible=True)  # no next_button becomes available

with gr.Blocks() as demo:
    with gr.Tab("Sentence"):
        gr.Markdown("""
        # Demo EmotioNL
        This demo allows you to analyse the emotion in a Dutch sentence.
        """)
        with gr.Row():
            with gr.Column():
                input = gr.Textbox(
                        label="Enter a sentence",
                        value="Jaaah! Volgende vakantie Barcelona en na het zomerseizoen naar de Algarve",
                        lines=1)
                send_btn = gr.Button("Send")
            output = gr.Textbox()
        send_btn.click(fn=inference_sentence, inputs=input, outputs=output)
    with gr.Tab("Dataset"):
        gr.Markdown("""
        # Demo EmotioNL
        This demo allows you to analyse the emotions in a dataset with Dutch sentences.

        _! As we are currently updating this demo, submitting your own data is unavailable for the moment ! However, you can try out the showcase mode 😊_
        
        The data should be in tsv-format with two named columns: the first column (id) should contain the sentence IDs, and the second column (text) should contain the actual texts. Optionally, there is a third column named 'date', which specifies the date associated with the text (e.g., tweet date). This column is necessary when the options 'emotion distribution over time' and 'peaks' are selected.
        
        You can also try out the demo in  showcase mode, which uses example data, namely a dataset with tweets about the COVID-19 pandemic.
        """)
        with gr.Row():
            with gr.Column():
                input_file = gr.File(
                    label="Upload a dataset")
                input_checks = gr.CheckboxGroup(
                    ["emotion frequencies", "emotion distribution over time", "peaks", "topics"],
                    label = "Select options")
                send_btn = gr.Button("Submit data")
                demo_btn = gr.Button("... or showcase with example data")
            with gr.Column():
                message = gr.Textbox(label="Message", visible=False)

                output_file = gr.File(label="Predictions", visible=False)
                next_button_freq = gr.Button("Show emotion frequencies", visible=False)
                
                output_plot = gr.Plot(show_label=False, visible=False).style(container=True)
                next_button_dist = gr.Button("Show emotion distribution over time", visible=False)
                
                output_dist = gr.Plot(show_label=False, visible=False)
                next_button_peaks = gr.Button("Show peaks", visible=False)
                
                output_peaks = gr.Plot(visible=False)
                next_button_topics = gr.Button("Show topics", visible=False)
                
                output_topics = gr.Plot(show_label=False, visible=False)
        
        #send_btn.click(fn=file, inputs=[input_file,input_checks], outputs=[output_file,next_button_freq,next_button_dist,next_button_peaks,next_button_topics])
        next_button_freq.click(fn=freq, inputs=[output_file,input_checks], outputs=[output_plot,next_button_dist,next_button_peaks,next_button_topics])
        next_button_dist.click(fn=dist, inputs=[output_file,input_checks], outputs=[output_dist,next_button_peaks,next_button_topics])
        next_button_peaks.click(fn=peaks, inputs=[output_file,input_checks], outputs=[output_peaks,next_button_topics])
        next_button_topics.click(fn=topics, inputs=[output_file,input_checks], outputs=output_topics)
        send_btn.click(fn=unavailable, inputs=[input_file,input_checks], outputs=message)
        demo_btn.click(fn=showcase, inputs=[input_file], outputs=[message,output_file,next_button_freq,next_button_dist,next_button_peaks,next_button_topics])

        
demo.launch()