| # #Import the required Libraries | |
| # import gradio as gr | |
| # import pickle | |
| # import pandas as pd | |
| # import numpy as np | |
| # import transformers | |
| # # Load 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() | |
| # import gradio as gr | |
| # from transformers import pipeline | |
| # import transformers | |
| # Model = transformers.AutoModel.from_pretrained('AmpomahChief/sentiment_analysis_on_covid_tweets') | |
| # pipeline = pipeline(task="image-classification", model=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() |