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# -*- coding: utf-8 -*- | |
# """gradio_app.ipynb | |
# Automatically generated by Colaboratory. | |
# Original file is located at | |
# https://colab.research.google.com/drive/1u8oKw0KTptVWpY-cKFL87N2IDDrM4lTc | |
# """ | |
## | |
import gradio as gr | |
import pandas as pd | |
import numpy as np | |
import pickle | |
from scipy.special import softmax | |
from transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoConfig | |
# Requirements | |
model_path = "QuophyDzifa/Sentiment-Analysis-Model" | |
tokenizer = AutoTokenizer.from_pretrained(model_path) | |
config = AutoConfig.from_pretrained(model_path) | |
model = AutoModelForSequenceClassification.from_pretrained(model_path) | |
# Preprocess text (username and link placeholders) | |
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) | |
def sent_analysis(text): | |
text = preprocess(text) | |
# PyTorch-based models | |
encoded_input = tokenizer(text, return_tensors='pt') | |
output = model(**encoded_input) | |
scores_ = output[0][0].detach().numpy() | |
scores_ = softmax(scores_) | |
# Format output dict of scores | |
labels = {0: 'NEGATIVE', 1: 'NEUTRAL', 2: 'POSITIVE'} | |
scores = {labels[i]: float(s) for i, s in enumerate(scores_)} | |
return scores | |
demo = gr.Interface( | |
fn=sent_analysis, | |
inputs=gr.Textbox(placeholder="Share your thoughts on COVID vaccines..."), | |
outputs="label", | |
interpretation="default", | |
examples=[ | |
["I feel confident about covid vaccines"], | |
["I do not like the covid vaccine"], | |
["I like the covid vaccines"], | |
["The covid vaccines are effective"] | |
], | |
title="COVID Vaccine Sentiment Analysis", | |
description="An AI model that predicts sentiment about COVID vaccines, providing labels and probabilities for 'NEGATIVE', 'NEUTRAL', and 'POSITIVE' sentiments.", | |
theme="default", | |
live=True | |
) | |
if __name__ == "__main__": | |
demo.launch("0.0.0.0:7860") | |