| from __future__ import absolute_import, division, print_function, unicode_literals | |
| import os | |
| import gc | |
| import gradio as gr | |
| import pandas as pd | |
| from transformers import pipeline | |
| def huggingface_result_page(paragraph): | |
| if article.strip(): | |
| model_base = pipeline('sentiment-analysis') | |
| sen_list = paragraph | |
| sen_list = sen_list.split('\n') | |
| sen_list_temp = sen_list[0:] | |
| results = [] | |
| temp_result_dict = [] | |
| for sen in sen_list_temp: | |
| sen = sen.strip() | |
| if sen: | |
| cur_result = model_base(sen)[0] | |
| temp_result_dict.append(sen) | |
| results.append(cur_result['label']) | |
| result = { | |
| 'Input': sen_list, 'Sentiment': results | |
| } | |
| print("LENGTH of results ====> ",str(len(results))) | |
| print("LENGTH of sen_list ====> ",str(len(temp_result_dict))) | |
| return pd.DataFrame(result) | |
| else: | |
| raise gr.Error("Please enter text in inputbox!!!!") | |
| inputs = gr.Textbox(lines=10, label="Paragraph") | |
| outputs = gr.Dataframe(row_count = (3, "dynamic"), col_count=(2, "fixed"), label="Here is the Result", headers=["Input","Sentiment"],wrap=True) | |
| demo = gr.Interface( | |
| huggingface_result_page, | |
| inputs, | |
| outputs, | |
| title="Huggingface Sentiment Analysis", | |
| css=".gradio-container {background-color: lightgray}", | |
| article = """Provide us your feedback on this demo and feel free to contact us at <a href="mailto:[email protected]" target="_blank">[email protected]</a> | |
| if you want to have your own sentiment analysis system. We will be happy to serve you for your sentiment analysis requirement. | |
| And don't forget to check out more interesting <a href="https://www.pragnakalp.com/services/natural-language-processing-services/" target="_blank">NLP services</a> | |
| we are offering. | |
| <p style='text-align: center;'>Developed by :<a href="https://www.pragnakalp.com" target="_blank"> Pragnakalp Techlabs</a></p>""" | |
| ) | |
| demo.launch() |