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| import gradio as gr | |
| import torch | |
| from transformers import AutoTokenizer, AutoConfig, AutoModel, AutoModelForSequenceClassification | |
| description_sentence = "<h3>Demo EmotioNL</h3>\nThis demo allows you to analyse the emotion in a sentence." | |
| description2 = "<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 what_happened(text, file_object, option_list): | |
| if file_object: | |
| output = "You uploaded a file." | |
| if len(option_list) > 0: | |
| output = output + "\nYou selected these options:\n- " + "\n- ".join(option_list) | |
| else: | |
| output = "Normally, this demo should analyse the emotions in this text:\n" + text | |
| if len(option_list) > 0: | |
| output = output + "\nYou can only select options when uploading a dataset." | |
| return output | |
| def what_happened1(text): | |
| output = "Normally, this demo should analyse the emotions in this text:\n" + text | |
| return output | |
| def what_happened2(file_object, option_list): | |
| if file_object: | |
| output1 = "You uploaded a file." | |
| if len(option_list) > 0: | |
| output1 = output1 + "\nYou selected these options:\n- " + "\n- ".join(option_list) | |
| else: | |
| output1 = "You should upload a file." | |
| output2 = output3 = output4 = output5 = "This option was not selected." | |
| if "emotion frequencies" in option_list: | |
| output2 = "This option was selected." | |
| 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] | |
| def inference_sentence(text): | |
| tokenizer = AutoTokenizer.from_pretrained(inference_modelpath) | |
| model = AutoModelForSequenceClassification.from_pretrained(inference_modelpath) | |
| 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 | |
| iface0 = gr.Interface( | |
| fn=what_happened, | |
| inputs=[ | |
| gr.Textbox( | |
| label= "Enter a sentence", | |
| lines=1, | |
| value="Your name"), | |
| gr.File( | |
| label="Or upload a dataset"), | |
| gr.CheckboxGroup( | |
| ["emotion frequencies", "emotion distribution over time", "peaks", "topics"], | |
| label = "Select options") | |
| ], | |
| outputs="text") | |
| iface_sentence = gr.Interface( | |
| fn=inference_sentence, | |
| description = description_sentence, | |
| inputs = gr.Textbox( | |
| label="Enter a sentence", | |
| lines=1), | |
| outputs="text") | |
| iface2 = gr.Interface( | |
| fn=what_happened2, | |
| description = description2, | |
| inputs=[ | |
| gr.File( | |
| label="Upload a dataset"), | |
| gr.CheckboxGroup( | |
| ["emotion frequencies", "emotion distribution over time", "peaks", "topics"], | |
| label = "Select options") | |
| ], | |
| #outputs=["text", "text", "text", "text", "text"]) | |
| outputs = [ | |
| gr.Textbox(label="Output file"), | |
| gr.Textbox(label="Emotion frequencies"), | |
| gr.Textbox(label="Emotion distribution over time"), | |
| gr.Textbox(label="Peaks"), | |
| gr.Textbox(label="Topics") | |
| ]) | |
| iface = gr.TabbedInterface([iface1, iface2], ["Sentence", "Dataset"]) | |
| iface.launch() |