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| """ | |
| Gradio Twitter analizer application. | |
| This module provides a gradio-based web application | |
| for the Twitter analyzer project. | |
| """ | |
| import gradio as gr | |
| from tweet_scraper import retrieve_tweet_text | |
| from backend import predict_positivity | |
| def process_tweet(url: str) -> str: | |
| """ | |
| Get a tweet's positivity. | |
| Args: | |
| url (str): Tweet's URL. | |
| Returns: | |
| str: Predicted positivity | |
| """ | |
| text = retrieve_tweet_text(url) | |
| outcome = predict_positivity(text) | |
| return outcome | |
| title = "Twitter Positivity Analyzer" | |
| description = """ | |
| <h2> Description </h2> | |
| Twitter is a social media network on which users post and interact with messages known as "tweets". It allows an user to post, like, and retweet tweets. | |
| Twitter is also known by the excessive negativity or criticism by a great part of its users. Considering that, this application intends to classify a tweet according to its positivity. The positivity is measured in five categories: | |
| - Extremely negative | |
| - Negative | |
| - Neutral | |
| - Positive | |
| - Extremely positive | |
| The application is based on a BERT model fine tuned on the [Coronavirus tweets NLP dataset](https://www.kaggle.com/datasets/datatattle/covid-19-nlp-text-classification). | |
| """ | |
| article = "Check out this [github repository](https://github.com/hectorLop/Twitter_Positivity_Analyzer) \ | |
| with a lot more details about this method and implementation." | |
| app = gr.Interface( | |
| fn=process_tweet, | |
| inputs=gr.inputs.Textbox(lines=2, placeholder="Tweet url..."), | |
| outputs="text", | |
| title=title, | |
| description=description, | |
| article=article, | |
| ) | |
| if __name__ == "__main__": | |
| app, local_url, share_url = app.launch() | |