Create app.py

app.py

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+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):
+	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.HTML(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()