| #Import the required Libraries | |
| import gradio as gr | |
| import pickle | |
| import pandas as pd | |
| import numpy as np | |
| import transformers | |
| # Load a BERT model from the Hugging Face model hub | |
| model = transformers.AutoModel.from_pretrained('AmpomahChief/sentiment_analysis_on_covid_tweets') | |
| # Define a function that takes in input and passes it through the model | |
| def predict(inputs): | |
| input_ids = transformers.BertTokenizer.from_pretrained('AmpomahChief/sentiment_analysis_on_covid_tweets').encode(inputs, return_tensors='pt') | |
| output = model(input_ids)[0] | |
| return output | |
| # Create a Gradio interface for the model | |
| interface = gr.Interface(fn=predict, inputs=gr.Textbox(prompt="Input text:"), outputs=gr.Textbox(prompt="Model output:")) | |
| # Launch the interface | |
| interface.launch() | |
| # import gradio as gr | |
| # # Creating a gradio app using the inferene API | |
| # App = gr.Interface.load("huggingface/AmpomahChief/sentiment_analysis_on_covid_tweets", | |
| # title="COVID 19 tweets sentiment analysis", description ="This is a sentiment analysis on COVID 19 tweets using pretrained model on hugging face", | |
| # allow_flagging=False, examples=[["Input your text here"]] | |
| # ) | |
| # App.launch() |