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| import torch # type: ignore | |
| from transformers import pipeline # type: ignore | |
| import gradio as gr # type: ignore | |
| from dotenv import load_dotenv # type: ignore | |
| import os | |
| device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') | |
| load_dotenv() | |
| task = 'text-classification' | |
| model = 'SamLowe/roberta-base-go_emotions' | |
| gitHubLink = 'https://github.com/pulkit-singhall' | |
| linkedInLink = 'https://www.linkedin.com/in/pulkit-singhal-a8113822a/' | |
| pipe = pipeline(task, model, device=device, framework='pt') | |
| def classify_text(text, top_results): | |
| result = pipe(text, top_k = top_results) # array of dict | |
| output = result[0]['label'] | |
| output = output[:1].upper() + output[1:] | |
| for i in range(1,len(result)): | |
| label = result[i]['label'] | |
| label = label[:1].upper() + label[1:] | |
| output = '\n'.join([output, label]) | |
| return output | |
| with gr.Blocks() as app: | |
| gr.Markdown(value = 'Get emotions from any textual sentence...', height = 28) | |
| with gr.Row(): | |
| with gr.Column(): | |
| sentence = gr.Textbox(label="English Sentence Here") | |
| slider = gr.Slider( | |
| label = 'Top Results you want', value = 1, | |
| minimum = 1, maximum = 10, step = 1) | |
| with gr.Row(): | |
| clear_btn = gr.ClearButton(components = [sentence], variant = 'secondary') | |
| classify_btn = gr.Button(value="Submit", variant = 'primary') | |
| with gr.Column(): | |
| result = gr.Textbox(label="Required Emotions") | |
| classify_btn.click(classify_text, inputs=[sentence, slider], outputs=[result]) | |
| examples = gr.Examples(examples=["That movie was amazing but I did not like the actors.", | |
| "Helen is a good swimmer.", | |
| 'What do you think about Elon Musk and his accomplishments?', | |
| 'Today was a horrible day'], | |
| inputs=[sentence]) | |
| gr.Markdown(value = f'Check out my [GitHub]({gitHubLink}) and [LinkedIn]({linkedInLink})', | |
| height = 28) | |
| app.launch(share = True) |