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import gradio as gr | |
import numpy as np | |
import torch | |
from transformers import AutoModelForSequenceClassification | |
from transformers import AutoTokenizer, AutoConfig | |
from scipy.special import softmax | |
# Setup | |
model_path = f"pakornor/roberta-base" | |
tokenizer = AutoTokenizer.from_pretrained('roberta-base') | |
config = AutoConfig.from_pretrained(model_path) | |
model = AutoModelForSequenceClassification.from_pretrained(model_path) | |
# Functions | |
# Preprocess text | |
def preprocess(text): | |
new_text = [] | |
for t in text.split(" "): | |
t = '@user' if t.startswith('@') and len(t) > 1 else t | |
t = 'http' if t.startswith('http') else t | |
new_text.append(t) | |
return " ".join(new_text) | |
# Input preprocessing | |
def sentiment_analysis(text): | |
text = preprocess(text) | |
encoded_input = tokenizer(text, return_tensors='pt') | |
output = model(**encoded_input) | |
scores_ = output[0][0].detach().numpy() | |
scores_ = softmax(scores_) | |
# Format output dictionary of scores | |
labels = ['Negative', 'Neutral', 'Positive'] | |
scores = {l:float(s) for (l,s) in zip(labels, scores_)} | |
return scores | |
# Gradio App | |
app = gr.Interface( | |
fn=sentiment_analysis, | |
inputs=gr.Textbox("Input tweet here:"), | |
outputs="label", | |
title="Sentiment Analysis of Tweets on Covid-19 Vaccines", | |
description="With this App, you can type Tweets related to the Covid Vaccine and the app will rate the sentiment of the tweet..!", | |
examples=[["Be careful of covid vaccination"], | |
["The vaccine can reduce your immunity to diseases"], | |
["I cant wait for the Covid Vaccine!"]] | |
) | |
if __name__ == "__main__": | |
app.launch(server_name='0.0.0.0', server_port=7860) |