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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")
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