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| import gradio as gr | |
| import torch | |
| import numpy as np | |
| import pandas as pd | |
| from tqdm import tqdm | |
| import altair as alt | |
| import matplotlib.pyplot as plt | |
| import datetime | |
| 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() | |
| """ | |
| def inference_sentence(text): | |
| output = "This sentence will be processed:\n" + text | |
| return output | |
| def unavailable(input_file, input_checks): | |
| output = "Submitting your own data is currently unavailable, but you can try out the showcase mode 😊" | |
| return gr.update(value=output, label="Oops!", visible=True) | |
| def file(input_file, input_checks): | |
| output = "output.txt" | |
| f = open(output, 'w') | |
| f.write("The predictions come here.") | |
| f.close() | |
| 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]}) | |
| 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: | |
| 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']}) | |
| 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", | |
| 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', 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: | |
| 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_neutral = {"13/3/2020": "up", "20/3/2020": "up", "21/3/2020": "down"} | |
| peaks_anger = {"13/3/2020": "up", "20/3/2020": "up", "21/3/2020": "down"} | |
| peaks_fear = {"13/3/2020": "up", "20/3/2020": "up", "21/3/2020": "down"} | |
| peaks_joy = {"13/3/2020": "up", "20/3/2020": "up", "21/3/2020": "down"} | |
| peaks_love = {"13/3/2020": "up", "20/3/2020": "up", "21/3/2020": "down"} | |
| peaks_sadness = {"13/3/2020": "up", "20/3/2020": "up", "21/3/2020": "down"} | |
| text_neutral = ", ".join([str(key) + " (↑)" if value == "up" else str(key) + " (↓)" for key, value in peaks_neutral.items()]) | |
| 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_neutral"></span> neutral:</p>' | |
| '<p class="tab">' + text_neutral + '<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: | |
| 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): | |
| output = "Some topics are found." | |
| return gr.update(value=output, 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 sentence. | |
| """) | |
| with gr.Row(): | |
| with gr.Column(): | |
| input = gr.Textbox( | |
| label="Enter a sentence", | |
| 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. 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. | |
| """) | |
| 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.Textbox(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.launch() | |