| import gradio | |
| import nltk | |
| import pandas as pd | |
| from transformers import pipeline | |
| summarizer = pipeline('summarization', model='t5-base') | |
| # classifier_model_name = 'bhadresh-savani/distilbert-base-uncased-emotion' | |
| # classifier_emotions = ['anger', 'disgust', 'fear', 'joy', 'sadness', 'surprise'] | |
| classifier_model_name = 'ProsusAI/finbert' | |
| classifier_emotions = ['positive', 'neutral', 'negative'] | |
| classifier = pipeline('text-classification', model=classifier_model_name) | |
| def my_inference_function(name): | |
| return "Hello " + name + "!" | |
| gr_interface = gradio.Interface( | |
| fn = my_inference_function, | |
| inputs = "text", | |
| outputs = "text" | |
| ) | |
| gr_interface.launch() | |